# Welcome to DRAGEN Array DRAGEN (Dynamic Read Analysis for GENomics) Array secondary analysis is a powerful bioinformatics software for Illumina Infinium array-based assays. DRAGEN Array uses cutting-edge data analysis tools to provide accurate, comprehensive, and highly efficient secondary analysis to maximize genomic insights and meet your research needs across multiple applications. DRAGEN Array is offered as a local package with command-line interface (no specialized server or hardware required) and as a cloud-based package with an intuitive graphical user interface, as summarized in the table below.
DescriptionKey featuresLocal analysisCloud analysis
GenotypingProvides genotyping results for any human Infinium genotyping array.
  • Greater than 99.5% genotyping accuracy
  • Genotyping VCF in as little as 35 seconds per sample
PGx – CNV callingProvides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Greater than 95% PGx CNV accuracy
PGx – star allele annotationProvides PGx star allele and variant coverage across 2400+ targets for over 50 genes, plus PGx CNV and genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Assess hard to discern PGx genes, including the elusive CYP2D6 with greater than 97% call rate
  • Obtain all PGx analysis results in ~1 minute per sample
Cytogenetics analysisProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays.
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
Cytogenetics analysis + Emedgene interpretationProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays with added visualization and case management in Emedgene
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
Methylation QCProvides high-throughput, quantitative methylation quality control for Infinium methylation arrays.
  • 21 algorithm-based quantitative control metrics with adjustable thresholds
  • Data summary plots
  • Proportion of CG probes passing with user defined p-value threshold
QC reportGenerates a self-contained, interactive HTML dashboard and a per-sample QC table to support quality control review of genotyping and DNA methylation array datasets.
  • Interactive HTML report with Control Dashboard, Automated QC, Heatmaps, and Trend Analysis
  • Local analysis can combine multiple datasets for cross-run Trend Analysis; cloud analysis currently summarizes one dataset per report
  • Sample QC table for downstream review with pass/fail status and control-based warnings/flags
AvailableAvailable
This product documentation describes the installation and setup, analysis execution, and result outputs. For the latest updates and release details, see the [DRAGEN Array Release Notes](/support-and-updates/release-notes). See [Introducing DRAGEN™ Array 1.0 for Infinium™ Array-Based Pharmacogenomics Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) for additional details on DRAGEN Array genotyping, PGx CNV calling and PGx star allele annotation. # DRAGEN Array Applications The following Types of Analysis are currently supported by DRAGEN Array: * DRAGEN Array – Genotyping and QC * DRAGEN Array – PGx – CNV calling * DRAGEN Array – PGx – Star allele annotation * DRAGEN Array – Cytogenetics analysis * DRAGEN Array - Cytogenetics analysis + Emedgene interpretation * DRAGEN Array – Methylation QC ### Product & Analysis Compatibility These products/beadchips have been verified to be compatible with the analyses below. To make the compatibility matrix easier to review, the tables are split by product category and ordered by product group, product, tested manifest, and analysis type. **Notes:** * The **Manifest** column lists the tested BPM/CSV product file revision. * Some historical manifest rows from the previous compatibility table were intentionally folded into the newer tested manifest revisions listed here. See the [release notes](/support-and-updates/release-notes) when version-specific legacy manifest support details are needed. * The cloud and local version columns indicate the version where support was introduced and later retained, unless noted otherwise. * Historical PGx manifest transitions for older releases are described in the [release notes](/support-and-updates/release-notes). #### Genotyping, PGx, and Cytogenetics Products | Product Group | Product | Manifest | DRAGEN Array Analysis Type | DRAGEN Array Cloud Version(s) | DRAGEN Array Local Version(s) | Genome(s) | Notes | | ------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | ----------------------------- | ----------------------------- | -------------- | -------------------------------- | | Genotyping | ASA | [ASA-24v1-0\_E2](https://support.illumina.com/downloads/infinium-asian-screening-array-v1-0-product-files.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | BovineSNP50 v3 | [BovineSNP50\_v3\_A1](https://support.illumina.com/array/array_kits/bovinesnp50-beadchip-kit.html) | Genotyping | v1.1, v1.4 | v1.0+ | UMD3 | Non-human bovine product | | Genotyping | CGA | CGA-24v1-0\_A1 | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | GCRA | [GCRA-24v1-0\_20068388\_B2](https://www.illumina.com/products/by-type/clinical-research-products/infinium-global-clinical-research.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | GDA | [GDA-8v1-0\_D2](https://support.illumina.com/array/array_kits/infinium-global-diversity-array.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | GSA v3 | [GSA-24v3-0\_A2](https://www.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | GSAv4 | [GSA-48v4-0\_20085471\_D2](https://www.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | Infinium EX product | | Genotyping | JSA | JSA-24v1-0\_B2 | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37, GRCh38 | | | Genotyping | PRSbooster | [PRSbooster\_20083382\_A](https://support.illumina.com/array/array_software/gda-prs.html) | Genotyping | v1.1, v1.4 | v1.0+ | GRCh37 | | | PGx | GCRA-ePGx | [GCRA-PGx-24v1-0\_20084467\_C2](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) |

Genotyping;
PGx – CNV calling;
PGx – Star allele annotation

| v1.1+ | v1.1+ | GRCh38 | Introduced with EX PGx support | | PGx | GDA-ePGx | [GDA\_PGx-8v1-0\_20042614\_G4](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) |

Genotyping;
PGx – CNV calling; PGx – Star allele annotation

| v1.1+ | v1.1+ | GRCh38 | Current tested G-series manifest | | PGx | GSAv4-ePGx | [GSA-PGx-48v4-0\_20079540\_E2](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) |

Genotyping;
PGx – CNV calling;
PGx – Star allele annotation

| v1.1+ | v1.1+ | GRCh38 | Introduced with EX PGx support | | Cytogenetics | GDA Cyto | [GDACyto-8v1-0\_20047166\_E2](https://support.illumina.com/array/array_kits/infinium-global-diversity-array-cyto-8.html) | Cytogenetics analysis | v1.2+ | v1.2+ | GRCh37, GRCh38 | | | Cytogenetics | GDA Cyto | [GDACyto-8v1-0\_20047166\_E2](https://support.illumina.com/array/array_kits/infinium-global-diversity-array-cyto-8.html) | Cytogenetics analysis + Emedgene interpretation | v1.2+ | N/A | GRCh37, GRCh38 | Cloud only analysis type | | Cytogenetics | GSA Cyto | [GSACyto-24v1\_20044998\_C2](https://support.illumina.com/array/array_kits/infinium-global-screening-array-cyto-24.html) | Cytogenetics analysis | v1.2+ | v1.2+ | GRCh37, GRCh38 | | | Cytogenetics | GSA Cyto | [GSACyto-24v1\_20044998\_C2](https://support.illumina.com/array/array_kits/infinium-global-screening-array-cyto-24.html) | Cytogenetics analysis + Emedgene interpretation | v1.2+ | N/A | GRCh37, GRCh38 | Cloud only analysis type | | Cytogenetics | CytoSNP-850K iScan | [CytoSNP-850Kv1-4\_iScan\_B2](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | Cytogenetics analysis | v1.2+ | v1.2+ | GRCh37, GRCh38 | | | Cytogenetics | CytoSNP-850K iScan | [CytoSNP-850Kv1-4\_iScan\_B2](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | Cytogenetics analysis + Emedgene interpretation | v1.2+ | N/A | GRCh37, GRCh38 | Cloud only analysis type | | Cytogenetics | CytoSNP-850K NS550 | [CytoSNP-850Kv1-4\_NS550\_B2](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | Cytogenetics analysis | v1.3+ | v1.3+ | GRCh37, GRCh38 | | | Cytogenetics | CytoSNP-850K NS550 | [CytoSNP-850Kv1-4\_NS550\_B2](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | Cytogenetics analysis + Emedgene interpretation | v1.3+ | N/A | GRCh37, GRCh38 | Cloud only analysis type | #### Methylation Products | Product | Tested Manifest | DRAGEN Array Analysis Type | DRAGEN Array Cloud Version(s) | DRAGEN Array Local Version(s) | Genome(s) | Notes | | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- | ----------------------------- | ----------------------------- | --------- | --------------------------- | | EPIC v1 | [infinium-methylationepic-v-1-0-b5-manifest-file.csv](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | Methylation – QC | v1.0 | N/A | GRCh38 | | | EPIC v2 | [EPIC-8v2-0\_A2.csv](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | Methylation – QC | v1.0 | N/A | GRCh38 | | | EPIC-Xtra | EPIC-Xtra\_GS\_20144570\_A1.csv | Methylation – QC | v1.0 | N/A | GRCh38 | Custom methylation product. | | MSA | [MSA-48v1-0\_20102838\_A1.csv](https://support.illumina.com/array/array_kits/infinium-methylation-screening-array.html) | Methylation – QC | v1.0 | N/A | GRCh38 | | ## DRAGEN Array – Genotyping and QC | Item | Description | | ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides genotyping results and sample QC report for Infinium genotyping arrays. Non-human species are also supported for diploid organisms when a reference genome is provided. | | Variant types detected |

SNV

Indel

| | Sample minimum | 1 sample | | Arrays supported | Any Infinium genotyping array, including human (including custom and semi-custom) and non-human diploid species, to create a SNV VCF output. Illumina provides [Genome FASTA Files](/product-guides/input-files#section-genome-fasta-files) required to map to the reference genome for human, genome build 37 and 38; for non-human diploid species, a user-provided reference genome is required. DRAGEN Array Cloud offers additional output formats including Locus Summary and Final Report which are applicable for Infinium arrays for human and non-human diploid species. | | Related Local Commands |

genotype call

genotype gtc-to-vcf

qc call
qc report

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Genotyping and QC** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

• QC Report Config File \[pre-setup on cloud]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on cloud and local]

• TBI Index File \[optional on cloud and local]

Per analysis batch:

• Genotype Summary Files

• Final Report \[cloud only]

• Locus Summary \[cloud only]

• QC metrics files

• QC Report

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: BioInsight Credits (BICs) to analyze and store data as needed.

| ## DRAGEN Array – PGx – CNV calling | Item | Description | | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

pgx copy-number call

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – CNV calling** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• QC metrics files

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: BICs to analyze and store data as needed.

| ## DRAGEN Array – PGx – Star Allele Annotation | Item | Description | | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides PGx annotation on over 50 genes, plus PGx CNV and genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

Star allele diplotype

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf

pgx copy-number call

pgx star-allele call

pgx star-allele annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – Star Allele Annotation** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• PGx Database File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

• Star Allele JSON File

Per analysis batch:

• Star Allele CSV Diplotype Summary and Supporting Files

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• QC metrics files

• Warning/Error Messages

| | Cost |

Local: Per sample analysis.

Cloud: Per sample analysis. BICs to store data as needed.

Visit the Illumina Product Page to learn more.

| ## DRAGEN Array – Cytogenetics analysis | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatibility) | | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

genotype gtc-to-bedgraph

cyto call

cyto annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Cytogenetics analysis** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]
• QC Report Config File \[pre-setup on cloud]

• Cytogenetics Model File \[pre-setup on cloud]

• Cytogenetics Database File \[only necessary for local]

• Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional on cloud]

• SNV VCF File \[optional on local and cloud]

• TBI Index File \[optional on local and cloud for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• QC metrics files

• QC Report

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: BICs to analyze and store data as needed.

| ## DRAGEN Array - Cytogenetics analysis + Emedgene interpretation | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling. This analysis type integrates with Emedgene via [Automatic Case Creation from BioInsight Platform Core (formerly ICA)](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) on cloud only. | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatibility) | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup]

• Cluster File \[may be pre-setup]
• QC Report Config File \[pre-setup on cloud]

• Cytogenetics Model File \[may be pre-setup]

• Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional]

• SNV VCF File \[optional]

• TBI Index File \[optional for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files

Per analysis batch:

• Genotype Summary Files

• Warning/Error Messages

| | Cost | Cloud: [BICs](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. As well as additional sample-based costs if uploaded into the [Emedgene](https://help.connected.illumina.com/emedgene) interface. | ## DRAGEN Array – Methylation QC | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides methylation QC for Infinium methylation arrays. | | Variant types detected | N/A | | Sample minimum | 1 sample | | Arrays supported | Recommended thresholds and all built-in control probes are available for Methylation Screening Array (MSA) and MethylationEPIC (v1 & v2) originating from iScan. In non-human and custom arrays, availability of built-in QC probes may vary, and failure thresholds must be defined by the user. | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Methylation – QC** from the dropdown. Adjust customizable thresholds as desired. Further detail can be found in Additional information for [DRAGEN Array Methylation QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc#section-dragen-array-methylation-qc). A maximum of 1152 samples are supported. | | Inputs |

• IDAT(s) \[from iScan instrument]

• Manifest Files \[may be pre-setup on cloud]

• Sample Sheet \[optional on cloud]

| | Outputs |

Per sample:

• Methylation Control Probe Output File

• Methylation CG Output File

Per analysis batch:

• Methylation Sample QC Summary Files

• Methylation Sample QC Summary Plots

• Methylation Principal Component Summary

• Methylation Manifest Files

• Methylation Logs and Error Files

| | Cost | Cloud: [BICs](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. | # DRAGEN Array Cloud Analysis DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. This documentation is organized into two main sections: ## [Analysis Type Details](/product-guides/dragen-array-cloud-analysis/overview) Learn about the specific configuration options, quality control recommendations, and requirements for each type of analysis supported by DRAGEN Array Cloud. Each analysis type has unique settings and best practices that help ensure optimal results. [View Analysis Type Details →](/product-guides/dragen-array-cloud-analysis/overview) ## [BaseSpace Operations](/product-guides/dragen-array-cloud-analysis/overview-1) Step-by-step guidance for using BaseSpace Sequence Hub to run DRAGEN Array analyses, including setup, launching analyses, viewing outputs, managing data, and troubleshooting. [Get Started with BaseSpace →](/product-guides/dragen-array-cloud-analysis/overview-1) # Analysis Type Details ## DRAGEN Array Cloud Analysis Overview DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. DRAGEN Array Cloud supports multiple types of analysis, each with specific configuration options and requirements. Select an analysis type below to learn more about its specific configuration options, thresholds, and recommendations: * [DRAGEN Array Genotyping and QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-genotyping) * [DRAGEN Array PGx CNV Calling](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-pgx-cnv-calling) * [DRAGEN Array PGx Star Allele Annotation](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-pgx-star-allele) * [DRAGEN Array Cytogenetics Analysis](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-cytogenetics-analysis) * [DRAGEN Array Methylation QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc) For information about supported products, versions, and genome builds for each analysis type, see [DRAGEN Array Applications](/overview/our-features). {% hint style="warning" %} The following v1.0.0 cloud analysis pipeline versions were deprecated in Q1 2026. This applies only to these specific pipeline versions, not to the analysis types in general. * DRAGEN Array - Methylation QC - 1-0-0 * DRAGEN Array - PGx - Star Allele Annotation - 1-0-0 * DRAGEN Array - Genotyping - 1-0-0 * DRAGEN Array - PGx - CNV Calling - 1-0-0 {% endhint %} # DRAGEN Array Cytogenetics Analysis ### DRAGEN Array - Cytogenetics Analysis #### Cytogenetics Threshold Adjustment When using **DRAGEN Array – Cytogenetics analysis** or **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** cloud analysis types, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on results objectives. Adjusting thresholds will impact the number of events called and thus, the output in the VCF and JSON files. The recommended thresholds/settings are pre-set within the software for any new configurations: | Threshold | Default Value | Min Value | Max Value | | ------------------------- | ------------- | --------- | --------- | | GTC Output | False | N/A | N/A | | SNV VCF Output | False | N/A | N/A | | DUP minimum size (kb) | 50 | 0 | 250000 | | DUP minimum probes | 10 | 0 | 250000 | | DEL minimum size (kb) | 25 | 0 | 250000 | | DEL minimum probes | 10 | 0 | 250000 | | LOH minimum size (kb) | 3000 | 0 | 250000 | | LOH minimum probes | 500 | 0 | 250000 | | GAINLOH minimum size (kb) | 500 | 0 | 250000 | | GAINLOH minimum probes | 300 | 0 | 250000 | | mDUP minimum size (kb) | 50 | 0 | 250000 | | mDUP minimum probes | 10 | 0 | 250000 | | mDEL minimum size (kb) | 25 | 0 | 250000 | | mDEL minimum probes | 10 | 0 | 250000 | | mLOH minimum size (kb) | 3000 | 0 | 250000 | | mLOH minimum probes | 500 | 0 | 250000 | | CNV Smoothing window size | 5 | 0 | 1000 | ### DRAGEN Array - Cytogenetics analysis + Emedgene interpretation This analysis type integrates with [Emedgene](https://help.connected.illumina.com/emedgene) to display results in a user-friendly interface. **Note:** Support for each DRAGEN Array version begins with a specific Emedgene release and continues with later versions. For more details see the [compatibility table](https://help.emg.illumina.com/emedgene-analyze-manual/supported-vcfs-variant-callers#compatibility-with-dragen-and-dragen-array-variant-callers) on the Emedgene help site. In that table, "EMG version" refers to the combination of the **Platform minor version** (Organization Settings > Environment > Platform minor version) and the **case pipeline version** (Organization Settings > Workbench & Pipeline > Pipeline versions > 4. case). Both must be updated. The **Secondary Analysis Pipeline** setting (Organization Settings > Workbench & Pipeline > Pipeline versions > 1. Sample) is not related to DRAGEN Array integration with Emedgene. #### Prerequisites * You'll need an additional Emedgene subscription to be either "Array", "Professional", or "Enterprise" tier. You can also follow the [Illumina Software Registration Guide](https://help.connected.illumina.com/account-management/rg-registration) to obtain that subscription. * To ensure proper integration with Emedgene (EMG), [Platform Core notifications](https://help.ica.illumina.com/project/p-notifications) must be enabled for the specific Platform Core BSSH-managed project. EMG relies on these notifications to detect when an analysis has successfully completed. To configure SNS ([Amazon Web Services Simple Notification Service](https://aws.amazon.com/sns/)) events in your managed Platform Core BSSH-managed project, follow these steps: * In the [Platform Core portal](https://ica.illumina.com/ica/), in the Platform Core BSSH-managed project (e.g. "BSSH Your Workgroup Name") navigate to the **Notifications** section via the left-hand menu. ![Platform Core Notifications Menu](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-cee9edf57b49986ed37c2820f23ceb4a27cbef1c%2Fica_notif_1.png?alt=media) * Click **+ Create**, then select **Platform Core Event**. ![Platform Core Event](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-145a3f2740d6095d80c1623df8fe5b124656315a%2Fica_notif_2.png?alt=media) * Fill in the required fields as follows: * **Event:** Analysis Success * **Type:** SNS * **Address:** Provide the correct address based on your region (contact if unsure). * **Payload Version:** v4 * **AWS Region:** This will be auto-populated based on the provided address. * (Recommended) Click **Send Test Message** to verify the configuration. * Click **Save** to complete the setup. ![Create Subscription](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-21d6ac4a976ca1394729fb7cb6ff0e91582669cb%2Fica_notif_3.png?alt=media) For more details on the prerequisites for this analysis, see the [Automatic Case Creation from Platform Core](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) section in the Emedgene User Guide.\ For more details on limitations for this analysis see the [release notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes/dragen-array-v1.3.0-emg-release-notes). #### Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/overview/our-features#product_compatibility) table. # DRAGEN Array Genotyping and QC ## Custom Configuration Options When using **DRAGEN Array – Genotyping and QC** cloud analysis type, you have the following configuration options available via the "Add Custom Configuration" option in Configuration Settings: * **Output File Selection**: Flexibility to turn off/on specific output files * VCF output can be toggled on or off * Final Report output can be toggled on or off * Locus Summary output can be toggled on or off * **GenCall Score Cutoff**: Adjustable threshold for genotype calling quality * Override default GenCall score cutoff * **Use Infinium I probe no-calls**: Constrain possible genotypes using Infinium I probe no-call information. * **QC Report Output Format**: Excel (xlsx) - default or CSV format. For more information, see [DRAGEN Array QC Report](/product-guides/dragen-array-local-analysis/qc-report). ## Recommendations * **For non-human species**: It is recommended to turn off VCF output, as VCF generation requires genome mapping which is only provided for human genomes (GRCh37 and GRCh38). * **For large sample numbers (\~400+ samples)**: It is recommended to turn off Final Report output, as Final Report files can reach 50+ GB and become difficult to download. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/overview/our-features#product_compatibility) table. # DRAGEN Array Methylation QC ## Methylation QC Threshold Adjustment When using **DRAGEN Array – Methylation – QC** cloud analysis type, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on study objectives. Adjusting thresholds will impact the pass or fail status of samples in the output files. Illumina recommends thresholds for MethylationEPIC v1 & v2 and Methylation Screening Array (MSA). Users may use these thresholds as a starting point when defining thresholds for their custom or semi-custom BeadChip or other Infinium Methylation arrays. Further tuning may be required based on BeadChip used, laboratory conditions, iScan settings, bisulfite conversion methods, FPPE sample type, etc. A dataset deemed acceptable to the user based on proportion probes passing can be used for these additional threshold adjustments. To customize thresholds, use the toggle to allow additional thresholds to be displayed and adjust as desired by typing in a numeric value or using the arrows to adjust up or down. Further detail of these thresholds including calculation method can be found in the [Methylation Sample QC Summary Files](/product-guides/output-files#methyl_qc_report) section. The recommended thresholds are pre-set within the software for MethylationEPIC and Methylation Screening Array with the following values: | Threshold | Methylation Screening Array | MethylationEPIC | | ----------------------------------- | --------------------------- | --------------- | | Restoration[^1] | 0 | 0 | | StainingGreen | 5 | 5 | | StainingRed | 5 | 5 | | ExtensionGreen | 5 | 5 | | ExtensionRed | 5 | 5 | | HybridizationHighMedium | 1 | 1 | | HybridizationMediumLow | 1 | 1 | | TargetRemoval1 | 1 | 1 | | TargetRemoval2 | 1 | 1 | | BisulfiteConversion1Green | 1 | 1 | | BisulfiteConversion1BackgroundGreen | 0.5 | 1 | | BisulfiteConversion1Red | 1 | 1 | | BisulfiteConversion1BackgroundRed | 0.5 | 1 | | BisulfiteConversion2 | 0.5 | 1 | | BisulfiteConversion2Background | 0.5 | 1 | | Specificity1Green | 1 | 1 | | Specificity1Red | 1 | 1 | | Specificity2 | 1 | 1 | | Specificity2Background | 1 | 1 | | NonpolymorphicGreen | 2.5 | 5 | | NonpolymorphicRed | 3 | 5 | | BgCorrectionOffset | 3000 | 3000 | | PvalThreshold | 0.05 | 0.05 | The first 21 rows in the tables correspond to the 21 control metrics used in the methylation sample QC. See section [Methylation Sample QC Summary Files](/product-guides/output-files#methyl_qc_report) for details. ## DRAGEN Array Methylation QC and GenomeStudio Methylation Module Differences DRAGEN Array Methylation QC software provides automated methylation sample QC using assay control probes on the Infinium Methylation Arrays. Unlike the manual visual QC in GenomeStudio, DRAGEN Array ultilizes 21 numerical metrics defined based on the control probes and uses standard thresholds to determine pass/fail status of a sample. Unlike GenomeStuio, probe detection rate (proportion of probes passing at a given p-value threshold) is not utilized to determine sample pass/fail status in DRAGEN Array. For more information, see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note. DRAGEN Array Methylation QC performs background normalization, dye bias correction, and detection p-value calculation differently in comparison to the GenomeStudio Methylation module, leading to differences in probe detection p-values and detection rates. For the GenomeStudio Methylation Module, non-cancer samples at standard DNA input typically have detection rate > 96%. The detection rates from DRAGEN Array Methylation QC are typically lower compared to GenomeStudio, because the detection p-value from DRAGEN Array is more stringent than that from the GenomeStudio Methylation Module. The table below shows example detection rates from the DRAGEN Array Methylation QC software from MSA (Methylation Screening Array) datasets. | Dataset | Min detection rate | Mean detection rate | Sample Count | | ------- | ------------------ | ------------------- | ------------ | | A | 86% | 93% | 220 | | B | 61% | 83% | 951 | | C | 63% | 85% | 34 | | D | 77% | 85% | 22 | Note that only samples passing QC are included and all samples are at or above 50ng DNA input. Detection p-value threshold 0.05. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/overview/our-features#product_compatibility) table. [^1]: If FFPE restore kit is used, Restoration threshold should be increased from 0 to 1. # DRAGEN Array PGx CNV Calling ## Quality Control Recommendations For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. The metric is provided in the genotyping sample summary file (gt\_sample\_summary.csv). For more details, see the explanation for the [local analysis](/product-guides/dragen-array-local-analysis#section-run-dragen-array-local). ## Semi-Custom PGx Products ### Running Semi-Custom PGx Analysis on Cloud Detailed notes on running this analysis for local can be found [here](/product-guides/dragen-array-local-analysis#section-pharmacogenomic-analysis-for-semi-custom-arrays). But for cloud, a workaround is necessary because semi-custom product samples are filtered from the BeadChip table in BaseSpace. Follow these steps: 1. Select an existing commercial product configuration (e.g., `GDA_PGx-8v1-0_G4 - GRCh38`) 2. Kick off an analysis using the **Import Sample Sheet** option for the semi-custom product samples ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/overview/our-features#product_compatibility) table. # DRAGEN Array PGx Star Allele Annotation ## Quality Control Recommendations For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). For more details, see the explanation for the [local analysis](/product-guides/dragen-array-local-analysis#section-run-dragen-array-local). ## Custom Configuration Options When using **DRAGEN Array – PGx – Star allele annotation** cloud analysis type, you have the following configuration option: * **Metabolizer Status Database**: Option to change the default metabolizer status database * Default: [CPIC](https://cpicpgx.org/) (Clinical Pharmacogenetics Implementation Consortium) * Alternative: [DPWG](https://www.pharmgkb.org/page/dpwg) (Dutch Pharmacogenetics Working Group) ## Semi-Custom PGx Products ### Running Semi-Custom PGx Analysis on Cloud Detailed notes on running this analysis for local can be found [here](/product-guides/dragen-array-local-analysis#section-pharmacogenomic-analysis-for-semi-custom-arrays). But for cloud, a workaround is necessary because semi-custom product samples are filtered from the BeadChip table in BaseSpace. Follow these steps: 1. Select an existing commercial product configuration (e.g., `GDA_PGx-8v1-0_G4 - GRCh38`) 2. Kick off an analysis using the **Import Sample Sheet** option for the semi-custom product samples Alternatively, to upload IDATs directly for a semi-custom product (rather than using Import Sample Sheet with a commercial configuration): 1. If a custom configuration does not already exist, create one using the **Add Custom Configuration** option and upload the [product files](/product-guides/input-files) for the semi-custom product. 2. Select the custom configuration for the semi-custom product. 3. Proceed to sample selection, choose **Import IDAT Files**, and upload the IDATs. For step-by-step details on these options, see [Running Analysis / Launch Analysis](/product-guides/dragen-array-cloud-analysis/overview-1/launch-analysis). ### Important Notes for Semi-Custom Arrays * PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. * When designing a semi-custom array based on a commercial Infinium PGx array (such as the Global Diversity Array with enhanced PGx), it is important to retain all commercial content (i.e., keep all content from the commercial product, such as GDA-ePGx) in the design, as removing content could decrease the quality of results. For detailed information on running complete semi-custom PGx analysis (including steps with semi-custom product files and commercial product files), see the [Pharmacogenomic Analysis for semi-custom arrays](/product-guides/dragen-array-local-analysis#section-pharmacogenomic-analysis-for-semi-custom-arrays) section in the DRAGEN Array Local Analysis documentation. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/overview/our-features#product_compatibility) table. ## Additional Resources * [PGx Allele Definitions and PGx Guidelines](/pharmacogenomics-pgx-reference/pgx-allele-definitions-and-pgx-guidelines) * [PGx Star Allele Coverage](/pharmacogenomics-pgx-reference/pgx-star-allele-coverage) * [PGx CNV Coverage](/pharmacogenomics-pgx-reference/pgx-cnv-coverage) # BaseSpace Microarray on BaseSpace Sequence Hub provides cloud-based analysis capabilities for Illumina microarray data. The platform supports genotyping, PGx, cytogenetics and other analyses through [DRAGEN Array Applications](/overview/our-features). It also supports [polygenic risk score calculations](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm). ## Prerequisites The following prerequisites are needed to get started: * **BioInsight Platform Core (formerly ICA) subscription**: A Platform Core Basic, Professional or Enterprise subscription can be used, which includes access to BaseSpace Sequence Hub. Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) to register the software. * **Workgroup setup**: Workgroups must be created before login. Using a workgroup allows all members of the workgroup to share access to resources, analyses, and data. Learn more about [managing a Workgroup](https://help.basespace.illumina.com/collaborate/manage-workgroups). * The workgroup owner must be a member of the workgroup with the "Has Access" role assigned. Do not use the "Has Access + Archive" role. * Designating a workgroup as 'Collaborative' allows projects to be shared with collaborators or Illumina Tech Support to assist with troubleshooting. To create a collaborative workgroup, select the Enable collaborators outside of this domain checkbox during workgroup creation. * **Software consumables**: BioInsight Credits (BICs) can be purchased for storage on the cloud platform and analysis pipelines with a compute charge. Per sample analysis can be purchased for relevant pipelines as listed in [DRAGEN Array Applications](/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm). Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) (found under *Example 3: Configuring the Software Consumables*) to register the software consumables. * **\[Optional] iScan integration**: The iScan System is integrated with Illumina Connected Platform and can send IDATs for further analysis. The iScan System must be running iScan Control Software version 4.2.1 or later. * [Instructions to Use BioInsight Platform Core (formerly ICA) with the iScan System](http://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/UseICA_fIS.htm) * [Troubleshooting iScan integration](/product-guides/dragen-array-cloud-analysis/overview-1/troubleshoot-iscan) * **EULA acceptance**: Accept all necessary End User License Agreements in BaseSpace Sequence Hub before scanning begins. * **Internet connection**: For uploading product files or IDATs, a network connection 1 GbE or faster is recommended. Note: Accessioning BeadChips before scanning and starting analysis is no longer a required step and has been automated within the system. ## Accessing the Microarray Hub The Microarray Hub is the central location for managing microarray data and analyses on BaseSpace Sequence Hub. Before using the Microarray Hub, ensure workgroup context is being used so all data and analyses can be viewed by all members of your workgroup. The name of your workgroup should appear in the top right corner. Navigate to the Microarray Hub by selecting the **Runs** tab, then the **Microarray scans** tab. ## Microarray Hub Tabs The Microarray Hub contains two tabs: * [**Data Management**](/product-guides/dragen-array-cloud-analysis/overview-1/manage-data) - View and manage scanned IDAT files, and start analyses with data that is ready. * [**Planned Analyses**](/product-guides/dragen-array-cloud-analysis/overview-1/plan-analysis) - View and manage planned analyses that will auto-launch when sample data becomes available. # Manage Data The Data Management tab allows you to view and manage all your scanned IDAT files in the cloud. From this tab, you can also start an analysis with data that is ready. To access this tab, navigate to the [Microarray Hub](/product-guides/dragen-array-cloud-analysis/overview-1) and select **Data Management**. ## Viewing Data To view your scanned array data, use the filtering and sorting options: * Filter by **Upload Status** to see files based on their current state. * Sort and filter by **Upload Date** to find files from a specific time period. ## Deleting Data To delete IDAT files: * Check boxes for individual samples on the left-hand side, or * Use the top checkbox to select all samples on the current page for bulk deletion. {% hint style="info" %} Deleting the selected items will permanently delete them and the action cannot be undone. Deleting items can affect ongoing analysis. Ensure there is no ongoing analysis with the selected items before proceeding. {% endhint %} ## Starting an Analysis Select **Start an analysis** to begin an analysis with your scanned data. See [Launch Analysis](/product-guides/dragen-array-cloud-analysis/overview-1/launch-analysis) for detailed steps on configuring and launching an analysis. # Launch Analysis ## Running Analysis This page describes how to start an analysis immediately using data that has already been scanned and uploaded. To access this workflow, navigate to the [Microarray Hub](/product-guides/dragen-array-cloud-analysis/overview-1), select the **Data Management** tab, and select **Start an analysis**. {% hint style="info" %} If you want to set up an analysis before your samples have been scanned, see [Planned Analyses](/product-guides/dragen-array-cloud-analysis/overview-1/plan-analysis) to create an analysis that will auto-launch when sample data becomes available. {% endhint %} Use the following steps to configure and launch an analysis: 1. Enter the Analysis Name 2. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 3. Select the Type of Analysis\ Further detail of each Type of Analysis is available in [DRAGEN Array Applications](/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) 4. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected. Details on file name constraints can be found in the [BioInsight Platform Core documentation](https://help.ica.illumina.com/project/p-data#file-folder-naming). Custom configuration options vary by Type of Analysis selected. More details are available in section [DRAGEN Array Applications](/overview/our-features). 5. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [DRAGEN Array Applications](/overview/our-features). 6. Select Next 7. Select either **Import Sample Sheet,** **Select BeadChips,** or **Import IDAT Files** * **Import Sample Sheet** presents a link to upload sample sheet. Users may download a template sample sheet by selecting the Download Template link. * **Select BeadChips** allows users to select BeadChips from the displayed list of available BeadChips. If selecting specific samples within the BeadChip is desired the Import Sample Sheet option should be used. * **Import IDAT Files** allows users to upload the IDAT files from a local folder to the cloud platform for use with the current and future analyses by users within the same workgroup. Note that manually uploaded IDATs are not QC-checked for failing registration scores. 8. Select **Launch Analysis** ## View Outputs 1. On the Analyses tab, view the analysis status, e.g., initializing or complete. 2. After the analysis is complete, select the analysis and select the Files tab. 3. From the Files tab, select the Output folder. ## Troubleshooting IDAT Import ### Checking Import Status The best way to check the import status is to go to the [Data Management](/product-guides/dragen-array-cloud-analysis/overview-1/manage-data) page. ### Sample is Not Ready For Analysis There are background processes necessary for analysis that run after the IDAT upload session has completed. In most cases these processes should take a few minutes at most after which you may launch analysis. If after some time there are samples that are still not marked ready for analysis, try re-importing the IDATs for the affected samples. If the issue persists, please contact Illumina Tech Support at . ### Product Mismatch The system requires a product association for every BeadChip. Please ensure that the product associated with the selected analysis configuration is also associated with the IDATs selected for import. Mismatch between the IDAT product identity and the analysis configuration selected may affect the ability to select samples for analysis. Contact to resolve product mismatch issues. ### Upload Timeout There is an idle timeout that may take effect for longer uploads. This means that if you are inactive for more than the set timeout duration you will be automatically logged out and file upload will stop. To avoid any interruption with the file upload, we recommend that you stay active on the page while your file is uploading. Alternatively, if you plan on uploading large amount of samples at a time, we suggest breaking your upload into multiple batches to avoid hitting the timeout. The timeout duration may be configured for enterprise domains by following the steps found in [Session Management](https://help.basespace.illumina.com/manage-your-account/manage-enterprise-domain#session-management). # Plan Analysis The Planned Analyses tab allows you to view and manage planned analyses that are configured to auto-launch when sample data becomes available. This is useful when you want to set up an analysis before your samples have been scanned. To access this tab, navigate to the [Microarray Hub](/product-guides/dragen-array-cloud-analysis/overview-1) and select **Planned Analyses**. {% hint style="info" %} If your sample data is already available and you want to start an analysis immediately, see [Launch Analysis](/product-guides/dragen-array-cloud-analysis/overview-1/launch-analysis). {% endhint %} ## Planning an Analysis Planning an analysis allows you to configure an analysis before your sample data has been scanned and uploaded. Once configured, the planned analysis will automatically launch when all sample data becomes available. Select **Plan an analysis** from the Planned Analyses tab to create a new planned analysis. Use the following steps to configure a planned analysis: 1. Enter the Analysis Name 2. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 3. Select the Type of Analysis\ Further detail of each Type of Analysis is available in [DRAGEN Array Applications](/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) 4. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected. Details on file name constraints can be found in the [BioInsight Platform Core documentation](https://help.ica.illumina.com/project/p-data#file-folder-naming). Custom configuration options vary by Type of Analysis selected. More details are available in section [DRAGEN Array Applications](/overview/our-features). 5. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [DRAGEN Array Applications](/overview/our-features). 6. Select Next 7. Select **Import Sample Sheet** to upload a sample sheet\ Download a template sample sheet by selecting the Download Template link. The samples listed in the sample sheet do not need to be scanned or have data uploaded yet. More details are available in the [Sample Sheet section](/product-guides/input-files#section-sample-sheet). 8. Specify the **Maximum failed scanned samples** threshold\ This setting determines how many samples can fail during scanning before the planned analysis is aborted.

A sample is considered failed during scanning when the stripe registration is less than 0.75. Failed stripes are flagged as potentially misregistered and appear colored red in the Scan Progress Indicator window of the iScan Control Software.

9. Save the planned analysis ## How Planned Analyses Execute Once a planned analysis is saved, the system monitors for sample data availability: * **Auto-launch**: When all sample data listed in the sample sheet becomes available, the analysis will automatically launch. * **Abort on threshold exceeded**: If the number of samples that failed during scanning exceeds the **Maximum failed scanned samples** threshold, the planned analysis will be aborted. * **Launch with passing samples**: If some samples fail but the count is within the threshold, the analysis will launch automatically with only the passing samples. ## Viewing Planned Analyses The Planned Analyses tab displays a list of all planned analyses. Click on any analysis to view its details, including the configuration settings and sample information. ### Overview Card The overview card at the top of the Planned Analyses tab provides a quick summary of your analyses by status: * **Planned**: Total number of analyses with planned status (not yet launched) * **Planned & Launched**: Number of analyses that have been planned and subsequently launched * **Aborted Before Launch**: Number of analyses that were aborted before launching You can filter the analyses table by selecting a date range in the overview card. This will display only the planned analyses that have been modified within the selected date range. Clicking on any of the status count numbers will automatically filter the analyses table to show only analyses with that status. ## Viewing Single Analysis Details Click on any planned analysis in the table to view its complete details. The details page is organized into three sections: ### Configuration The Configuration section displays the core settings for your planned analysis: * **Analysis Name**: The name you assigned to the planned analysis * **Project**: The project where the analysis output files will be stored * **Analysis Type**: The type of analysis selected * **Detailed Configuration Settings**: The specific configuration parameters for your selected analysis type ### Samples The Samples section contains information about the samples included in your planned analysis: * **Samplesheet**: Download the samplesheet that was imported for this analysis * **Maximum Failed Samples Allowed**: The threshold you set for how many samples can fail during scanning before the planned analysis is aborted ### Status The Status section shows the current state and progress of your planned analysis: * **Status**: The current status of the analysis (e.g., planned, launched, aborted) * **Array Status Summary**: A summary of the scanning status for all samples in the analysis or the launch status * **Export Sample Status**: Download a detailed file containing the status of every individual sample in the analysis. This file provides information about each sample's processing status and any errors encountered. #### Sample Statuses and Reasons When you export the sample status, each sample will have a **Status** and a **Reason**. There may be multiple possible reasons for each status, depending on the sample's situation. The reason provides additional explanation for why the sample received its status. | Status | Reason | Explanation | | ------- | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Ready | Sample will be included in analysis due to successful data upload. | The sample data is available and meets all requirements for inclusion in the analysis. | | Failed | Sample will be excluded from analysis due to scanning failure. | Sample failed during scanning because the stripe registration is less than 0.75 (flagged as potentially misregistered in iScan Control Software). | | Pending | Sample is pending data upload. | The sample is not ready for analysis because it has not yet been scanned or its data has not been uploaded. | ## Deleting Planned Analyses Only planned analyses with a **planned** status (analyses that have not yet launched) can be deleted from this page. Deleting a planned analysis removes it from the system and prevents it from auto-launching when sample data becomes available. To delete planned analyses: * Check boxes for individual analyses on the left-hand side, or * Use the top checkbox to select all analyses on the current page for bulk deletion. {% hint style="info" %} Deleting a planned analysis will permanently remove it and the action cannot be undone. Analyses that have already launched cannot be deleted from this page. {% endhint %} # Share Project Project sharing allows a user to share files with users outside the workgroup for collaboration or with Illumina Tech Support for troubleshooting. To share a project on BaseSpace Sequence Hub, first set the Workgroup type as 'Collaborative' during [Workgroup setup](/product-guides/dragen-array-cloud-analysis/overview-1#section-prerequisites), and then use the following steps to obtain a link to your project. The project can then be accessed by anyone with the link. All files in the project are shared. ## Steps to Share a Project 1. Navigate to the Projects tab 2. Click the button next to the desired project 3. Select the Share button above to list 4. Select the Get Link Option to Activate a link for the project 5. Copy the link and send it to the desired recipient(s) ## Additional Notes * **Ownership**: The project owner maintains ownership and write access. If project owner deletes the data, the collaborators lose access to it. * **Collaborative workgroup requirement**: Either sending or receiving domain must be collaborative. See [Workgroup setup in BaseSpace](https://help.basespace.illumina.com/microarray/overview) for more information. * **Regional restrictions**: Must be in the same [AWS regional instance](https://help.basespace.illumina.com/manage-your-account/regions). Data cannot be transferred directly between instances, however you can download and share data separately. * **Enterprise domains**: Use this same [share-by-link method](https://help.basespace.illumina.com/collaborate/share-with-collaborators/share-by-link), not share-by-transfer. # Troubleshoot iScan Integration The firewall protects the iScan control computer by filtering incoming traffic to remove potential threats. The firewall is enabled by default to block all inbound connections. Keep the firewall enabled and allow outbound connections. For the instrument to connect to BaseSpace Sequence Hub, you will need to add regional platform endpoints and instrument specific endpoints to the allow list on your firewall. Regional endpoints and further detail can be found in [Security and Networking for Illumina instrument control computers](https://support-docs.illumina.com/SHARE/NetworkSecurity/Content/SHARE/FrontPages/NetworkingSecurity.htm). The following table shows the applicable endpoints for the iScan.
EndpointCategoryPurpose
ica.illumina.comRequiredSend IDAT files to Platform Core
o.ss2.usRequiredCertificate authorization
ocsp.digicert.comRequiredCertificate authorization
ocsp.pki.goog/gsr2RequiredCertificate authorization
ocsp.rootca1.amazontrust.comRequiredCertificate authorization
ocsp.rootg2.amazontrust.comRequiredCertificate authorization
ocsp.sca1b.amazontrust.comRequiredCertificate authorization
fonts.gstatic.comRequiredDisplay fonts
fonts.googleapis.comRecommendedDisplay fonts
cdn.walkme.comRecommendedTelemetry
cdn3.userzoom.comRecommendedTelemetry
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## IDAT Fail Status iScan will mark certain samples with a FAIL status if the registration quality is too poor for that particular section. Selected samples that are marked with FAIL status will be excluded from analysis and there would be no results for that sample, even though IDATs are generated. These samples will not be available for selection through beadChip table or samplesheet upload when launching analysis. The registration quality can be found in the `metrics.txt` file. More information on that file can be found in the [iScan documentation](https://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/ScanMetrics_fIS.htm). # DRAGEN Array Local Analysis ## DRAGEN Array Local Overview DRAGEN Array provides accurate, comprehensive, and efficient analysis of Infinium microarray data. The local command-line interface makes it easy for power users to have granular control and flexibility to support large scale microarray genomic studies. ## Getting Started DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. If new to using command-line interface, please review the [Command-line interface Basics](#tips-for-using-the-command-line-interface). ### Computing Requirements Before downloading and installing the software, ensure the following specifications are met for best performance: | Category | Recommendation | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | CPU | 8 cores | | Memory | 32 GB available or more | | Hard Drive | 30 GB or more of free disk space | | Operating System |

One of the following:

  • Windows 10 or later – win10-x64
  • CentOS 7 or later, Ubuntu 20.04 or later – linux-x64
| **Note on Cybersecurity:** DRAGEN Array is not required to run as adminstrative user. We recommend you do not run with elevated permissions. ### Quota Specifications The star-allele call command in DRAGEN Array Local requires quota to run. The quota is charged per sample analyzed and can be purchased on the [Illumina Product Page](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html). Quota is used for all samples analyzed including re-analysis or low-quality samples. Quota is checked before and after analysis but not after updating the usage. Users will need to re-run the command to re-check the current usage after a run. The credential provided in the activation email after purchasing should be used as an input to the star-allele call command through the "--license-server-url" option. During runtime, the [logs](/product-guides/output-files#section-warningerror-messages-and-logs) will record the remaining quota at the beginning and the end of the analysis. Internet is required to do a software license check and ensure paid quota is available for all samples in the analysis batch. For the software license check, the following endpoints are used: * In v1.0 and v1.1: `license.edicogenome.com` * In v1.2+: `license.dragen.illumina.com` **NOTES:** * Do not use `license.dragen.illumina.com` license server urls when running DRAGEN Array v1.0 and v1.1 as that domain only works with v1.2+ versions. This is described in the [1.0.0](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes#section-known-issues) and [1.1.0](/support-and-updates/release-notes/dragen-array-v1.1.0-release-notes#section-known-issues) known issues. * In v1.1+, during analysis, precomputed quota is no longer checked. This can result in a scenario where an analysis run can be over-quota, but will not fail until the end of the run. An example: if there is only quota for 6 samples, but the analysis run contains 8 samples, the analysis will proceed as normal until the end when usage is updated the software will produce the following error: `Error updating usage. HTTP error status code: 409` and will not write the results to disk. ## Installation Please follow the steps below to install the software on your compute infrastructure: 1. Click on the latest DRAGEN Array version installation package for the platform of your choice. Installers for Windows and Linux are available on the [Illumina Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html).\ \ Once download is completed, move the DRAGEN Array installation package to the desired folder. Administrative permissions may be required for system folders, for example `/usr/local/bin for Linux`, and `C:\Program Files` for Windows.\ \ **Note**: Throughout the remainder of the document, Linux will be assumed in the examples. 2. Unzip and extract the package. The executable can be found in the dragena subfolder of the software download after extraction. 3. To check that the DRAGEN Array installation was successful, follow these steps: * Open a command prompt (Windows) or terminal (Linux). * \[Optional] Add `/path/to/dragena/`, e.g. `/usr/local/bin/dragena-linux-x64-DAv1.4.0/dragena/`, to your PATH – to access the executable anywhere in the folder structure * Execute the following command: `/path/to/dragena/dragena version`, or if the environmental variable PATH is set: dragena version The version of the software will be displayed in the terminal window when the installation was successful. ### Linux Dependencies DRAGEN Array is published as a self-contained .NET application. While the application bundle includes the .NET runtime, certain native libraries provided by the host operating system are still required. If these dependencies are missing, the application may fail to start or produce runtime errors. For **Debian/Ubuntu**-based Linux distributions, see [.NET DEB dependencies](https://learn.microsoft.com/en-us/dotnet/core/install/linux-scripted-manual#deb-dependencies). For **RHEL/CentOS/Fedora** and other **RPM**-based Linux distributions, see [.NET RPM dependencies](https://learn.microsoft.com/en-us/dotnet/core/install/linux-scripted-manual#rpm-dependencies). ## Run DRAGEN Array Local For genotyping or cytogenetic analysis, there is no sample minimum required to run analysis. For CNV PGx analysis, a minimum of 24 samples is required to run analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. With a standard hardware specification in section [Computing Requirements](#computing_requirements), up to 500 GDA-ePGx samples can be processed per analysis batch. To optimize performance of the targeted PGx CNV caller and minimize batch effect, it is recommended to: * Group samples in the same assay batch (e.g. whole genome amplication and targeted gene application assay batch) into the same analysis batch. * Avoid combining sample batches processed on different reagent lots. * Analyze batches of 96 samples or more. * Samples processed in a two-week period from multiple library preparation batches can be grouped together to meet size requirement of an analysis batch. In such cases, it is recommended to use the same lot of reagents and instruments used in the workflow. * Use the CN Model and PGx Database File provided as part of the standard product files ## Quick Start Review section [DRAGEN Array Applications](/overview/our-features) for information on input files to use, sample minimums per analysis type and other best practices. Command examples show analysis for a Linux system using folders instead of sample sheets. For Windows users, make sure to substitute the file paths in the commands following windows conventions, e.g., using backslash (\\) instead of forward-slash (/). A sample sheet can be used to select specific samples out of a folder. **Note**: DRAGEN Array will overwrite older files if using the same `--output-folder` from a previous analysis. If this is not desired, use different `--output-folder` for re-analyses. ### Genotyping Use the following instructions to start genotyping analysis. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf` ### QC Report Use the following instructions to generate an interactive QC Report (HTML format) and QC Table (spreadsheet) for genotyping arrays. User-defined metadata provided in a [Sample Sheet](/product-guides/input-files#section-sample-sheet) at the beginning of this workflow will be present in the final QC Report. See [Metadata propagation (gt\_sample\_summary)](/product-guides/dragen-array-local-analysis/qc-report#metadata-propagation-gt_sample_summary) for details on how metadata is carried into the QC outputs. Refer to [Command Index](#command_index_1) for parameters for all commands, and see Analysis Workflow section of [DRAGEN Array QC Report](/product-guides/dragen-array-local-analysis/qc-report) for full instructions, which also covers methylation arrays. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed, or to a different desired directory if the executable was added to the PATH environment variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input. `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Either use directly the output folder from Step 2, or prepare a dataset folder containing the following output files from Step 2. If you want to combine multiple datasets, see [Multiple dataset folders](/product-guides/dragen-array-local-analysis/qc-report#multiple-dataset-folders) and [Parent folder containing multiple dataset folders](/product-guides/dragen-array-local-analysis/qc-report#parent-folder-containing-multiple-dataset-folders). * controls.raw\_metrics.csv (required) * controls.qc\_metrics.csv (required) * gt\_sample\_summary.csv (highly recommended) 4. Use the qc report command to generate QC Report and QC Table using the dataset folder from Step 3 as input. Note that the [Configuration file is optional](/product-guides/dragen-array-local-analysis/qc-report#configuration-file-optional). `dragena qc report --data /user/gtc --config /user/config.yaml --output-folder /user/qc_report --label dataset_name` ### PGx Use the following instructions to start the full PGx analysis, covering genotyping, PGx CNV and PGx star allele calling. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the pgx copy-number call command to call PGx CNVs from the GTC files and produce CNV VCF files. It is recommended to use the same output folder used for SNV VCF since the star-allele call command accepts one VCF folder with SNV and PGx CNV VCFs.\ `dragena pgx copy-number call --cn-model /user/productfiles/manifest_pgx_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` **Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. 5. Use the pgx star-allele call command to generate star allele calls using the CNV and SNV VCF files generated by the gtc-to-vcf and copy-number call commands.\ `dragena pgx star-allele call --vcf-folder /user/vcf --database /user/productfiles/DAv1.4.0-rc2-pgx-mapping-sha.b164b4aa02f34cc9b72aba020c3a8735cb8e4421.zip --output-folder /user/star-alleles --license-server-url https://username:password@license.dragen.illumina.com` **Note**: For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 6. Use the pgx star-allele annotate command to summarize the star alleles into CSV files and add metabolizer statuses to the star alleles generated by the star-allele call command. Guidelines (CPIC or DPWG) can be specified.\ `dragena pgx star-allele annotate --star-alleles star_alleles.dat --guidelines CPIC --output-folder /user/metabolizer-statuses` 7. \[Optional] Use the pgx copy-number train command to retrain the copy number model.\ `dragena pgx copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --platform LCG --output-folder /user/productfiles/cnmodelnew` ### Cytogenetics Use the following instructions to start the full cytogenetics analysis, covering genotyping, CNV and LOH calling, and annotation. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the cyto call command to determine copy number variants and loss of heterozygosity given genotypes.\ `dragena cyto call --cn-model /user/productfiles/cyto_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the cyto annotate command to generate JSON annotation files with gene annotations, cytogenetic bands, various QC fields, and the variant information from the VCFs.\ `dragena cyto annotate --annotation-db /user/productfiles/CytoAnnotateData_DAv1.2.0.zip --vcf-folder user/vcf --output-folder /user/cyto-annotations` ## Command Index Use the following syntax when using the command-line interface: `dragena [module] [sub-module (not needed for cyto)] [command] [required parameters] [optional parameters]` **Note:** Command names in this guide are lowercase, but CLI options are case-sensitive and must be entered exactly as shown. In particular, the `qc report` threshold options use mixed case: `--threshold-callRate` and `--threshold-logRdev`. | Module | Description | | -------- | --------------------------------------------------------------------------------- | | qc | Extract control probe intensities, compute QC metrics and generate QC reports. | | genotype | Call genotypes, single nucleotide variants, and various related file conversions. | | pgx | Pharmacogenomics CNV, star allele calling and metabolizer status annotation. | | cyto | Cytogenetics CNV/LOH/mosaic calling and annotation. | | help | Display more information on a specific command. | | version | Display version information. | ### **help** Displays the first-layer help information. ### **version** Displays current DRAGEN Array Local version. ### **qc** The root command for QC. | Command | Description | | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | qc call | Performs QC sample analysis given raw iScan data (IDAT). | | qc report | Generates a comprehensive microarray QC report from one or more dataset folders containing DRAGEN Array QC outputs. Each dataset folder must include `controls.raw_metrics.csv` and `controls.qc_metrics.csv`, and can also include `gt_sample_summary.csv` for functional QC. Supports parent-folder discovery, dataset labeling, and interactive dashboards for quality assessment and troubleshooting. | | qc help | Displays the help information for the qc command. | | qc version | Displays version information for the qc command. | ### **qc call** Extracts raw control probe intensities and computes QC metrics from IDAT files. | Option | Description | | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --array-type | \[Required] Array type for QC analysis. Valid values: genotyping, methylation | | --csv-manifest | \[Required] Path to [CSV manifest](/product-guides/input-files#manifest_files) file. | | --idat-folder |

\[Required]

Specifies the path to the directory where all intensity data IDATs (for the samples to be processed) are located. If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet.

This path also includes the contents of all subdirectories.

| | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering. | | --output-folder | Directory path to output files. Default is the current working directory. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the qc call command. | | --json-log | Outputs logs in JSON format. Default is false. | **Note:** Either --idat-folder, --sample-sheet, or both are required inputs. ### **qc report** Generates a QC Report with comprehensive quality assessment for microarray datasets processed with the DRAGEN Array platform. Input dataset folders should contain CSV outputs generated by other DRAGEN Array commands: `controls.raw_metrics.csv` and `controls.qc_metrics.csv` from either `dragena qc call` or `dragena genotype call`, and optionally `gt_sample_summary.csv` from `dragena genotype call` for functional QC metrics. | Option | Description | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --data | **\[Required]** `` One or more dataset folder paths. Each dataset folder must contain `controls.raw_metrics.csv` and `controls.qc_metrics.csv`, generated by either `dragena qc call` or `dragena genotype call`, and can also include `gt_sample_summary.csv`, generated by `dragena genotype call`, to add functional QC metrics. Provide multiple dataset folder paths as a comma-separated list, or provide one parent folder path to automatically discover dataset folders beneath it. | | --config | `` Path to a YAML configuration file for default QC thresholds. Command-line threshold options take precedence over config values. Defaults: `callRate = 0.98`, `logRDev = 0.20`. All other thresholds are unset by default. See [Configuration file (optional)](/product-guides/dragen-array-local-analysis/qc-report#configuration-file-optional) for template configuration files and usage guidance. | | --label | `` Optional labels for each dataset (comma-separated). The number of labels must match the number of datasets. If omitted, dataset folder names are used as labels. | | --output-folder | Directory path to output files. Default is the current working directory. | | --output-format | `` Output format for the sample QC table. Default: `xlsx`. The XLSX format includes conditional coloring and a **“Thresholds”** sheet. | | --threshold-callRate | `` Override the call rate threshold used to determine Functional QC pass/fail status. Default: `0.98`. If provided, this value overrides the corresponding threshold in the YAML file passed with `--config`. | | --threshold-logRdev | `` Override the logRDev threshold used to determine Functional QC pass/fail status. Default: `0.20`. If provided, this value overrides the corresponding threshold in the YAML file passed with `--config`. | | --debug | Logs will include stack traces. Default is `false`. | | --json-log | Logs will be output in JSON format. Default is `false`. | | --help | Display this help screen. | | --version | Display version information. | **Note:** The `callRate` and `logRDev` thresholds are the thresholds used to determine Functional QC pass/fail status. If `--config` is omitted, built-in defaults are used. If `--threshold-callRate` or `--threshold-logRdev` is provided, the command-line value takes precedence over the corresponding value in the YAML config file. {% hint style="danger" %} If the input data for `qc report` was generated while using a sample sheet, sample metadata such as sample names, process information, and other sample-sheet values will be carried into the QC report outputs, including tables and HTML files. Review QC report outputs before sharing them. If you need to remove some metadata before sharing, remove it from `gt_sample_summary.csv` and rerun the `qc report` command. {% endhint %} ### **qc help** Displays the help information for a qc command. ### **qc version** Displays current DRAGEN Array Local version. ### **genotype** The root command for genotype calling. | Command | Description | | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- | | genotype call | Determines genotype calls (GTC) from IDAT files. | | genotype gtc-to-bedgraph | Converts GTC to BedGraphs, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files. | | genotype gtc-to-vcf | Converts GTC to VCF. | | genotype help | Displays the help information for the genotype command. | | genotype version | Displays version information for the genotype command. | ### **genotype call** Determines genotype calls (GTC) from IDAT files. | Option | Description | | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the [bead pool manifest in BPM format](/product-guides/input-files#manifest_files). | | --cluster-file | \[Required] Specifies the path to the [EGT cluster file](/product-guides/input-files#section-cluster-file) to use. | | --idat-folder |

Specifies the path to the directory where all intensity data IDATs (for the samples to be processed) are located. If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet.

This path also includes the contents of all subdirectories.

| | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --gencall-cutoff | GenCall score cutoff to label a NoCall. Default is 0.15. | | --help | Displays help information for the genotype call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --num-threads | Number of parallel threads to run. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | **Note**: Either --idat-folder, --sample-sheet, or both are required inputs. ### **genotype gtc-to-bedgraph** Converts GTC to BedGraph files, producing BedGraph formatted visualization files from the Log R Ratio and B-allele frequency data contained in the GTC intermediate files. | Option | Description | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --bpm-manifest | \[Required] Specifies the path to the [bead pool manifest in BPM format](/product-guides/input-files#manifest_files). | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --debug | Include stack traces in logs. Default is false. | | --help | Displays help information for the genotype gtc-to-bedgraph command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | **Note**: Either --gtc-folder, --sample-sheet, or both are required inputs. ### **genotype gtc-to-vcf** Converts GTC (v5) to [SNV VCF Files](/product-guides/output-files#snv_vcf_file). The command is only applicable for [Genotype Call Files](/product-guides/output-files#genotype_call_file) produced by DRAGEN Array. | Option | Description | | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the [bead pool manifest in BPM format](/product-guides/input-files#manifest_files). | | --csv-manifest | \[Required] Specifies the path to the [CSV manifest](/product-guides/input-files#manifest_files) with SourceSeq column. | | --genome-fasta-file | \[Required] Specifies the path to the [genome FASTA file](/product-guides/input-files#section-genome-fasta-files) (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --auxiliary-loci | Specifies the path to the VCF file with auxiliary definitions of loci, such as for multi-nucleotide variants. | | --debug | Include stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --filter-loci | Generates a text file containing a list of probe names to be filtered. | | --unsquash-duplicates | Generates unique VCF records for duplicate assays. Default is false. | | --help | Displays help information for the genotype gtc-to-vcf command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --use-infI-nc-info | When combining loci, use Infinium I probe no-calls to constrain possible genotypes. Default is false. See [Using Infinium I NoCall Information](/product-guides/output-files#using-infinium-i-nocall-information) for more details. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | #### Squashing duplicates In the manifest, there can be cases where the same variant is probed by multiple different assays. These assays may be the same design or alternate designs for the same locus. In the default mode of operation, these duplicates will be "squashed" into a single record in the VCF to reflect a true variant rather than probe genotype. The method used to incorporate information across multiple assays is defined further in the [VCF description](/product-guides/output-files#snv_vcf_file). When the `--unsquash-duplicates` option is provided, this "squashing" behavior is disabled, and each duplicate assay will be reported in a separate entry in the VCF file. This option is helpful when you are interested in investigating or validating the performance of individual assays, rather than trying to generate genotypes for specific variants. Note that if a locus has more than two alleles and is also queried with duplicated designs, the duplicates will not be unsquashed (i.e., in the case of multi-allelic variants). **DO NOT** use `--unsquash-duplicates` option if doing star allele calling downstream as that command expects squashed variants. #### Genome cache By default, the entire reference genome will be read into memory. Generally, this will be more efficient than reading data from the indexed reference on disk at the expense of greater memory utilization. For situations in which the genome caching is not desirable (low memory availability or a small input manifest), it is possible to disable this default behavior with the `--disable-genome-cache` option. #### Auxiliary loci Certain classes of variant types (such as multi-nucleotide variants) are not currently supported in the upstream analysis software that produces GTC files. However, it is possible to query this type of variant by creating a SNP design that differentiates the specific multi-nucleotide alleles of interest. For example, if the true source sequence is ATGC\[AT/CG]GTAA This assay could be designed as a SNP assay with the following source sequence ATGC\[A/C]NNNN `gtc-to-vcf` provides an option (`--auxiliary-loci`) to supply a list of auxiliary records (in VCF format) to restore the true alleles for these cases in the output VCF. There are several restrictions around this function * The auxiliary definition must NOT be a multi-allelic variant. * The auxiliary definition must be a multi-nucleotide variant. * There must NOT be multiple array assays (e.g., duplicates) for the locus. **Notes:** * Either --gtc-folder, --sample-sheet, or both are required inputs. * The genome fasta files for human genomes are provided by Illumina on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ### **genotype help** Displays the help information for a genotype command. ### **genotype version** Displays current DRAGEN Array Local version. ### **pgx** The root command for pgx module | Command | Description | | ----------- | ---------------------------------------------- | | copy-number | Call and train copy number variants. | | star-allele | Star Allele Caller for Illumina Microarrays | | help | Display more information on a specific command | | version | Display version information. | ### **pgx copy-number** The root command for actions that act on pgx copy number variants. | Command | Description | | ----------------------- | --------------------------------------------------------------------- | | pgx copy-number call | Determines copy number variants given genotypes (GTC to CNV VCF). | | pgx copy-number help | Displays help information for a copy-number command. | | pgx copy-number train | Trains copy number model for a set of samples (GTC to CN Model File). | | pgx copy-number version | Displays version information for copy-number. | ### **pgx copy-number call** The command used to call copy number variants. A batch of 24 samples or more are required for analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. | Option | Description | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --cn-model | \[Required] Specifies the path to the [copy number model parameters file](/product-guides/input-files#cn_model_file) (.dat). | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the copy-number call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | ### **pgx copy-number train** Trains pgx copy number (CN) model for a set of samples. Generate a new pgx CN model if using a customized cluster file (.egt) optimized for the specific data set. * Execute the train command using the data sets that were used to optimize the cluster file. * To use a pgx CN model generated by the train command, the mask file for the manifest must be saved in the same directory as the manifest. * A minimum of 96 samples is required to use the copy-number train command. For optimal performance, at least 150 is recommended. * For best performance, validate the pgx CN model using truth data before using in pgx CN calling. See [Optimizing cluster files and copy number models](#optimizing_cluster_files) for further details. | Option | Description | | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the [bead pool manifest in BPM format](/product-guides/input-files#manifest_files). Assumes [mask file](/product-guides/input-files#mask_file) (.msk) is in the same directory. | | --genome-fasta-file | \[Required] Specifies the path to the [genome FASTA file](/product-guides/input-files#section-genome-fasta-files) (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --platform | \[Required] Specifies which microarray platform generated the data. Set this to 'LCG' for GDA-ePGx, 'EX' for GSAv4-ePGx or GCRA-ePGx | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --help | Displays help information for the copy-number train command. | | --json-log | Outputs logs in JSON format. Default is false. | | --version | Displays version information. | | --output-folder | The location to output the CN model. By default, the output folder is the current working directory. | ### **pgx copy-number help** Displays help information for the copy-number command. ### **pgx copy-number version** Displays version information for pgx copy-number command. ### **pgx star-allele** The root command PGx star allele calling. | Command | Description | | ------------------------ | ---------------------------------------------------- | | pgx star-allele call | Determines PGx star allele and variant genotypes. | | pgx star-allele annotate | Annotate PGx gene functions and product JSON report. | | pgx star-allele help | Displays help information for a star allele command. | | pgx star-allele version | Displays version information for star allele. | ### **pgx star-allele call** Calls PGx star allele diplotypes. The SNV VCF files should be generated using the DRAGEN Array gtc-to-vcf command with unsquash-duplicates off (default) and without filter loci. | Option | Description | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | --database | \[Required] The [PGx database file](/product-guides/input-files#section-pgx-database-file) (.zip). | | --license-server-url | \[Required] The license server url with credentials. | | --vcf-folder | \[Required] The directory containing \*.snv.vcf.gz and \*.cnv.vcf.gz files. | | --query-license-quota | During beginning and end of analysis, the license server will be queried for the quotas on the valid license(s) and display the result. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Directory path to output files. Default is the current working directory. | | --version | Displays version information. | ### **pgx star-allele annotate** Annotates and summarizes the star-alleles, specifically for metabolizer statuses and outputs in a consolidated JSON report. Metabolizer status is determined through direct lookup into public PGx guidelines CPIC or DPWG as specified by the user. | Option | Description | | --------------- | -------------------------------------------------------------------------------------------- | | --star-alleles | \[Required] Path to star alleles file (.dat) generated by the call subcommand. | | --guidelines | PGx guidelines to use for annotation. Valid values are ‘CPIC’ and ‘DPWG’. Default is ‘CPIC’. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele annotate command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Directory path to output files. Default is the current working directory. | | --version | Displays version | ### **pgx star-allele help** Displays help information for a star-allele command. ### **pgx star-allele version** Displays version information for star-allele. ### **cyto** The root command for Cytogenetics analysis and annotation. | Command | Description | | ------------- | --------------------------------------------------------------------------- | | cyto call | Determines copy number variants and loss of heterozygosity given genotypes. | | cyto annotate | Annotates samples and generates cytogenetics json reports. | | cyto help | Display more information on a specific command. | | cyto version | Displays version information. | ### **cyto call** Determines copy number variants (CNV) and loss/absence of heterozygosity (LOH/AOH) given genotypes. | Option | Description | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --cn-model | \[Required] Path to [cyto model parameters file](/product-guides/input-files#cyto_model_file) (.dat). | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | [Sample sheet](/product-guides/input-files#section-sample-sheet) that allows for filtering and providing sample metadata. | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-del-probes | Deletion CNV size limit (probes). Only deletions with equal or more probes than the min-del-probes will be reported in the json file. Default is 10. | | --min-del-size | Deletion CNV size limit (kb). Only deletions with size equal or larger than the min-del-size will be reported in the json file. Default is 25kb. | | --min-dup-probes | Duplication CNV size limit (probes). Only duplications with equal or more probes than the min-dup-probes will be reported in the json file. Default is 10. | | --min-dup-size | Duplication CNV size limit (kb). Only duplications with size equal or larger than the min-dup-size will be reported in the json file. Default is 50kb. | | --min-loh-probes | LOH size limit (probes). Only LOH events with equal or more probes than the min-loh-probes will be reported in the json file. Default is 500. | | --min-loh-size | LOH size limit (kb). Only LOH events with size equal or larger than the min-loh-size will be reported in the json file. Default is 3000. | | --min-gainloh-probes | GAINLOH size limit (probes). Only GAINLOH events with equal or more probes than the min-gainloh-probes will be reported in the json file. Default is 300. | | --min-gainloh-size | GAINLOH size limit (kb). Only GAINLOH events with size equal or larger than the min-gainloh-size will be reported in the json file. Default is 500. | | --min-mdel-probes | Mosaic deletion CNV size limit (probes). Only mosaic deletions with equal or more probes than the min-mdel-probes will be reported in the json file. Default is 10. | | --min-mdel-size | Mosaic deletion CNV size limit (kb). Only mosaic deletions with size equal or larger than the min-mdel-size will be reported in the json file. Default is 25kb. | | --min-mdup-probes | Mosaic duplication CNV size limit (probes). Only mosaic duplications with equal or more probes than the min-mdup-probes will be reported in the json file. Default is 10. | | --min-mdup-size | Mosaic duplication CNV size limit (kb). Only mosaic duplications with size equal or larger than the min-mdup-size will be reported in the json file. Default is 50kb. | | --min-mloh-probes | Mosaic LOH size limit (probes). Only mosaic LOH events with equal or more probes than the min-mloh-probes will be reported in the json file. Default is 500. | | --min-mloh-size | Mosaic LOH size limit (kb). Only mosaic LOH events with size equal or larger than the min-mloh-size will be reported in the json file. Default is 3000. | | --max-mosaic-fraction | The maximum allowable mosaic fraction. Mosaic variants at or above this are promoted. Promoted variants are marked with a HIGHFRACTION INFO tag and retain their mosaic fraction value. Default is 1.0 | | --smoothing | Smoothing window size, specifying the number of probes on each side of the center probe used for smoothing LRR values. Default is 5. | **Notes:** * In general, more than 10 probes per variant region are recommended for confident CNV calling. * If a value below 10 is specified for the minimum CNV probe threshold (e.g., --min-dup-probes, --min-mdel-probes), the software will automatically reset the value to 10. * If a specified value exceeds the effective genome size, all variants will be filtered out. * Greater than 10 events (DEL/DUP/AOH) per chromosome is an indication of need for visual inspection. * If mosaic fraction cannot be estimated due to insufficient informative probes, it will be set to NaN. * Mosaic events that surpass the `--max-mosaic-fraction` limit have the MOSAIC tag in the INFO field of the VCF replaced with an HIGHFRACTION tag. * Mosaic events with a fraction below 15-20% may be missed. * LogRDev > 0.2 is indicative of a low-quality sample and is flagged with the SampleQuality flag in the VCF file. * For samples with LogRDev > 0.3, profiles are typically very noisy. In such cases, only whole-chromosome-level events are detected and reported to prevent excessive false positives. ### **cyto annotate** Annotates samples and generates cytogenetic json reports. | Option | Description | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --annotation-db | \[Required] [Database](/product-guides/input-files#cyto_db_file) for variant annotations. | | --vcf-folder | \[Required] The directory containing the \*.cnv.vcf.gz files. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-del-probes | Deletion CNV size limit (probes). Only deletions with equal or more probes than the min-del-probes will be reported in the json file. Default is 10. | | --min-del-size | Deletion CNV size limit (kb). Only deletions with size equal or larger than the min-del-size will be reported in the json file. Default is 25kb. | | --min-dup-probes | Duplication CNV size limit (probes). Only duplications with equal or more probes than the min-dup-probes will be reported in the json file. Default is 10. | | --min-dup-size | Duplication CNV size limit (kb). Only duplications with size equal or larger than the min-dup-size will be reported in the json file. Default is 50kb. | | --min-loh-probes | LOH size limit (probes). Only LOH events with equal or more probes than the min-loh-probes will be reported in the json file. Default is 500. | | --min-loh-size | LOH size limit (kb). Only LOH events with size equal or larger than the min-loh-size will be reported in the json file. Default is 3000. | | --min-gainloh-probes | GAINLOH size limit (probes). Only GAINLOH events with equal or more probes than the min-gainloh-probes will be reported in the json file. Default is 300. | | --min-gainloh-size | GAINLOH size limit (kb). Only GAINLOH events with size equal or larger than the min-gainloh-size will be reported in the json file. Default is 500. | | --min-mdel-probes | Mosaic deletion CNV size limit (probes). Only mosaic deletions with equal or more probes than the min-mdel-probes will be reported in the json file. Default is 10. | | --min-mdel-size | Mosaic deletion CNV size limit (kb). Only mosaic deletions with size equal or larger than the min-mdel-size will be reported in the json file. Default is 25kb. | | --min-mdup-probes | Mosaic duplication CNV size limit (probes). Only mosaic duplications with equal or more probes than the min-mdup-probes will be reported in the json file. Default is 10. | | --min-mdup-size | Mosaic duplication CNV size limit (kb). Only mosaic duplications with size equal or larger than the min-mdup-size will be reported in the json file. Default is 50kb. | | --min-mloh-probes | Mosaic LOH size limit (probes). Only mosaic LOH events with equal or more probes than the min-mloh-probes will be reported in the json file. Default is 500. | | --min-mloh-size | Mosaic LOH size limit (kb). Only mosaic LOH events with size equal or larger than the min-mloh-size will be reported in the json file. Default is 3000. | | --min-qual | Min CNV qual and LOH qual scores. Default is 20. | **Notes:** * If a value below 10 is specified for the minimum CNV probe threshold (e.g., --min-dup-probes, --min-mdel-probes), the software will automatically reset the value to 10. * If a specified value exceeds the effective genome size, all variants will be filtered out. * The metadata "cyto.cnv.dat" file that is generated during cyto call in the vcf-folder needs to be kept in the vcf-folder for cyto annotate. * The vcfs files need to be zipped and indexed for cyto annotate, which means "--no-bgzip" flag cannot be turned on for the cyto vcf file generation if those vcf files are going to be used for cyto annotate command. * The "cyto annotate" step needs at least 5GB free space on the hard drive. ### **cyto help** Display more information on a specific command. ### **cyto version** Displays version information. ## Troubleshooting and Additional Support ### Tips for using the Command-line interface DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. When using command-line consider the following tips: * Spaces cannot be part of a file name in a command. If the file name has spaces, use quotes around the file name * CLI options are case-sensitive. Enter option names exactly as shown in this guide, especially mixed-case options such as `--threshold-callRate` and `--threshold-logRdev`. * Do not place duplicate IDAT pairs for the same `SentrixBarcode_Position` under the same `--idat-folder` path. Here, `SentrixBarcode_Position` refers to the combined Sentrix barcode and position identifier (that is, `SentrixBarcode_A` + `SentrixPosition_A` in the samplesheet). If rescans exist, remove or relocate the unwanted IDATs before running `genotype call` or `qc call`. * To correct a typing error in a previously entered command, use the up arrow to repeat the previous command, then correct the error before re-entering it. * Double check the command. Misspelling, extra, or missing dashes, etc. will cause the command to be unrecognizable by the software. * When entering paths or long names, copy and paste the values to help avoid errors. * If using Windows, use a File Explorer window to navigate to the product file or folder that is needed by the DRAGEN Array Local command. While holding down the shift button on the keyboard, right click the file and select the 'Copy as Path' option. Then paste the copied path into the command prompt to use the file or folder. * To cancel a command while it is running, press Control + C on the keyboard. ### Optimizing cluster files and copy number models A [Cluster File](/product-guides/input-files#section-cluster-file) (.egt) contains the cluster positions of every probe used for genotyping analysis. Illumina provides a standard cluster file for all commercial Infinium BeadChips. It may be desirable to create a custom cluster file if the one provided does not fit the data well or if a semi-custom or custom BeadChips that do not come with a cluster file, are used. [GenomeStudio 2.0](https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) is the software used to create custom cluster files. When naming a custom cluster file, use only alphanumeric characters (a–z, A–Z, 0–9), hyphens (-), and underscores (\_). Spaces and other special characters are not permitted. To facilitate the review and optimization of PGx variant GenTrain cluster positions, a GenomeStudio auxiliary file is provided for each PGx Array product through the [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) and array product files page, e.g. [Infinium Global Diversity Array with Enhanced PGx Product Files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html). The auxiliary file is a tab-delimited text file that can be imported into GenomeStudio through Column Import. The file contains the Infinium Assay to PGx star allele mapping, covering the variants involved in DRAGEN Array PGx star allele calling. When updating the cluster file for pharmacogenomic applications, understand the specifications for the copy number model file before beginning. Before creating a custom cluster file, review the [Infinium Genotyping Data Analysis Technical Note](https://www.illumina.com/Documents/products/technotes/technote_infinium_genotyping_data_analysis.pdf), the [Infinium Arrays Support Webinar Video](https://youtu.be/4JTrbMUbVN0?si=ZgRDLwN6umGBhv2G), and [Custom cluster file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). A [PGx Copy Number (CN) Model File](/product-guides/input-files#cn_model_file) (.dat) contains the data needed to make accurate copy number calls for pharmacogenomics. This file is used in the creation CNV VCFs which are inputs to the star allele calling command. Illumina provides a standard CN model file for all commercial PGx Infinium BeadChips. If it is determined the cluster file needs to be customized, the CN Model File should also be updated using the copy-number train command available with DRAGEN Array Local only. i.e., 1. Use GenomeStudio 2.0 to generate a new cluster file. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/new_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/new_gtcs` 3. Use the copy-number train command to retrain the copy number model. **Note: The --platform option can be found in the `Assay Format` heading value from the CSV manifest.**\ `dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/new_gtcs --platform LCG --output-folder /user/productfiles/new_cnmodel` 4. Use the `new_cnmodel` for subsequent `copy-number call` commands. Note the difference in the cluster file requirement based upon the version of DRAGEN Array used: * **Version 1.1+**: If using a CN model with a different cluster file, the software will provide a warning but will proceed with copy number calling. As a result, a user can choose to keep using the commercial CN model from Illumina in combination with custom updated EGT file in the PGx analysis. * **Version 1.0**: The same cluster file used for copy number training must be used to generate GTC files for copy number calling. Otherwise, the software will produce an error and exit. For reference, see the [Command Index](#command_index_1) for details of `copy-number train` command. To retrain the CN model file, 96 samples must be used at minimum with 90 of those samples passing QC defined as Log R Dev less than or equal to 0.2. It is recommended to train with at least 150 samples. A greater number of samples can be advantageous, but diminishing returns and longer computation times are seen after 3,000 samples. It is recommended to manually QC the training samples and remove samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 so only the highest quality samples are used in the training. The same samples used to create the new cluster file should be used to retrain the CN Model. To minimize batch effect in the training sample set, the samples should be analyzed in as few batches as possible and come from the same reagent lots. The copy-number train algorithm is designed with the assumption that the copy number distribution resembles the standard population distributions. This ensures the updated CN model file is representative of the normal populations in which it will be used to calculate copy number for key pharmacogenomic targets. ### Pharmacogenomic analysis for semi-custom arrays Semi-custom arrays add additional content or other pre-designed [Infinium booster content](https://www.illumina.com/science/consortia/human-consortia.html) to enhance the commercial array content. This additional content can be analyzed for [genotyping applications](/overview/our-features#section-dragen-array-genotyping) to obtain information on SNV and indel calls. For [pharmacogenomic applications](/overview/our-features#section-dragen-array-pgx-cnv-calling), PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. When designing a semi-custom array using a commercial Infinium PGx array backbone, such as the Global Diversity Array with enhanced PGx, it is important to retain all backbone content in the design as removing content could decrease the quality of result. Pharmacogenomic analysis for semi-custom arrays should be run using [DRAGEN Array Local](/product-guides/dragen-array-local-analysis). Because the PGx CNV calling and PGx star allele calling algorithms are only compatible with commercial product files (see [Applications](/overview/our-features)), to fully analyze semi-custom PGx beadchips some steps of the pipeline can be run twice; once with the semi-custom product files (to get complete semi-custom SNV VCF files), and once with the commercial product files (to get the PGx CNV VCF files, PGx Star Allele output, and metabolizer report). The semi-custom product files can be used via the Command-line interface in `genotype call`, `genotype gtc-to-vcf`, and used in GenomeStudio, i.e., 1. Use GenomeStudio 2.0 to prepare a custom cluster file for the semi-custom array, following guidance outlined in [Custom cluster\ file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). 2. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 3. Use the genotype call command to call all semi-custom genotypes and generate custom content GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --cluster-file /user/productfiles/semi_custom_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/semi_custom_gtcs` 4. Use the genotype gtc-to-vcf command to create custom content SNV VCF files from the custom content GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --csv-manifest /user/productfiles/semi_custom_manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/semi_custom_gtcs --output-folder /user/semi_custom_vcfs` 5. Perform [Quick Start](#section-quick-start) steps 1-6 using the **commercial** Infinium PGx array product files to obtain PGx CNV VCFs, star allele calls, and metabolizer status annotations. Keep the GTC files and SNV VCF files generated using the semi-custom product files in clearly labelled folders to distinguish them from the GTC and SNV VCF files generated using the commercial product files. Note that the GTC and SNV VCFs generated using the commercial product files will not contain genotypes for the semi-custom/add-on content. The GTC and SNV VCFs generated using the semi-custom product files cannot be used for downstream PGx analysis commands. # DRAGEN Array QC Report The DRAGEN Array QC Report is a **self-contained, interactive HTML dashboard** that helps you evaluate the quality of microarray datasets processed with the **DRAGEN Array** pipeline. It combines per-sample functional QC metrics, control-probe QC metrics (probe-level and summarized), and interactive visualizations to help you quickly: * Inspect per-sample metrics, for example, **Autosomal Call Rate**, **Log R Ratio Standard Deviation (LogRDev)**, **Sex estimate** * Detect assay or instrument issues using control-probe intensity patterns * Identify outliers, spatial artifacts, and batch effects using heatmaps and trend plots * Apply automated QC thresholds and export results for downstream review * Quickly calculate project-wide average call rate and LogRDev, and monitor trends across multiple datasets *** ## Analysis Workflow Use the following instructions to generate an interactive QC Report (HTML format) and QC Table (spreadsheet format). If you used a [Sample Sheet](/product-guides/input-files#section-sample-sheet) in the upstream workflow, user-defined metadata can be carried into the final QC report outputs. See [Command Index](/product-guides/dragen-array-local-analysis#command_index_1) for all command parameters. Methylation and genotyping workflow differences are highlighted below. | Workflow | Upstream command | Key inputs | Dataset folder contents for `dragena qc report` | When to use | | ----------------------- | ----------------------- | ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | | Methylation | `dragena qc call` | CSV manifest (`--csv-manifest`) + IDAT folder | `controls.raw_metrics.csv` + `controls.qc_metrics.csv` | Standard methylation QC-report workflow | | Genotyping, recommended | `dragena genotype call` | BPM manifest (`--bpm-manifest`) + cluster file (`--cluster-file`) + IDAT folder | `controls.raw_metrics.csv` + `controls.qc_metrics.csv` + `gt_sample_summary.csv` | Recommended when you want the richer HTML QC report experience, including functional QC, Autosomal Call Rate and LogRDev views, and sample heatmaps | | Genotyping, limited | `dragena qc call` | CSV manifest (`--csv-manifest`) + `--array-type genotyping` + IDAT folder | `controls.raw_metrics.csv` + `controls.qc_metrics.csv` | Use only for control-based genotyping QC inputs when `gt_sample_summary.csv` is not needed | ### Methylation workflow ```mermaid --- config: look: handDrawn theme: redux layout: elk --- flowchart TB classDef methylCmd fill:#CDEED8,stroke:#2F7D4A,color:#184A2B,stroke-width:2px; classDef input fill:#FFF0D9,stroke:#C56A1A,color:#7A3E00,stroke-width:1.5px; classDef option fill:#EFE7FF,stroke:#7B57C8,color:#4B2B88,stroke-width:1.5px; classDef output fill:#FFFFFF,stroke:#5E6B7A,color:#1F2937,stroke-width:1.25px; subgraph M_inputs["Inputs to dragena qc call"] direction TB M_csv["CSV manifest
(--csv-manifest)"]:::input M_array["--array-type
methylation"]:::option M_idat["IDAT folder
(--idat-folder)"]:::input M_sheet["Sample sheet
(--sample-sheet)
Optional"]:::input end M_qccall["dragena qc call"]:::methylCmd subgraph M_dataset_box["QC#8209;call output folder"] direction TB M_qcmetrics["controls.qc_metrics.csv"]:::output M_raw["controls.raw_metrics.csv"]:::output end M_data["--data"]:::option M_config["--config
Optional"]:::option M_outdir["--output-folder
Default: Current working directory"]:::option M_report["dragena qc report"]:::methylCmd M_html["QC report
(HTML)"]:::output M_table["QC table
(CSV or XLSX)"]:::output M_inputs --> M_qccall M_qccall --> M_dataset_box M_dataset_box -.-> M_data M_outdir --> M_report M_data --> M_report M_config --> M_report M_report --> M_html M_report --> M_table style M_inputs fill:#FFF9EE,stroke:#D6A04E,stroke-width:1px style M_dataset_box fill:#F9FBFD,stroke:#94A3B8,stroke-width:1.5px ``` ### Genotyping workflow For genotyping, `dragena genotype call` is the recommended upstream path because it produces `gt_sample_summary.csv`, which enables functional QC metrics and richer QC-report visualizations such as Autosomal Call Rate and LogRDev views, plus the sample heatmaps. #### Control-only path: `dragena qc call` Use this path when you only need control-based genotyping QC inputs. It uses a CSV manifest and does not generate `gt_sample_summary.csv`, so the QC report will not include functional QC metrics. If you have existing `gt_sample_summary.csv` files generated by older versions of DRAGEN Array prior to v1.4.0 release, you can combine those with the outputs from `dragena qc call` by specifying the existing output folder through the `--output-folder` option of `dragena qc call`, and then generate the full QC Report. ```mermaid --- config: look: handDrawn theme: redux layout: elk --- flowchart TB classDef methylCmd fill:#CDEED8,stroke:#2F7D4A,color:#184A2B,stroke-width:2px; classDef input fill:#FFF0D9,stroke:#C56A1A,color:#7A3E00,stroke-width:1.5px; classDef option fill:#EFE7FF,stroke:#7B57C8,color:#4B2B88,stroke-width:1.5px; classDef output fill:#FFFFFF,stroke:#5E6B7A,color:#1F2937,stroke-width:1.25px; subgraph M_inputs["Inputs to dragena qc call"] direction TB M_csv["CSV manifest
(--csv-manifest)"]:::input M_array["--array-type
genotyping"]:::option M_idat["IDAT folder
(--idat-folder)"]:::input M_sheet["Sample sheet
(--sample-sheet)
Optional"]:::input end M_qccall["dragena qc call"]:::methylCmd subgraph M_dataset_box["QC#8209;call output folder"] direction TB M_qcmetrics["controls.qc_metrics.csv"]:::output M_raw["controls.raw_metrics.csv"]:::output end subgraph G_dataset_box["Legacy genotype#8209;call output"] direction TB G_gt["gt_sample_summary.csv"]:::output end M_outdir["--output-folder
Default: Current working directory"]:::option M_config["--config
Optional"]:::option M_data["--data"]:::option M_report["dragena qc report"]:::methylCmd M_html["QC report
(HTML)"]:::output M_table["QC table
(CSV or XLSX)"]:::output M_inputs --> M_qccall M_qccall --> M_dataset_box M_dataset_box -.-> M_data G_dataset_box -.- M_dataset_box M_outdir --> M_report M_data --> M_report M_config --> M_report M_report --> M_html M_report --> M_table style M_inputs fill:#FFF9EE,stroke:#D6A04E,stroke-width:1px style M_dataset_box fill:#F9FBFD,stroke:#94A3B8,stroke-width:1.5px ``` #### Recommended path: `dragena genotype call` Use this path for most genotyping datasets. It uses the BPM manifest and cluster file, and the output folder already contains the control QC files plus `gt_sample_summary.csv` for functional QC reporting. ```mermaid --- config: look: handDrawn theme: redux layout: elk --- flowchart TB classDef genotypeCmd fill:#D9E9FF,stroke:#2A62B8,color:#15386B,stroke-width:2px; classDef input fill:#FFF0D9,stroke:#C56A1A,color:#7A3E00,stroke-width:1.5px; classDef option fill:#EFE7FF,stroke:#7B57C8,color:#4B2B88,stroke-width:1.5px; classDef output fill:#FFFFFF,stroke:#5E6B7A,color:#1F2937,stroke-width:1.25px; subgraph G_inputs["Inputs to dragena genotype call"] direction TB G_bpm["BPM manifest
(--bpm-manifest)"]:::input G_egt["Cluster file
(--cluster-file)"]:::input G_idat["IDAT folder
(--idat-folder)"]:::input G_sheet["Sample sheet
(--sample-sheet)
Optional
Source of metadata"]:::input end G_gencall["dragena genotype call"]:::genotypeCmd subgraph G_dataset_box["Genotype#8209;call output folder"] direction TB G_qcmetrics["controls.qc_metrics.csv"]:::output G_raw["controls.raw_metrics.csv"]:::output G_gt["gt_sample_summary.csv"]:::output end G_data["--data"]:::option G_config["--config
Optional"]:::option G_outdir["--output-folder
Default: Current working directory"]:::option G_report["dragena qc report"]:::genotypeCmd G_html["QC report
(HTML)"]:::output G_table["QC table
(CSV or XLSX)"]:::output G_inputs --> G_gencall G_gencall --> G_dataset_box G_dataset_box -.-> G_data G_outdir --> G_report G_data --> G_report G_config --> G_report G_report --> G_html G_report --> G_table style G_inputs fill:#FFF9EE,stroke:#D6A04E,stroke-width:1px style G_dataset_box fill:#F9FBFD,stroke:#94A3B8,stroke-width:1.5px ``` {% hint style="info" %} Use `dragena qc call` for methylation or for control-only genotyping inputs that rely on a CSV manifest. For genotyping, prefer `dragena genotype call` because it uses the BPM manifest plus cluster file and generates `gt_sample_summary.csv`, enabling functional QC metrics and the fuller QC-report feature set. In either workflow, `--data` points to the dataset folder, a parent folder, or a comma-separated dataset list for `dragena qc report`. {% endhint %} ### Instructions 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Alternatively, navigate to any working directory if the executable was added to your PATH. 2. Generate the QC input files using one of the following commands: * For genotyping datasets, use `dragena genotype call` in most cases. This is the recommended path when you want both control-based QC and functional QC metrics such as **Autosomal Call Rate**, **LogRDev**, and **Sex estimate**. ```bash dragena genotype call \ --bpm-manifest /user/productfiles/manifest.bpm \ --cluster-file /user/productfiles/clusterfile.egt \ --idat-folder /user/IDATs \ --output-folder /user/gtc ``` * Use `dragena qc call` if you want control-based QC inputs only, or if you have existing `gt_sample_summary.csv` file for the input IDATs, or if you are preparing QC-report inputs for methylation datasets. For genotyping, this command uses a CSV manifest and does not produce `gt_sample_summary.csv`. ```bash dragena qc call \ --array-type \ --csv-manifest /user/productfiles/manifest.csv \ --idat-folder /user/IDATs \ --output-folder /user/qc_metrics ``` 3. Prepare a dataset folder for `dragena qc report`. The dataset folder must contain at least the following files: * `controls.raw_metrics.csv` (required) * `controls.qc_metrics.csv` (required) * `gt_sample_summary.csv` (highly recommended) If you ran `dragena genotype call` for the dataset you want to review, you can use the genotype output folder directly as the dataset folder because it already contains `controls.raw_metrics.csv`, `controls.qc_metrics.csv`, and `gt_sample_summary.csv`. If you want to combine multiple datasets or use a parent folder that contains many dataset folders, see [Example input folder structures](#example-input-folder-structures) below. 4. Run `dragena qc report` using the dataset folder from Step 3 as input. The `--config` file is optional. See [Configuration file (optional)](#configuration-file-optional) below for template links and default-threshold behavior. ```bash dragena qc report \ --data /user/dataset_folder \ --config /user/config.yaml \ --output-folder /user/qc_report ``` If you want to use the genotype output folder directly and do not need a custom config file, the command can be as simple as: ```bash dragena qc report \ --data /user/gtc \ --output-folder /user/qc_report ``` If you want to combine multiple known dataset folders into one QC report, provide them as a comma-separated list after `--data`. If you also provide `--label`, use the same comma-separated style and supply one label per dataset. Do not add spaces between items; use commas only. ```bash dragena qc report \ --data /user/dataset_A,/user/dataset_B \ --label batch_A,batch_B \ --output-folder /user/qc_report ``` 5. Open the HTML report in your web browser: * `DRAGENArray_QC_Report_YYYY_MM_DD.html` {% hint style="info" %} If you generated the QC report in **BaseSpace Sequence Hub (BSSH)**, download the HTML report to your computer and then double-click the file to open it in your web browser. {% endhint %} {% hint style="warning" %} If the input data for `qc report` was generated while using a sample sheet, sample metadata such as sample names, processing information, and other sample-sheet values will be carried into the final QC report outputs when present in the input files. Review the QC report outputs before sharing them. If you need to remove some metadata before sharing, remove it from `gt_sample_summary.csv` and rerun `dragena qc report`. {% endhint %} {% hint style="info" %} If you provide a parent folder (for example `--data data/parent_folder`), the tool can automatically discover and process multiple dataset folders. See **Example input folder structures** below. {% endhint %} {% hint style="info" %} Local QC reports can combine multiple dataset folders into one report. This is the supported way to compare runs or batches in a single **Trend Analysis** view. Current cloud QC reports are generated for a single dataset, so their Trend Analysis view summarizes that dataset only rather than comparing multiple runs. {% endhint %} *** ## Command-line options The `dragena qc report` command supports the following options to control output location, format, and QC thresholds. {% hint style="warning" %} CLI options are case-sensitive and must be entered exactly as shown. In particular, the threshold flags use mixed case: `--threshold-callRate` and `--threshold-logRdev`. {% endhint %} ### Output options #### `--output-folder` Directory path where output files are written. * **Default:** Current working directory * Applies to both the HTML report and QC tables Example: ```bash dragena qc report \ --data project_folder \ --output-folder output/qc_report ``` #### `--output-format ` Use this option to choose whether the per-sample QC table is written as an `xlsx` workbook or a `csv` file. The default output format is `xlsx`. When the QC table is written as `xlsx`, the workbook includes conditional formatting based on the thresholds that were applied when the report was generated, and it includes a **Thresholds** worksheet that records those threshold settings. Those applied thresholds can come from built-in defaults, a YAML file provided with [`--config`](#configuration-file-optional), or command-line overrides described in [QC threshold overrides (CLI)](#qc-threshold-overrides-cli). When the QC table is written as `csv`, the output contains values only. It does not include workbook formatting or additional worksheets. Example: ```bash dragena qc report \ --data project_folder \ --output-format csv ``` ### QC threshold overrides (CLI) These options override functional QC thresholds directly from the command line. They take precedence over built-in defaults **and** any corresponding values provided via `--config`. For example, if `--config` sets `callRate: 0.90` but you run with `--threshold-callRate 0.95`, the report uses **0.95**. #### `--threshold-callRate ` Override the Autosomal Call Rate threshold. * **Default:** `0.98` * Samples with Autosomal Call Rate **below** this value are marked as FAIL. Example: ```bash dragena qc report \ --data project_folder \ --threshold-callRate 0.95 ``` #### `--threshold-logRdev ` Override the Log R Ratio Standard Deviation (LogRDev) threshold. * **Default:** `0.20` * Samples with LogRDev **above** this value are marked as FAIL. Example: ```bash dragena qc report \ --data project_folder \ --threshold-logRdev 0.25 ``` {% hint style="info" %} For complex or reproducible QC configurations, use a YAML configuration file via `--config`. CLI threshold options are most useful for quick exploratory runs. {% endhint %} {% hint style="info" %} **Precedence (highest to lowest):** 1. Command-line threshold flags (e.g., `--threshold-callRate`, `--threshold-logRdev`) 2. YAML config file values provided via `--config` 3. Built-in defaults {% endhint %} *** ## Input files Each dataset folder must include the required QC metric files below. Optional files enable additional features (for example, functional genotyping QC metrics) or override defaults (configuration). | File name | Description | Required | | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- | | `controls.raw_metrics.csv` | Probe-level control intensities used to compute control QC metrics. | Yes | | `controls.qc_metrics.csv` | Per-sample summarized QC metrics derived from control probes. | Yes | | `gt_sample_summary.csv` | Per-sample genotyping metrics (including Autosomal Call Rate, LogRDev, Sex estimate). Strongly recommended for functional QC evaluation. | No (strongly recommended) | | `config.yaml` | Overrides QC thresholds and report behavior when provided via `--config`. | No | {% hint style="info" %} See [**Metadata propagation (gt\_sample\_summary)**](#metadata-propagation-gt_sample_summary) for details on how user-defined columns are exposed in the report. {% endhint %} {% hint style="info" %} Only a subset of control probe metrics from `controls.raw_metrics.csv` are propagated to the merged QC outputs.\ See [**Control probe propagation from `controls.raw_metrics.csv`**](#control-probe-propagation-from-controls.raw_metrics.csv) for details. {% endhint %} ### Example input files #### `controls.qc_metrics.csv` ``` SampleId,ImagingDate,SamplePlate,SampleWell,ScannerId,ScannerVersion,StainingGreenQC,StainingRedQC,ExtensionGreenQC,ExtensionRedQC,HybridizationHighMediumQC,HybridizationMediumLowQC,TargetRemovalIQC,StringencyQC,NonSpecificBindingGreenQC,NonSpecificBindingRedQC,NonPolymorphicGreenQC,NonPolymorphicRedQC 205930510001_R01C01,11/19/2021 12:39:31 PM,WG0587702-DNA,A06,N0782,4.3.0.934,18.989004,15.226524,17.069284,7.0317426,1.4189211,2.8047338,3.2466092,1.7800437,3.1761158,3.1761158,0.07639844,0.11531731 ... ``` #### `controls.raw_metrics.csv` ``` SampleId,Category,Beadtype,Control,Color,GreenIntensity,RedIntensity 205930510001_R01C01,Extension,11603365,Extension (G),Blue,38679,4168 205930510001_R01C01,Extension,12613307,Extension (C),Green,39708,4505 ... ``` #### `gt_sample_summary.csv` ``` Sample ID,Sample Name,Sample Folder,Autosomal Call Rate,Call Rate,Log R Ratio Std Dev,Sex Estimate,TGA_Ctrl_5716 Norm R,SentrixBarcode_A,SentrixPosition_A 205930510001_R01C01,205930510001_R01C01,GSAPGx/205930510001,0.95813435,0.9528165,0.23342112,U,3.1297944,205930510001,R01C01 ... ``` *** ## Metadata propagation (gt\_sample\_summary) When the upstream workflow uses a sample sheet, sample-sheet metadata columns are first propagated into `gt_sample_summary.csv`. During QC report generation, all columns present in `gt_sample_summary.csv` are then propagated into the report sample metadata and into the merged QC table outputs. In the merged QC `xlsx` and `csv` outputs, propagated metadata columns from `gt_sample_summary.csv` are included and ordered alphabetically. Not every propagated metadata field is offered in the report UI for **Color by** or **Facet by**. Those controls only include fields that behave like useful categorical groupings. In particular, fields with only one unique value are not offered, and fields with more than 20 unique values are also not offered for coloring or faceting. Metadata fields that are not eligible for **Color by** or **Facet by** can still appear in hover text when space allows. However, some metadata may be omitted from hover tooltips when there is not enough room to display all fields. *** ## Control probe propagation from `controls.raw_metrics.csv` Not every raw control-probe row present in `controls.raw_metrics.csv` is carried forward into the QC report outputs. Here, **not propagated** means that specific raw probe entries from `controls.raw_metrics.csv` are excluded from the merged downstream raw-probe data used by the report and QC table. The report focuses on raw control probes that support actionable review and on summarized QC metrics used for downstream evaluation. As a result, some raw probe categories are intentionally excluded during merge and summarization. ### Included versus excluded raw probe names The key distinction is between the **standard raw probe names used by the report** and the **additional raw probe names that may appear in some input files but are not propagated downstream**. | Control category | Raw probe names propagated downstream | Raw probe names excluded from downstream raw-probe outputs | | ------------------- | --------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | **NON-POLYMORPHIC** | Standard non-polymorphic probe rows such as `NP (G)` and `NP (T)` | Additional methylation-platform rows `NP (G) 1`, `NP (G) 2`, `NP (G) 3`, `NP (G) 4`, `NP (G) 5` | | **STAINING** | Standard staining probe rows `Biotin (Bkg)`, `Biotin (High)`, `DNP (Bkg)`, `DNP (High)` | Additional methylation-platform rows `Biotin(5K)` and `DNP(20K)` | ### Excluded raw control-probe entries The following raw control-probe categories or probe names are excluded from the merged downstream raw-probe outputs: * **NEGATIVE** * **NORM** * **Additional NON-POLYMORPHIC NP (G) probe rows found on some methylation platforms** * NP (G) 1 * NP (G) 2 * NP (G) 3 * NP (G) 4 * NP (G) 5 * **Additional STAINING probe rows found on some methylation platforms** * DNP(20K) * Biotin(5K) These probe rows can still be present in the original `controls.raw_metrics.csv` input file for completeness, but they are not propagated as downstream raw-probe entries in the QC report outputs. In other words, users may still see summarized **Staining** or **Non-Polymorphic** control metrics in the report, and may also see the standard raw probe rows used for those metrics, even though the extra raw probe rows listed above are excluded. ## Configuration file (optional) Provide a YAML configuration file to override the report’s QC thresholds. You can start from one of the Illumina template config files below or supply your own YAML file. Set a value to `null` to **disable** a check, or set a numeric value to **enable** it (for example `tgaControl: 1.0`). The template config files below are suggested starting points and should be adjusted based on sample type, platform, and lab-specific performance. Illumina template config files are available below: {% file src="/files/wqHVYLDo1m2wx2zQ9d2h" %} {% file src="/files/GBZegmeDYoPinNXmvoHx" %} {% file src="/files/n2MNa2RrPWQbssTS1ol4" %} {% file src="/files/Zp6CKGOL4NkdsfQl71Pq" %} Suggested starting points by product and assay chemistry: * Genotyping assays that use **Infinium non-EX** reagents/chemistry: start with `config_genotyping.yaml`. * Genotyping assays that use **Infinium EX** reagents/chemistry: start with `config_genotyping_EX.yaml`. Example products include **GSA: Infinium Global Screening Array-48 v4.0 Kit**, **GSA-ePGx: Infinium Global Screening Array with Enhanced PGx-48 v4.0**, **GCRA: Infinium Global Clinical Research Array-24 v1.0 Kit**, **GCRA-ePGx: Infinium Global Clinical Research Array with Enhanced PGx-24 v1.0 Kit**, and other customized **Infinium EX** products. * Methylation assays that use **MethylationEPIC** reagents/chemistry: start with `config_methylation_EPIC.yaml`. * Methylation assays that use **Infinium EX** reagents/chemistry: start with `config_methylation_MSA.yaml`. Example products include **Infinium Methylation Screening Array-48 Kit** and **Infinium Methyl EX iSelect Custom BeadChip (24/48 formats)**. If you are using a customized product and are unsure which assay chemistry it uses, contact [Illumina Technical Support](mailto:techsupport@illumina.com) before selecting a QC configuration file. You can edit the QC config YAML using a plain‑text editor (for example, Notepad). {% hint style="warning" %} When saving a config file from Windows Notepad, verify that the file extension remains `.yaml` or `.yml` and was not changed to `.yaml.txt` or `.yml.txt`. If needed, use **Save As**, set **Save as type** to **All Files**, and enter a filename such as `config.yaml`. {% endhint %} For guidance on adjusting DNA methylation QC thresholds, see the following [Illumina documentation](https://help.dragenarray.illumina.com/product-guides/dragen-array-cloud-analysis/dragen-array-methylation-qc#methylation-qc-threshold-adjustment). ### Apply a configuration file Use the `--config` CLI flag to apply the selected configuration file: ```bash dragena qc report \ --data data/parent_folder \ --config .yaml \ --output-folder output/parent_folder_report ``` {% hint style="info" %} If `--config` is omitted, the report uses built-in defaults. {% endhint %} ### How suggested thresholds were derived The suggested thresholds in the example configuration files were derived empirically from an internal review of more than 10 datasets spanning both expected good-quality samples and failed samples. For each metric, Illumina evaluated the observed distribution of values, including the center and spread of the apparent null or background distribution, and used that information to choose practical starting thresholds for routine QC review. These values are intended as **suggested starting points**, not universal acceptance criteria. Users should review and revise thresholds for their own assay, sample type, laboratory workflow, scanner settings, bisulfite conversion method, FFPE usage, and historical performance. If a dataset repeatedly shows a consistent offset for one of these control metrics while other QC evidence remains acceptable, review the threshold in context rather than treating the default value as absolute. **Note:** For `config_genotyping.yaml`, those thresholds were evaluated only using LCG and HTS datasets. {% hint style="info" %} For control metrics, configured thresholds are primarily used to flag samples for review in the QC report. They are recommended operating cutoffs, not assay-independent pass/fail truths. {% endhint %} ### Generic example YAML structure (illustrative only) The two YAML blocks below are **generic illustrative examples**, not chemistry-specific recommended starting points. * The genotyping example below is not specific to **Infinium EX** or **LCG/HTS** chemistry. * The methylation example below is not specific to **Infinium EX/MSA** or **MethylationEPIC** chemistry. * For chemistry-specific starting points, use the recommended template files listed above. #### Generic genotyping YAML example ```yaml QC: Report: # Core sample metrics callRate: 0.98 # Minimum acceptable call rate. logRDev: 0.15 # Maximum acceptable Log R Ratio SD. # Shared control probe metrics stainingGreen: 5 # Minimum Biotin High / Background ratio. stainingRed: 5 # Minimum DNP High / Background ratio. extensionGreen: 5 # Minimum green-channel extension ratio. extensionRed: 5 # Minimum red-channel extension ratio. targetRemovalI: 5 # Minimum target removal ratio for Control I. hybGreenHighMed: 1.25 # Minimum hybridization High / Medium ratio. hybGreenMediumLow: 1.25 # Minimum hybridization Medium / Low ratio. npGreen: 5 # Minimum non-polymorphic green ratio. npRed: 5 # Minimum non-polymorphic red ratio. restoration: null # Use 1 when the FFPE Restore kit is used. # Genotyping-specific metrics stringency: 1.5 # Minimum stringency ratio. nonSpecificBindingGreen: 5 # Minimum nonspecific binding green ratio. nonSpecificBindingRed: 5 # Minimum nonspecific binding red ratio. tgaControl: 1 # Minimum TGA_Ctrl_5716 normalized R value. ``` #### Generic DNA methylation YAML example ```yaml QC: Report: # Staining controls stainingGreen: 5 # Minimum Biotin High / Background ratio. stainingRed: 5 # Minimum DNP High / Background ratio. # Extension controls extensionGreen: 5 # Minimum green-channel extension ratio. extensionRed: 5 # Minimum red-channel extension ratio. # Hybridization controls hybGreenHighMed: 1 # Minimum hybridization High / Medium ratio. hybGreenMediumLow: 1 # Minimum hybridization Medium / Low ratio. # Restoration control restoration: null # Use 1 when the FFPE Restore kit is used. # Target removal controls targetRemovalI: 1 # Minimum target removal ratio for Control I. targetRemovalII: 1 # Minimum target removal ratio for Control II. # Bisulfite conversion controls bisulfiteConversionIGreen: 1 # Minimum Type I green conversion ratio. bisulfiteConversionIGreenBg: 0.5 # Minimum Type I green background ratio. bisulfiteConversionIRed: 1 # Minimum Type I red conversion ratio. bisulfiteConversionIRedBg: 0.5 # Minimum Type I red background ratio. bisulfiteConversionII: 0.5 # Minimum Type II conversion ratio. bisulfiteConversionIIBg: 0.5 # Minimum Type II background ratio. # Specificity controls specificityIGreen: 1 # Minimum Type I green specificity ratio. specificityIRed: 1 # Minimum Type I red specificity ratio. specificityII: 1 # Minimum Type II specificity ratio. specificityIIBg: 1 # Minimum Type II background specificity ratio. # Non-polymorphic controls npGreen: 2.5 # Minimum non-polymorphic green ratio. npRed: 3 # Minimum non-polymorphic red ratio. ``` *** ## Output files | File name | Description | | --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `DRAGENArray_QC_Report_YYYY_MM_DD.html` | Self-contained interactive HTML report intended to be distributable and viewable offline in a web browser. The report includes dashboards such as Control Dashboard, Automated QC, Sample QC Heatmaps, Trend Analysis, and a QC Metric Config menu for threshold customization. | | `DRAGENArray_QC_table_YYYY_MM_DD.` | Per-sample QC table (one row per sample) containing functional metrics (when available), derived control metrics, and selected raw control-probe intensities. The file extension depends on `--output-format` (`xlsx` or `csv`). | *** ## QC evaluation criteria ### Functional QC (per sample) Functional QC evaluates overall genotyping performance of each sample: * **Autosomal Call Rate**\ Fraction of autosomal probes successfully called for a sample. Higher values indicate better performance. * **Log R Ratio Standard Deviation (LogRDev)**\ Measures signal noise across probes. Lower values indicate more stable intensity measurements. #### Functional QC status (PASS/FAIL) A sample’s functional QC status is determined by comparing its metrics to configured thresholds: * **PASS** * `Autosomal Call Rate` ≥ threshold * `LogRDev` ≤ threshold * **FAIL** * One or both metrics fall outside thresholds {% hint style="info" %} Functional QC requires a per-sample metric file (for example `gt_sample_summary.csv`). If that file is not provided, functional QC is unavailable. It does not support methylation at this time. For methylation QC please refer to [DRAGEN Array - Methylation QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc) on cloud. {% endhint %} *** ### Control-based QC (per sample) Control-based QC evaluates whether array chemistry and processing performed as expected. Control metrics originate from: * `controls.raw_metrics.csv` — raw, probe-level control intensities * `controls.qc_metrics.csv` — summarized per-sample control QC values Common control categories include: * Staining * Extension * Hybridization (High/Medium/Low) * Non-polymorphic * Non-specific binding * Target removal * Stringency * Restoration (when applicable) * Bisulfite conversion controls (methylation arrays) * Specificity (methylation arrays) For more background on interpreting Infinium controls, see: [Evaluation of Infinium Genotyping Assay Controls Training Guide](https://support.illumina.com/content/dam/illumina-support/courses/eval-inf-controls/story_content/external_files/Infinium_Controls_Training_Guide.pdf) #### Control QC status & flags Each control metric is compared to its configured threshold. When a value is outside the acceptable range, the sample receives a **flag** indicating the affected control and channel. *** ### Interpreting combined QC results The report shows both functional QC and control-based QC for every sample: * A sample may **PASS functional QC** but still receive **control warnings**. * Multiple or severe control failures may indicate assay-related issues that impact downstream results. This combined view can help distinguish: * **Biological failures** (for example, degraded DNA) * **Technical failures** (for example, staining or hybridization issues) *** ## HTML QC report The HTML report is organized into dashboards designed for routine QC review and deeper troubleshooting. ### Control dashboard The Control Dashboard provides interactive visualizations of raw control-probe intensities to help identify instrument and assay issues. These plots help you review whether control signals such as staining, extension, hybridization, and non-polymorphic probes behave as expected across samples. You can: * Select samples directly from plots to highlight those same samples across related plots in both the Control Dashboard and the Automated QC dashboard * Hover to see details (for example, sample ID, barcode, position, autosomal call rate when available) * Explore distributions and trends to detect outliers or systematic shifts * Zoom and filter for focused investigation To clear a plot-based selection, double-click in the plot area. This restores the full sample set in the Automated QC table and removes the cross-plot highlighting. ![Control dashboard](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-9bd70b3421607b494be8a0e3fb2d395150861c2a%2Fcontrol_dash_board.png?alt=media) #### Chart toolbar reference Each plot includes a toolbar for zooming, panning, selection, autoscaling, and exporting. ![Plotly toolbar controls](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-205b9d102bd714a0a30b30c05ec944b53e28db3c%2Fplotly_controls.png?alt=media) *** ### Automated QC The Automated QC dashboard provides a consolidated, objective view of sample-level QC by applying QC rules and thresholds across all samples. Use it to: * Quickly assess pass/fail status * Identify samples and metrics outside thresholds * Support decisions for sample inclusion, reprocessing, or follow-up analysis #### Functional QC Status Histogram This histogram shows the number and percentage of samples in the dataset that are classified as **PASS** or **FAIL** for functional QC. Functional QC status is determined from the sample-level functional metrics, using the Autosomal Call Rate and LogRDev thresholds currently applied in the report. Those thresholds can come from the defaults, a YAML config file, command-line overrides, or the **QC Metric Config** settings described in [Updating thresholds in the HTML report](#updating-thresholds-in-the-html-report). A sample is counted as **FAIL** if it fails any individual functional QC metric that is currently enabled. Otherwise, the sample is counted as **PASS**. #### Control-Based QC Status Histogram This histogram shows the number and percentage of samples in the dataset that are **FLAGGED** or **CLEAR** for control-based QC. Control-based QC status is determined from the control QC metric thresholds currently applied in the report. A sample is counted as **FLAGGED** if any individual control QC metric is outside its applied threshold. Samples without any active control-based QC flags are counted as **CLEAR**. #### Sample QC table The Sample QC table provides a per-sample summary of the same QC decisions shown in the histograms, along with the underlying metrics used to support review. It includes: * Functional QC status (PASS/FAIL) when functional metrics are available * Control QC flags and annotations that highlight values outside thresholds * Sorting and filtering to focus on failing samples or specific metrics The table can be exported in either `xlsx` or `csv` format. The `xlsx` output preserves conditional coloring, while the `csv` output contains values only. For more detail about the table output formats, see [`--output-format `](#output-format-csvxlsx). ![Automated QC table](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-90483ee3ef5425879a83b21a0a9dafcfb1d743f1%2Fautomated_qc_table.png?alt=media) #### QC metric plots Supporting plots in the Automated QC dashboard complement the table by showing **per-sample QC metric scatter plots**. In these plots, the x-axis represents samples in the current dataset view, and the y-axis represents one derived QC metric. Users can sort the sample order by **Autosomal Call Rate**, **Log R Dev**, any propagated user-provided metadata field, or any derived QC metric available in the report. Users can also color samples by eligible propagated metadata fields to help reveal group-specific patterns or batch effects. For genotyping arrays, the initial point colors are based on **functional QC status**: **PASS** or **FAIL** determined from the applied **Autosomal Call Rate** and **Log R Dev** thresholds. Plotted metrics can include control-derived metrics such as **Staining**, **Extension**, **Hybridization**, **Target Removal**, **Nonpolymorphic**, **Stringency**, **Specificity**, and **Bisulfite Conversion**. For example, **Staining Red** is calculated as `DNP High Red / DNP Bkg Red`, and **Stringency** is calculated as `Stringency PM (Red) / Stringency MM (Red)`. Each Automated QC scatter plot also includes help text with the formula used to derive the selected metric. Most control metrics are constructed as ratios that compare expected signal against background or against an opposing control signal. That makes them more stable for QC review than raw intensities alone because even if absolute intensities shift between scanners or runs, the signal-versus-background relationship is expected to remain relatively consistent. When a QC threshold is set for a plotted metric, the scatter plot shows that cutoff as a dashed threshold reference line. These thresholds can come from built-in defaults, a YAML config file, command-line overrides, or values applied in the **QC Metric Config** menu. For more detail on how methylation QC control metrics and their recommended starting thresholds are defined, see [Methylation Sample QC Summary Files](https://github.com/illumina-swi/dragen-array-docs/tree/DAv1.4/docs/output-files.md#methyl_qc_report) and [Methylation QC Threshold Adjustment](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc#section-methylation-qc-threshold-adjustment). These plots also participate in linked selection. When you select samples in one scatter plot, those same samples are highlighted in the other scatter plots across the Automated QC and Control Dashboard views, and the Automated QC table is filtered to show only the selected samples. To clear the selection, double-click in the plot area. These plots help you: * See which individual samples fall outside a QC threshold * Compare sample-to-sample variation for one metric at a time * Relate noisy or failing samples to other QC views in the report In the example figure below, the bottom plot shows a group of samples below the **Stringency** threshold line, and those points are also colored as functional QC **FAIL**. Other samples in the same figure are also colored as **FAIL** even though they do not fall below the Stringency cutoff, which suggests that other QC metrics may be contributing to the failure; review the other QC metric plots and the table to identify the control probes or sample-level metrics associated with those samples. ![Automated QC scatter plots](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-ab00903d584e23c77d0ae1b6294351539be247c8%2Fautomated_qc_scatter_plots.png?alt=media) *** ### Sample QC heatmaps The Sample QC Heatmaps dashboard provides a spatial view of sample-level QC metrics across chips or plates to help detect spatial artifacts and localized issues. #### Overview * Each cell represents a sample * Cell color reflects a selected QC metric (for example Autosomal Call Rate, LogRDev) * Hover shows sample identifiers and metric values Plate information may be derived from: * IDAT metadata, or * A user-provided sample sheet (columns: `Sample_Plate` and `Sample_Well`) {% hint style="info" %} If both sources provide plate information, the report uses the user-provided sample sheet values. {% endhint %} ![Heatmap by chip](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-8ec0c44ce8899121079b4811abfcb3c56c947369%2FHeatmap_by_chip.png?alt=media) ![Heatmap by plate](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-153505c2ed2f9de39a0f7d64a8901c1800bcae4e%2FHeatmap_by_plate.png?alt=media) *** ### Trend analysis The Trend Analysis dashboard provides a high-level view of QC trends across multiple datasets, runs, batches, or instruments. For local analysis, this cross-dataset summary is available when you generate one report from multiple dataset folders. If you generate the report from a single dataset folder, the dashboard still appears but summarizes that one dataset only. Current cloud QC reports also operate on a single dataset at a time. Use it to: * Monitor changes over runs and scan dates * Detect drift, batch effects, or instrument-specific anomalies * Compare QC performance between datasets or groups of chips #### Summary table The summary table aggregates QC metrics at the dataset level (for example): * Number of samples / chips * Scan date range * Autosomal Call rate statistics (min/mean/standard deviation, counts above/below threshold) * LogRDev statistics (mean/standard deviation, counts above threshold) * TGA control statistics for PGx Genotyping datasets * Sex prediction summary (number of males, females and unknowns) ![Trend analysis table](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-5784fe29732b65391eed5816d323c426565aa21a%2FTrend_analysis_table.png?alt=media) #### Summary plots Summary plots provide visual comparisons across datasets or barcodes, such as: * Autosomal Call rate distributions * LogRDev box plots * Sample count plots * Sex estimate distributions For example, box-and-whisker plots in Trend Analysis summarize how a metric is distributed within each dataset so you can compare center, spread, and outliers across runs. In a box plot, the box represents the middle 50% of the data distribution, the center line marks the median, and the whiskers extend to the smallest and largest non-outlier values shown for that dataset. When a threshold is configured for a plotted metric, the plot also shows the applied cutoff as a dashed reference line. The following schematic shows the main parts of a box-and-whisker plot: ![Box-and-whisker plot anatomy: whiskers extend from the box to the smallest and largest non-outlier values. The box shows the interquartile range (Q1-Q3), which is the middle 50% of the data.](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-3907991e5920484cc5b4d4ea6ed2aedba047a7ee%2Fboxplot_anatomy.svg?alt=media) ![Trend analysis plots](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-d8480b03f04b51279a3bb93143f9f274d7aa9ba2%2FTrend_analysis_plots.png?alt=media) *** ## Updating thresholds in the HTML report You can update QC threshold cutoffs directly in the HTML report: 1. Open **QC Metric Config** (⚙). 2. Enter new numeric values for metrics you want to enforce. * Leave a field blank (or set it to `null` in YAML) to **disable** that check. 3. Click **Apply Thresholds**. ### Buttons * **Apply Thresholds**: Recalculates pass/fail and refreshes visuals, including threshold reference lines in plots, using the current values. * **Download Thresholds**: Exports the currently applied thresholds as a YAML file. * **Upload Thresholds**: Imports a YAML file and fills the threshold fields. {% hint style="warning" %} Changes made in the HTML report are **session-only** and apply only to the currently opened report file. **Download Thresholds** exports the currently applied thresholds as a **YAML file**. To reuse the same thresholds in a future run, pass that YAML file to the `qc report` command (for example, via `--config`) when generating a new report. **Upload Thresholds** loads a YAML file back into the UI fields for the current report. {% endhint %} ![QC thresholds config](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-d04cff85ce4ba02b9c524d9ae379db159cd0b276%2Fqc_thresholds.png?alt=media) *** ## Interactive features ### Filter table samples using scatter plot selection Selecting samples in a scatter plot filters the corresponding rows in the Automated QC table. The same selected samples are also highlighted in related scatter plots across the Automated QC and Control Dashboard views. To clear this plot-based filtering and cross-plot highlighting, return to the plot and double-click in the plot area to remove the selection. ![Sample selection filters table](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-b9b5549fcc25112218ae026ce571fd9f9a5551a3%2Fsample_selection_filter_table.gif?alt=media) ### Highlight samples in scatter plots by selecting from the table Selecting rows in the table highlights the corresponding points in scatter plots. To clear this table-based highlighting, click **Select None** in the bottom-right corner of the table. ![Table selection highlights samples](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-77433c64bb421d96ed0eb3cbce620c9bece45c45%2Fsample_selection.gif?alt=media) When exporting the table, any active row selection is preserved. If you click **Excel** or **CSV** while rows are selected, only those selected rows are exported. To export all samples, click **Select None** before exporting. ### Table filtering and sorting The interactive table supports: * Showing/hiding columns * Sorting by any metric * Filtering by search or criteria * Combining table filtering with plot selection for cross-linked exploration *** ## Performance & dataset size limits For performance reasons, large datasets may use a tabbed layout to keep the browser responsive. * **More than 1,000 samples in a single dataset**: control and automated-QC plots are arranged into tabs (interactive features remain available). * **More than 12,000 samples in a single dataset**: scatter plots are disabled and replaced with a notice. Example tabbed layout: ![Tabbed Control dashboard](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-77affb1c369d3d2af3b47afe3f23d1fe7c925c1b%2Flarger_dataset_control_dashboard.png?alt=media) ![Tabbed Automated QC](https://2250409810-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvpICEpxwEG3Hnd1kFkeJ%2Fuploads%2Fgit-blob-118fe22b8b9ff54ca551d4387f65c37498606bf8%2Flarger_dataset_automated_qc.png?alt=media) ### How to visualize very large projects If your project exceeds the scatter-plot limit, consider: * Filtering input by run date/batch/folder and generating separate reports * Splitting input into logical batches (for example per-run or per-center) When a QC Report html contains more than 20,000 samples across datasets, the loading can be slowed down. *** ## Example input folder structures ### Single dataset folder In this example, `project_folder/` is one dataset folder that contains the required input files for a single QC report. ``` project_folder/ ├── controls.qc_metrics.csv ├── controls.raw_metrics.csv ├── gt_sample_summary.csv ``` ```bash dragena qc report \ --data project_folder/ \ --label pgx2025 \ --output-folder output/single_project ``` *** ### Multiple dataset folders Use this mode on local analysis when you want to combine multiple runs or batches into a single QC report and compare them in **Trend Analysis**. In this mode, `--data` is a comma-separated list of dataset folder paths. Each path must point directly to a folder that contains the required input files. In the example below, `dataset_A/` and `dataset_B/` are example folder paths. When writing a list for `--data`, do not include spaces before or after the commas. If you use `--label`, write the labels the same way: one comma-separated item per dataset, with no spaces between items. ``` dataset_A/ ├── controls.qc_metrics.csv ├── controls.raw_metrics.csv ├── gt_sample_summary.csv dataset_B/ ├── controls.qc_metrics.csv ├── controls.raw_metrics.csv ├── gt_sample_summary.csv ``` The command below tells `dragena qc report` to load both dataset folders into one report: ```bash dragena qc report \ --data dataset_A,dataset_B \ --label pgx2025,pgx2024 \ --output-folder output/multiple_datasets_with_labels_provided ``` Use this form when you already know the exact dataset folders you want to include. *** ### Parent folder containing multiple dataset folders Use this mode when many dataset folders are organized under one parent folder and you want the local QC report to discover them automatically. This is different from the previous example only in how `--data` is used: * In the previous example, `--data` lists each dataset folder path explicitly. * In this example, `--data` points to one parent folder path, and the tool searches below that parent folder to find valid dataset folders automatically. Some users describe this as a "recursive" search. In practice, it means the tool starts from the parent folder and looks through its subfolders for dataset folders that contain the required QC files. ``` project_folder/ ├── dataset_A/ │ ├── controls.qc_metrics.csv │ ├── controls.raw_metrics.csv │ ├── gt_sample_summary.csv ├── dataset_B/ │ ├── controls.qc_metrics.csv │ ├── controls.raw_metrics.csv │ ├── gt_sample_summary.csv ├── dataset_C/ │ └── ... ``` The command below tells `dragena qc report` to start from the parent folder `project_folder` and automatically discover dataset folders under it: ```bash dragena qc report \ --data project_folder \ --output-folder output/parent_folder_report ``` If you do not provide `--label`, the report assigns a dataset label based on each detected dataset folder name (for example `dataset_A`, `dataset_B`). To compare multiple runs on local, use one of these two approaches: * Use `--data dataset_A,dataset_B,...` when you want to list the dataset folder paths yourself. * Use `--data project_folder` when `project_folder` is a parent folder that contains many dataset folders and you want the tool to discover them automatically. **Parent-folder discovery labels and ordering** * If you supply `--label`, labels are assigned in the order datasets are discovered. * If multiple datasets share the same folder name, a numeric suffix is appended (for example `datasetA`, `datasetA_2`) to ensure uniqueness. *** ## Troubleshooting & FAQ
Missing samples **Possible cause:** Sample IDs do not match across input files.\ **Recommended action:** Align Sample IDs across `controls.*` and (if provided) `gt_sample_summary.csv`.
Unexpected QC failures **Possible cause:** Thresholds are too strict for your dataset or application.\ **Recommended action:** Review and adjust thresholds using `--config` or the **QC Metric Config** menu in the HTML report.
Scatter plots are disabled Scatter plots are disabled when the dataset exceeds **12,000 samples**.\ **Recommended action:** Filter or split the dataset and generate separate reports.
CSV parsing errors **Possible cause:** Quoting/encoding issues.\ **Recommended action:** Ensure UTF-8 encoding and valid comma delimiters.
Report is extremely slow to load or does not finish loading **Possible cause:**\ Very large datasets (for example, >50,000 samples) generate large input files that require significant client‑side processing. In the current implementation, extremely large datasets (for example, \~100,000 samples) may exceed browser or memory limits and fail to load completely. **Recommended action:**\ Filter or split the dataset into smaller subsets and generate separate QC reports for each subset.
What happens if the input folder contains both genotyping and methylation datasets? If `dragena qc report` detects both genotyping and methylation dataset folders within the same `--data` input set, report generation stops with an error. In this guide, **mixed dataset types** specifically means this combination. **Example:** A parent folder passed to `--data` contains one dataset folder with methylation `qc call` outputs and two dataset folders with genotyping `qc call` outputs. This folder structure triggers the mixed-dataset error: ``` datasets/ ├── dataset1_methylation/ │ ├── controls.qc_metrics.csv │ └── controls.raw_metrics.csv ├── dataset2_genotyping_pgx/ │ ├── controls.qc_metrics.csv │ ├── controls.raw_metrics.csv │ └── gt_sample_summary.csv └── dataset3_genotyping_non_pgx/ ├── controls.qc_metrics.csv ├── controls.raw_metrics.csv └── gt_sample_summary.csv ``` ```bash dragena qc report --data datasets ``` **Recommended action:** Generate separate QC reports for genotyping and methylation inputs. For example, separate them into different parent folders: ``` reports_input/ ├── methylation/ │ └── dataset1_methylation/ │ ├── controls.qc_metrics.csv │ └── controls.raw_metrics.csv └── genotyping/ ├── dataset2_genotyping_pgx/ │ ├── controls.qc_metrics.csv │ ├── controls.raw_metrics.csv │ └── gt_sample_summary.csv └── dataset3_genotyping_non_pgx/ ├── controls.qc_metrics.csv ├── controls.raw_metrics.csv └── gt_sample_summary.csv ``` ```bash dragena qc report --data reports_input/methylation dragena qc report --data reports_input/genotyping ```
What happens if the input folder contains both PGx and non-PGx genotyping datasets? This combination is supported. The report is generated as long as each dataset folder contains the required input files. PGx datasets can include **TGA control** information, while non-PGx datasets do not. Other shared genotyping QC outputs are generated normally. For example, if the input contains one PGx dataset folder and one non-PGx genotyping dataset folder, the report still runs as a single genotyping report. TGA-related values are populated only for the PGx dataset.
What happens if the input folder contains both legacy and EX genotyping chips? This combination is supported and does not trigger the mixed-dataset error. There is no special difference in report generation or QC interpretation beyond chip-format-specific heatmap layout. Because legacy and EX chips use different physical layouts, the chip heatmaps can appear different even when the rest of the report is comparable. The same guidance applies to different versions of the same genotyping BeadChip family: they are treated as supported genotyping inputs rather than as mixed dataset types.
*** ## Warning and error messages During report generation, the following situations may trigger warnings or errors: * Input data directory does not exist * Required input files are missing in one or more dataset folders * Number of labels does not match number of detected datasets * Labels are modified during sanitization and become non-unique * Genotyping and methylation dataset folders are detected together in a single run * Invalid output format is specified * Invalid or unsupported QC threshold configuration is provided Note, **mixed dataset types** refers specifically to combining **genotyping** and **methylation** datasets in one report. Supported mixed genotyping inputs, such as **PGx + non-PGx**, **legacy + EX**, or different versions of the same genotyping BeadChip family, do **not** trigger this error. ### Examples | Message | Cause | | ------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ❌ Input data folder does not exist: `data/non_existent_dir` | Input directory does not exist | | ❌ No valid dataset folders detected. Each folder must include at least: `controls.raw_metrics.csv`, `controls.qc_metrics.csv` | No folders containing both required CSV files were found | | ❌ Dataset `MyDataset` is missing required file(s): ... `controls.qc_metrics.csv` ... | One or more required control metric files are absent | | ❌ Number of labels (2) does not match number of datasets (3). | `--label` count does not match detected datasets | | ⚠️ Label sanitized: `My Label<1>` → `My_Label_1` | Label contained unsupported characters | | ❌ Duplicate labels after sanitization: `dataset` (x2). Each dataset label must be unique. | Labels became non-unique after sanitization | | ❌ Mixed dataset types are not supported in a single report. Genotyping datasets: `geno_run_A`, `geno_run_B` ... Methylation datasets: `meth_run_C` ... | A single run included both genotyping and methylation dataset folders. This error is not raised for supported mixed genotyping inputs such as PGx + non-PGx or legacy + EX. | | ❌ Invalid `--output-format` value. Only `csv` or `xlsx` are accepted. | Unsupported output format | | ❌ QC config file was provided but does not exist: `/path/to/config.yaml` | Config path does not exist | | ❌ Input file must be a YAML file with extension `.yaml`/`.yml`. Current config file extension: `.json` | Config must be YAML | | ❌ Failed to parse QC config as YAML ... | YAML syntax error | | ❌ QC config must be wrapped in a top-level `QC` object containing a `Report` object. | Config structure is incorrect | | ❌ Invalid threshold overrides: Unknown threshold key ... | Unrecognized threshold name | | ❌ Invalid threshold overrides: Invalid numeric value ... | Value could not be parsed as a number | | ❌ Threshold for `Autosomal Call Rate` must be between 0 and 1 ... | Autosomal Call Rate threshold out of bounds | | ❌ Threshold for `stainingGreen` must be non-negative ... | Threshold must be ≥ 0 | # Input Files The following section describes the input files required by DRAGEN Array.\ Product files (anything other than the IDATs) can be found on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ## IDAT Files For each sample a pair of raw intensity files (.idat) are generated from the iScan System or NextSeq550 (for select arrays). They provide intensities in the red and green channels for each probe on the Infinium array. More information on which arrays can be used with NextSeq550, can be found on the [Illumina Knowledge page on NextSeq550](https://knowledge.illumina.com/microarray/general/microarray-general-faq-list/000003871). An IDAT file is identified by the BeadChip Barcode (12-digit unique Sentrix ID, i.e. 123456789101), BeadChip Position (row and column of the sample, i.e. R01C01), and Grn (Green) or Red for the specific channel. ## Manifest Files The CSV and BPM manifest files can be found on the Illumina Support Site for all commercial Infinium BeadChips or on [MyIllumina](http://my.illumina.com/) for custom and semi-custom designs. DRAGEN Array only supports manifest files from the Illumina Support site. For instructions on obtaining manifest files from MyIllumina, see Illumina Knowledge article, [How to access custom array product files (manifest and product definition files) in MyIllumina](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001531). The CSV manifest file (.csv) provides complementary data to the BPM manifest file in a human readable format. It is a required input to the genotype gtc-to-vcf command to enable VCF generation for insertion/deletion variants. `gtc-to-vcf` depends on the presence of accurate mapping information within the manifest, and may produce inaccurate results if the mapping information is incorrect. Mapping information follows the implicit dbSNP standard, where * Positions are reported with 1-based indexing. * Positions in the PAR are reported with mapping position to the X chromosome. * For an insertion relative to the reference, the position of the base immediately 5' to the insertion (on the plus strand) is given. * For a deletion relative to the reference, the position of the most 5' deleted based (on the plus strand) is given. ## Cluster File The cluster file (.egt) is a standard product file provided by Illumina for commercial genotyping products and it is a required input for the genotype call command in DRAGEN Array. Custom cluster files may be required for optimal genotyping performance. See section [Optimizing cluster files and copy number models](/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## PGx CN Model File The PGx CN (Copy Number) model file (.dat) is a required input to the pgx copy-number call command to enable accurate copy number calling for pharmacogenomics. Illumina provides a standard CN model file for each PGx array product. CN model files are named based on the manifest file revision (e.g., a CN model file trained from manifest revision B1 is paired with that B1 manifest) and must be used with their paired manifest file of the same revision. See section [Optimizing cluster files and copy number models](/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## Cytogenetics Model File The cytogenetics model file (.dat) is a required input to the cyto call command. Illumina provides a standard cyto model file for each supported array product. Cyto model files are named based on the manifest file revision and must be used with their paired manifest file of the same revision. For custom or other products, please contact Tech Support to request a cyto model file and ensure you include the product BPM manifest, EGT cluster file, and IDAT or GTC files for at least one sample. The cytogenetics model file primarily contains probe GC content information and is tied to the probe sequence/manifest. As long as the manifest and DRAGEN Array version remain unchanged, retraining is generally not required. Contrary to the PGx CN Model File, the Cytogenetics Model File does not require retraining when a different cluster file is used. **Note:** The CN model file needs to be updated upon manifest revisions since probes can be added or removed during manifest revisions. A mismatch between the CN model file and the manifest will cause an error during `pgx copy-number call` and `cyto call`. ## Mask File The mask file (.msk) is a required input to the pgx copy-number train command to enable accurate pgx copy number training for pharmacogenomics. It does not need to be provided as an explicit input to the command line interface but should reside in the same folder as the BPM manifest. It should have the same base name as the manifest for the product. Illumina provides a mask file for each PGx array product and these can be found on the [product files support page.](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html) ## PGx Database File The PGx database file (.zip) contains the variant mapping information from Infinium PGx arrays to PGx variants. Each line in this file represents a single probe ID mapping to a variant's HGVS (Human Genome Variation Society) tag. This creates a map of many probes to one variant. DRAGEN Array cross references this map with SNV VCF IDs during runtime to do star allele calling. It works across all supported PGx products, even though the probes and variant coverage differ across them. ## Cytogenetics Database File The cytogenetics database file (.zip) contains information from Ensembl and RefSeq data sources used in the generation of Cytogenetics Annotation JSON File. This file can be used across products (beadchip/manifest types and versions). It is only necessary for input to local analysis (i.e., `cyto annotate`) as it is already stored in the cloud for cloud analysis. It may be updated in the future to accomodate changes in the underlying Ensembl and RefSeq datasources. ## Genome FASTA Files The genome FASTA file (.fa) is a text file with the reference genome sequences.The FASTA index file (.fai) contains metadata about chromosomal orchestration within the FASTA file for a particular species. DRAGEN Array PGx calling supports human genome build 37 and 38. The genome FASTA file and FASTA index file are both provided by Illumina for human species and should be stored together in the same input folder.\ For custom reference genomes, the contig identifiers in the provided genome FASTA file must match exactly the chromosome identifiers specified in the provided manifest. For a standard human product manifest, this means that the contig headers should read ">1" rather than ">chr1". Note: The Genome FASTA file is only required for the dragen-array-local-analysis workflow. If you're using dragen-array-cloud-analysis, you do not need to provide this file. ## Sample Sheet The sample sheet is a CSV formatted input file that utilizes a couple required fields for sample lookup (`SentrixBarcode_A, SentrixPosition_A` for local, `beadChipName, sampleSectionName` for cloud) to enable adding optional metadata and analyzing a filtered list of samples within a folder. It is intended to be flexible and the local version should be backwards compatible with most GenomeStudio samplesheets. The root folder which DRAGEN Array will search the files for can be set by either providing it via the `--idat-folder` or `--gtc-folder` options (where applicable). Or by setting the `RootFolder` field in the `[Header]` section. This `RootFolder` should be the full absolute path to the sample files. e.g., ``` [Header] RootFolder,/test/samples [Data] .... ``` **Note:** In the case of conflict between `RootFolder` and the CLI options (`--idat-folder` or `--gtc-folder`), the CLI options take precedence. The following are examples of all valid samplesheets. * Most basic (no sections, one sample) ``` SentrixBarcode_A,SentrixPosition_A 204753010023,R02C01 ``` * Medium complexity (no sections, multiple samples, optional data) ``` SentrixBarcode_A,SentrixPosition_A,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010024,R01C01,NA1233,Group2,M ``` * High complexity (sections, multiple samples, optional data) ``` [Header] RootFolder,/tests/samples Date,1/1/2025 [Data] SentrixBarcode_A,SentrixPosition_A,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010024,R01C01,NA1233,Group2,M ``` **Notes:** * The column names are case insensitive. For example, the columns `Sample_Name` and `sample_name`, would be considered the same and the software would produce an error like this: `Duplicate column sample_name found. Column names are case-insensitive. Please remove or rename the column from the samplesheet and re-process.` * Because user-provided fields get output in the [Genotype Summary File](/product-guides/output-files#genotype_summary_files), the column names cannot conflict with those fields. For example, if the user provides a column named `Sex Estimate` in their samplesheet. DRAGEN Array will produce the following error: `Sex Estimate is a reserved keyword. Please remove or rename the column from the samplesheet and re-process.` * The optional fields (i.e. not `SentrixBarcode_A` and `SentrixPosition_A`) will be output as-is in the [genotype summary files](/product-guides/output-files#genotype_summary_files) for the `genotype call` command. * The `[Manifests]` section (used by GenomeStudio to delineate manifests in multi-manifest analyses) is currently ignored in DRAGEN Array. * The samplesheet is validated to ensure that column names and field values do not exceed 500 characters. * When editing samplesheets in Excel, format `SentrixBarcode_A` as a number with 0 decimal places. If stored as text with a decimal or in scientific notation, the software cannot determine the IDAT file names and the run will fail. ### Sample Name Determination The sample name used in downstream outputs is determined by the following precedence rules: 1. **Samplesheet** — If a `Sample_Name` column is present, that value per row/sample is used. **Note:** If that column is present, it *must* be non-empty for every row and it cannot conflict with the default Sample ID - `SentrixBarcode_Position` (e.g., `204753010023_R01C01`). 2. **IDAT or GTC metadata** — If no samplesheet value is available, the sample name embedded in the IDAT or GTC file metadata is used. 3. **SentrixBarcode\_Position fallback** — If neither (1) nor (2) provides a sample name, the sample name defaults to the default Sample ID - `SentrixBarcode_Position` (e.g., `204753010023_R01C01`). When both the samplesheet and file metadata define a sample name, the samplesheet value takes precedence. **Note:** does not modify the sample name stored in the GTC file; it only affects downstream outputs such as the [Genotype Sample Summary](/product-guides/output-files#genotype_summary_files) and VCFs. If downstream commands are run with different samplesheets that specify different sample names, the resulting downstream outputs will reflect those different names. The key takeaway is that the samplesheet for any given command is treated as the source of truth for the sample name. ### Methylation QC sample sheet For DRAGEN Array Methylation QC on cloud, the samplesheet does not currently support sections such as `[Header]` and `[Data]` and instead of using `SentrixBarcode_A` and `SentrixPosition_A` columns as the sample's keys, it uses `beadChipName` and `sampleSectionName`. Furthermore, the additional optional sample sheet fields are used in analysis. Following Sample\_Group, any number of additional columns can be added to include meta data fields such as sex, sample type, plate and well information, etc. Additional columns added after the Sample\_Group column may have user-defined column header values. The Sample\_ID field and any additional metadata added will be replicated in the Sample QC Summary output files. The Sample\_Group field will be used to populate the PCA Control Plot within the Sample QC Summary Plots file and the Principal Component Summary file. For the PCA Control Plot, each sample group will be assigned a unique color. Samples assigned to the same Sample\_Group value will be the same color in the PCA Control Plot. e.g., ``` beadChipName,sampleSectionName,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010023,R02C01,NA1232,Group2,F 204753010024,R01C01,NA1233,Group2,M 204753010024,R02C01,NA1234,Group1,M ``` ### Cytogenetics analysis + Emedgene interpretation sample sheet For Cytogenetics analysis + Emedgene interpretation on cloud, an additional column: `demographicSex` will be used to compare against to the `Sex Estimate` output from DRAGEN Array genotyping module and be displayed in Emedgene. The allowed values for this field are `M` (Male), `F` (Female), or `U` (Unknown). Example: ``` SentrixBarcode_A,SentrixPosition_A,demographicSex 204753010023,R01C01,F 204753010023,R02C01,F 204753010024,R01C01,M 204753010024,R02C01,M ``` ## YAML Config File The YAML config file (`.yaml` or `.yml`) is an optional user-provided input to the local `qc report` command and a pre-setup input file to the cloud [DRAGEN Array - Genotyping and QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-genotyping) pipeline. Use it when you want to override QC thresholds. Set a threshold to a numeric value to enable that check, or set it to `null` to disable it. If `--config` is omitted, `qc report` uses built-in defaults for `callRate` and `logRDev`. Other thresholds are unset by default. For cloud [DRAGEN Array - Genotyping and QC](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-genotyping) pipeline, a set of default product file configurations are provided for commercial array products, where chemistry-specific default YAML config files are used. To change the QC thresholds, users can create Custom Configuration with updated YAML config files. For the required YAML structure, chemistry-specific template files, generic examples, and additional guidance, see [Configuration file (optional)](/product-guides/dragen-array-local-analysis/qc-report#configuration-file-optional). ## Input File Summary Table In addition to the input files, there are set of intermediate files, including GTC, SNV VCF, CNV VCF and PGx CSV, which are outputs of some DRAGEN Array Local commands and inputs to other commands. The table below summarizes the input files or intermediate file, their sources, and the associated DRAGEN Array Local commands and options. | Input File | File Extension | Source | Command | Option | | ------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------ | --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------- | | [IDAT](#section-idat) | .idat | User provided from scanning instrument |

genotype call
qc call

| --idat-folder | | [CSV Manifest](#manifest_files) | .csv | Product file from Illumina |

genotype gtc-to-vcf
qc call

| --csv-manifest | | [BPM Manifest](#manifest_files) | .bpm | Product file from Illumina |

pgx copy-number train

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

| --bpm-manifest | | [Cluster File](#section-cluster-file) | .egt | Product file from Illumina or user created using GenomeStudio | [genotype call](/product-guides/dragen-array-local-analysis#section-genotype-call) | --cluster-file | | [PGx CN Model](#cn_model_file) | .dat | Product file from Illumina or user created using DRAGEN Array Local | [pgx copy-number call](/product-guides/dragen-array-local-analysis#section-pgx-copy-number-call) | --cn-model | | [Cytogenetics CN Model](#cyto_model_file) | .dat | Product file from Illumina | [cyto call](/product-guides/dragen-array-local-analysis#section-cyto-call) | --cn-model | | [PGx Database](#section-pgx-database-file) | .zip | Product file from Illumina | [pgx star-allele call](/product-guides/dragen-array-local-analysis#section-pgx-star-allele-call) | --database | | [Cytogenetics Database](#cyto_db_file) | .zip | Product file from Illumina | [cyto annotate](/product-guides/dragen-array-local-analysis#section-cyto-annotate) | --annotation-db | | [Genome FASTA](#section-genome-fasta-files) | .fa | Product file from Illumina |

genotype gtc-to-vcf

pgx copy-number train

| --genome-fasta-file | | [YAML Config File](#section-yaml-config-file) |

.yaml

.yml

| Illumina template config file or user-provided config file | [qc report](/product-guides/dragen-array-local-analysis/qc-report) | --config | | [Sample Sheet](#section-sample-sheet) | .csv | User provided |

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

cyto call
qc call

| --sample-sheet | | [GTC](/product-guides/output-files#genotype_call_file) | .gtc | DRAGEN Array output from genotype call |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

| --gtc-folder | |

SNV VCF

PGx CNV VCF

|

.snv.vcf

.cnv.vcf

| DRAGEN Array output from genotype gtc-to-vcf and pgx copy-number call | [pgx star-allele call](/product-guides/dragen-array-local-analysis#section-pgx-star-allele-call) | --vcf-folder | | [PGx DAT](/product-guides/output-files#star_allele_dat) | .dat | DRAGEN Array output from pgx star-allele call | [pgx star-allele annotate](/product-guides/dragen-array-local-analysis#section-pgx-star-allele-annotate) | --star-alleles | | [Cytogenetics CNV VCF](/product-guides/output-files#cyto_vcf_file) | .cnv.vcf | DRAGEN Array output from cyto call | [cyto annotate](/product-guides/dragen-array-local-analysis#section-cyto-annotate) | --vcf-folder | # Output Files The following section describes the outputs produced by DRAGEN Array. ## PGx CNV VCF File DRAGEN Array produces one PGx CNV variant call file (VCF) (\*.cnv.vcf) per sample to report the CN status on the gene and sub gene level, along with the CN events for PGx targets. The PGx CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV call quality. The CNV call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<7) are flagged by the Q7 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The PGx CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The PGx CNV VCF output file includes the following content. ```vcf ##fileformat=VCFv4.1 ##source=dragena 1.4.0 ##genomeBuild=38 ##reference=file:///hg38_with_alt/hg38_nochr_MT.fa ##FORMAT= ##FORMAT= ##ALT= ##FILTER= ##FILTER= ##INFO= ##INFO= ##INFO= ##INFO= ##OverallPloidy=1.8 ##GCCorrect=True ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 204619760001_R01C01 1 109687842 CNV:GSTM1:chr1:109687842:109693526 N 60 PASS CNVLEN=5685;PROBE=124;END=109693526;SVTYPE=CNV CN:NR 2:0.966631132771593 4 68537222 CNV:UGT2B17:chr4:68537222:68568499 N 60 PASS CNVLEN=31278;PROBE=383;END=68568499;SVTYPE=CNV CN:NR 0:0.376696837881692 10 133527374 CNV:CYP2E1:chr10:133527374:133539096 N 60 PASS CNVLEN=11723;PROBE=194;END=133539096;SVTYPE=CNV CN:NR 2:0.980059731860893 16 28615068 CNV:SULT1A1:chr16:28603587:28613544 N 57 PASS CNVLEN=8315;PROBE=164;END=28623382;SVTYPE=CNV CN:NR 2:0.980552325552963 19 40844791 CNV:CYP2A6.intron.7:chr19:40844791:40845293 N 60 PASS CNVLEN=503;PROBE=38;END=40845293;SVTYPE=CNV CN:NR 2:0.9663775484762 19 40850267 CNV:CYP2A6.exon.1:chr19:40850267:40850414 N 60 PASS CNVLEN=148;PROBE=21;END=40850414;SVTYPE=CNV CN:NR 2:0.9663775484762 22 42126498 CNV:CYP2D6.exon.9:chr22:42126498:42126752 N 48 PASS CNVLEN=255;PROBE=370;END=42126752;SVTYPE=CNV CN:NR 2:0.981703411438716 22 42129188 CNV:CYP2D6.intron.2:chr22:42129188:42129734 N 10 PASS CNVLEN=547;PROBE=333;END=42129734;SVTYPE=CNV CN:NR 2:0.965498002434641 22 42130886 CNV:CYP2D6.p5:chr22:42130886:42131379 N 60 PASS CNVLEN=494;PROBE=172;END=42131379;SVTYPE=CNV CN:NR 2:0.970341562236357 22_KI270879v1_alt 270316 CNV:GSTT1:chr22_KI270879v1_alt:270316:278477 N 60 PASS CNVLEN=8162;PROBE=91;END=278477;SVTYPE=CNV CN:NR 2:1.01191145130511 ``` ## Cytogenetics VCF File DRAGEN Array produces one cytogenetics Variant Call File (VCF) (\*.cnv.vcf) per sample to report the CN and LOH status of the detected variants. The cytogenetics CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV/LOH call quality. It reflects the probability that the called state (e.g., deletion, duplication, or LOH) is correct. This probability is converted into a Phred-scaled score using the standard formula: `QUAL = -10 * log10(1 - probability of the called state)`. The Phred-scaled score is capped at 60 and the minimum value is 0. Low quality calls (QUAL<10) are flagged by the Q10 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The cytogenetics CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as cyto annotate. One example file can be found below: ```vcf ##fileformat=VCFv4.1 ##source=dragena 1.4.0 Cyto ##genomeBuild=37 ##product=GDACyto-8v1-0_A ##reference=file://genome.fa ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##platform=cytoplatform ##ALT= ##ALT= ##ALT= ##FILTER= ##FILTER= ##FILTER= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##OverallPloidy=1.9 ##GCCorrect=True ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 208588190001_R02C01 1 109687841 DEL:chr1:109687842:109693526 N 60 PASS SVLEN=5685;PROBE=99;END=109693526 GT:CN:NR:LRD 1/1:1:0.8860:0.21 16 28603586 DUP:chr16:28603587:28613544 N 60 PASS SVLEN=9958;PROBE=197;END=28613544 GT:CN:NR:LRD 1/1:3:1.1666:0.11 ## Example of a mosaic deletion variant 19 49460914 MosaicDEL:19:49460915:53118000 N 60 PASS SVLEN=3657086;PROBE=4994;END=53118000;MOSAIC GT:CN:NR:LRD:MF 1/1:1:0.9478:0.0626:0.4487 ## Example of an LOH variant (CN=2) 21 42129187 AOH:chr22:42129188:42129734 N 37 PASS SVLEN=547;PROBE=198;END=42129734;LOHTYPE=AOH GT:CN:NR:LRD 1/1:2:1.0208:0.25 ## Example of a GAINLOH variant (CN=3) 22 9426437 AOH:22:9426438:14766246 N 60 PASS SVLEN=5339809;PROBE=422;END=14766246;LOHTYPE=AOH GT:CN:NR:LRD:MF 1/1:3:1.0995:0.0617:. ``` ## SNV VCF File The software produces one genotyping variant call file (\*.snv.vcf) file per sample, covering single nucleotide variants (SNV) and indels for the sample. It reports GenCall score (GS), B Allele Frequency (BAF), and Log R Ratio (LRR) per variant. The VCF file output follows [VCF4.1 format](https://samtools.github.io/hts-specs/VCFv4.1.pdf). Some additional details: * The FILTER column is hardcoded to `PASS` and is not dependent on the `GT` value. It does not reflect the underlying quality of the call. Refer to the `GS` value for quality information. * Genotypes are adjusted to reflect the sample ploidy. Calls are haploid for loci on Y, MT, and non-PAR chromosome X for males. * Multiple SNPs in the input manifest which are mapped to the same chromosomal coordinate (e.g. tri-allelic loci or duplicated sites) are collapsed into one VCF entry and a combined genotype generated. To produce the combined genotype, the set of all possible genotypes is enumerated based on the queried alleles. Genotypes which are not possible based on called alleles and assay design limitations (e.g. Infinium II designs cannot distinguish between A/T and C/G calls) are filtered. If only one consistent genotype remains after the filtering process, then the site is assigned this genotype. Otherwise, the genotype is ambiguous (more than 1) or inconsistent (less than 1) and a no-call is returned. * Certain SNV and indel calls will be skipped when reported in the VCF. Skipped data can include unmapped loci (i.e., `Chr` is `0` in the manifest), intensity-only probes used for CNV identification, and indels that do not map back to the genome. See [Warning/Error Messages and Logs](#section-warningerror-messages-and-logs) for messages that may be seen with DRAGEN Array Local related to the skipped data. * The BAF and LRR are oriented with Ref as A and Alt as B relative to the reference genome, while GS is agnostic to the reference genome. Users familiar with GenomeStudio may observe BAF and LRR reported in the VCF as 1 minus the value reported in GenomeStudio depending on the Ref Alt allele orientation with the reference genome. GenomeStudio reports these values based on the information in the manifest without knowledge of the reference genome. * The SNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the [TBI Index File](#tbi-index-file). To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The SNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The SNV VCF output file includes the following content. The last row shows an example of variant call. ```vcf ##fileformat=VCFv4.1 ##source=dragena 1.4.0 ##genomeBuild=38 ##reference=file:///genomes/38/genome.fa ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 202937470021_R06C01 1 2290399 rs878093 G A . PASS . GT:GS:BAF:LRR 0/1:0.7923:0.50724137:0.14730307 ``` ### Note on Multi-Allelic Variants (MAV) Illumina Microarrays are inherently bi-allelic assays made up of [Infinium I or Infinium II probe designs](https://www.illumina.com/Documents/products/technotes/technote_iselect_design.pdf) which require special design considerations and have some inherent limitations. However, DRAGEN Array can combine multiple assays with different target bases but the same genomic position to make multi-allelic variant calls (MAVs). In this context, the MAV nomenclature is not only referring to an actual multi-ALT allelic variant call (i.e., `GT=1/2` being the only possible outcome from a read-driven, single-sample VCF) but rather, any site where there are multiple ALTs in the VCF (e.g., `REF=A`,`ALT=T,C`). In other words, unlike NGS small variant callers like [DRAGEN](https://help.dragen.illumina.com/), which will dynamically call variants based on the reads present, DRAGEN Array creates the REF and ALTs for each entry in the SNV VCF based on theoretical, a-priori information from the manifest and reference genome files. It then only "fills in" the `GT` entry based on the core genotyping algorithm. This is how from DRAGEN Array single-sample SNV VCFs, it is possible to get `GT` calls like `0/2`, `2/3`, `1/3`, etc. The MAV calling algorithm currently checks all possible genotypes based on the alleles present in the overlapping assays, retaining only genotypes whose alleles are consistent with all the calls of the individual assays. If either no genotypes remain or if multiple genotypes remain after filtering, the result is considered ambiguous and reported as a NoCall to avoid false positives. The ambiguity often arises when one assay is a NoCall due to presumed probe failure rather than missing signal. In such cases, its potential genotypes are not excluded, contributing to ambiguity. When DRAGEN Array outputs a NoCall because of the described behaviors, they are logged as [warnings](#section-warningerror-messages-and-logs) (e.g., `Failed to combine genotypes due to inconsistencies...`). Overall, the current algorithm errs on the side of caution to ensure quality calls, but produces some idiosyncratic behavior and potential false NoCalls when genotypes are biologically consistent but differ due to probe designs. Some illustrative examples are below to help understand the current limitations: | Scenario | Expected MAV Call | Actual MAV Call | Explanation | | ---------------------------------------- | ----------------- | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Inf II \[A/G] -> AA + Inf I \[T/G] -> TT | AT | NoCall | The Inf II assay cannot differentiate A versus T alleles, hence AA call for the Inf II assay is consistent with AA, AT, or TT genotypes. The Inf I assay TT call, however, is consistent with AT or TT genotypes. Combining the two assays results in a NoCall due to the AT or TT ambiguity. | | Inf I \[T/A] -> AA + Inf II \[A/G] -> NC | AA | NoCall | NoCall for Inf II probe leaves possibility of AG. Ambiguity between AG or AA leads to NoCall. | Variants of this type also only output the `GT` and `GS` fields. They do not output `BAF` or `LRR` values at this time. The `GT` field is determined via the algorithm described above. The `GS` field is simply the minimum `GS` of all the overlapping assays. #### Using Infinium I NoCall Information In v1.4, some additional behavior was added to improve the overall call rate and accuracy of MAV calling. When the option `--use-infI-nc-info` is enabled, the algorithm constrains possible genotypes by ruling out Infinium I probe no call related alleles not present in the other assays (i.e., reversing the underlying assumption - presuming missing signal, not probe failure). This reduces the chance of ambiguity after filtering possible genotypes but also increases false positive rate. **Note:** Using this option requires an update to your [cluster file](/product-guides/input-files#section-cluster-file) to cluster "zeroed-out" MAV assays. Without doing this, accuracy will drop when using this option. Reach out to for more details. ### Note on delimiters in the "ID" field By default, when multiple probes are present for a given variant, all probe names are included in the "ID" field of the resulting VCF file. * For SNP entries, probe names are separated by commas (,). * For Indel entries, probe names are separated by semicolons (;). ### Note on REF/ALT "flipping" for INDELs Expected REF and ALTs for INDELS may not match the dbSNP annotations in rare cases. E.g., an expected "Deletion" with the REF = "ATCG" and the ALT = "A" may be "flipped" to an "Insertion" variant with REF="A" and ALT="ACTG". The corresponding genotype output will take this into account so the actual VCF is still correct. This is simply a notation issue in some of the manifest files. ### Note on PLINK compatibility It is possible to make DRAGEN Array genotype VCF files compatible for conversion to PED/MAP format with preprocessing using tabix (v1.19.1), BCFtools (v1.21) and PLINK (v1.9). The following three commands demonstrate the basic process. * `bcftools merge -l vcf_list.txt -Oz -o merged.vcf.gz` creates a single compressed VCFs from individual sample VCFs listed in the `vcf_list.txt` text file. * `tabix -p vcf merged.vcf.gz` creates a binary index for the merged file. * `plink --vcf merged.vcf.gz --recode --out merged` creates .ped and .map files with the prefix provided to `--out`. Some optional arguments may be provided to PLINK depending on the content of the VCFs to be converted and the downstream analysis. * For VCFs containing non-standard human chromosomes (e.g. haplotype chromosomes or unplaced contigs), the `--allow-extra-chr` flag can be used. * If using non-human data, refer to the [PLINK documentation](https://www.cog-genomics.org/plink/1.9/input#chr_set) for the `--chr-set` argument and supported options. * By default, PLINK will only consider the most common ALT allele for multi-allelic variants. The `--biallelic-only` argument can be provided to exclude multi-allelic variants altogether. As an alternative, using `bcftools norm -m - in.vcf.gz -Oz -o out.vcf.gz`, can be used upstream of PLINK to split multi-allelic variants into bi-allelic records to retain them for downstream processing. For more info on the options described and others, refer the [PLINK VCF conversion documentation](https://www.cog-genomics.org/plink/1.9/input#vcf). ## Genotype Call (GTC) File The genotype call algorithm produces one genotype call file (.gtc) per sample analyzed. The Genotype Call (GTC) file contains the small variant (SNV and indel) genotype for each marker specified by the product and sample quality metrics. The sample marker location is not included and must be extracted from the manifest file. Binary proprietary format can be parsed using the Illumina open-source tool [BeadArray Library File Parser](https://github.com/Illumina/BeadArrayFiles). **Note on lack of i18n:** GTCs are binary/fixed format files designed before modern internationalization and localization tools. There is a related [known issue](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes#section-known-issues) that makes the GTCs unable to be used in downstream analyses. Refer to the same issue to see a workaround. **Note on legacy GTCs:** Other Illumina software (such as AutoConvert and Beeline) also produce GTC files.\ These "legacy GTC" files will work in DRAGEN Array genotyping commands such as `genotype gtc-to-vcf` but they will not work with all other downstream analyses such as [Cytogenetics analysis](/overview/our-features#section-dragen-array-cytogenetics-analysis) and [PGx](/overview/our-features#section-dragen-array-pgx-star-allele-annotation). We recommend using DRAGEN Array end-to-end starting from IDATs for these analyses. ## BedGraph Files The BedGraph files contains the Log R Ratios (LRR.bedgraph) and B-Allele Frequencies (BAF.bedgraph) from the genotyping algorithm for use in visual tools. ## PGx Star Allele Dat File The Star Allele Dat file is an intermediate file generated by the pgx star-allele call command and serves as the input to the pgx star-allele annotate command. ## PGx Star Allele CSV Files The following files are output from the pgx star-allele annotate command. Each CSV file also contains meta information marked by # at the top of the file for the genome build and PGx database used for the star allele calling. The structure of the output files is shown below.
### Diplotype Summary CSV The file `diplotype_summary.csv` contains all the star allele calls for all samples in a run. Each row in the file provides either a star allele diplotype or simple variant call for a PGx-related gene. Star allele diplotype calls for a sample and a gene may span multiple lines where alternative solutions can be listed. The `diplotype_summary.csv` file contains the following details per sample: | Field | Description | | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Sentrix barcode and position of the sample. | | Solution Id | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1, with alternative solutions ranked lower. | | Gene or Variant | The gene symbol for haplotype calls, or gene symbol plus rsID (or variant identifier) for variant-level calls. | | Type | PGx calling type: **Haplotype** (star allele–based call) or **Variant** (individual variant call). | | Solution | Star allele or variant solution. For diploid calls, the format is *Allele1/Allele2*. Variant calls may include explicit nucleotide changes or reference calls. | | Array‑Equivalent Alleles (Allele 1) | Star alleles for allele 1 that are indistinguishable from the reported solution given the content of the genotyping array and no‑call variants. The allele with the highest population frequency is reported in the solution. | | Array‑Equivalent Alleles (Allele 2) | Star alleles for allele 2 that are indistinguishable from the reported solution given the content of the genotyping array and no‑call variants. The allele with the highest population frequency is reported in the solution. | | No Call Variants | Variants that are not called in the sample SNV VCF. | | Alleles Impacted by No‑Call | Star alleles whose definitions are impacted by missing or no‑call variants. | | Background Alleles | Background or reference star alleles relevant to the gene, often used to contextualize indistinguishable or default calls. | | Variant Change | Observed variant change, structural variant, or notation explaining an unresolved or no‑call condition (e.g., explicit genomic coordinates or deletion events). | | Score | A probability score representing the likelihood of the inferred solution based on the SNV and CNV inputs, input quality, and population frequencies of PGx alleles. The score ranges from 0 to 1. | | Raw Score | Raw probability score of the solution excluding population frequency. The score ranges from 0 to 1. | | Copy Number Solution | Estimated copy number for each gene region. The field is formatted as *GeneRegion=CopyNumber* (e.g., `CYP2D6.exon.9=2`). | Below is an example of the first 4 columns from a diplotype summary CSV file: `Sample,Solution Id,Gene or Variant,Type,Solution` `204650490282_R02C01,1,CYP2C9,Haplotype,*9/*11` `204650490282_R02C01,1,CYP2C19,Haplotype,*2/*10` ### Diplotype Annotation CSV The diplotype\_annotations.csv file contains aggregated genotype annotations and their corresponding clinical interpretations for pharmacogenomics (PGx)–related genes across all samples in a run. Each row represents the final, resolved genotype result for a single sample–gene pair, along with any associated activity score and phenotype interpretation based on CPIC guidelines. | Field name | Description | | ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `Sample` | Unique identifier for the sample within the run. Each sample appears on multiple rows, one per reported gene. | | `Gene` | Gene or genomic locus associated with the reported PGx result (e.g., CYP2C9, SLCO1B1, MT‑RNR1). | | `PGx Guideline` | Clinical pharmacogenomics guideline used for interpretation. Values may include **CPIC** (Clinical Pharmacogenetics Implementation Consortium) or **DPWG** (Dutch Pharmacogenetics Working Group). | | `Genotype` | **Aggregated genotype** result for the sample and gene. This may be represented as a star‑allele diplotype (e.g., \*1/\*1), a combination of specific variants (e.g., rsID‑based calls), or reference/variant notation. | | `Activity Score` | Numeric activity or function score derived from the aggregated genotype for genes with defined scoring systems (e.g., CYP genes, DPYD). Reported as `n/a` when not applicable. | | `Phenotype Database Annotation` | Clinical phenotype or functional interpretation associated with the aggregated genotype (e.g., *Normal Metabolizer*, *Decreased Function*, *Ivacaftor non‑responsive*). Reported as `n/a` if no standardized interpretation is available. | ### Supporting Variants CSV The `supporting_variants.csv` lists the variant‑level evidence used to support each allele call reported in `diplotype_summary.csv`. Each row links a solution‑level allele call to one of its supporting genomic variants, including probe identifiers, genotype, copy number, and quality metrics, enabling full traceability, validation, and troubleshooting. | Field | Description | | ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Unique identifier for the analyzed sample (e.g., array sample or well ID). | | Solution Id | Identifier for the allele solution within a gene. This field corresponds directly to **Solution Id** in the `diplotype_summary` file, enabling linkage between allele calls and their supporting variants. | | Gene or Variant | Gene symbol for star‑allele–based calls (e.g., `CYP2D6`) or variant identifier for simple variant–based calls. | | Allele | Star allele or allele designation supported by the listed variant (e.g., `*1`, `*2`, `*36+*10`). | | HGVS | Genomic variant represented using HGVS nomenclature (e.g., `NC_000010.11:g.94842866A>G`). | | RSID | dbSNP reference SNP identifier (e.g., `rs3758581`). Provided for external reference and downstream validation; may be empty if not applicable. | | Chromosome | Chromosome on which the variant is located (e.g., `1–22`, `X`, `MT`). | | Position | Genomic base‑pair position of the variant on the chromosome. | | Ref | Reference allele at the variant position. | | Alts | Alternate allele(s) observed at the variant position. May be comma‑separated for multiallelic sites. | | Genotype | Observed genotype for the variant in the sample, reported in VCF‑style notation (e.g., `0/1`, `1/1`, `0/2`). | | Variant Copy Number | Estimated copy number of the variant allele, reflecting zygosity or duplications where applicable. | | GS | Genotype score representing confidence or quality of the variant call. | | BAF | B‑allele frequency for the variant, used to support genotype and copy‑number interpretation. | | ProbeID | Illumina probe identifier(s) used to genotype the variant. Included for downstream validation and troubleshooting. | ### Unresolved Variants CSV Explicit reporting of variants that could not be resolved, with accompanying reasons (for example, variants not present on the array or variants with no-call genotype). The `unresolved_variants.csv` file is analogous to `supporting_variants.csv` and contains variant‑level details for unresolved variants that are included in the allele definition for each allele call across every gene and solution within a sample. | Field | Description | | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Unique identifier for the analyzed sample (e.g., array sample or well ID). | | Gene or Variant | Gene symbol associated with the star‑allele call (e.g., CYP2D6, UGT1A1) or the specific variant being evaluated. | | Solution Id | Identifier for the allele solution in which this unresolved variant occurs, allowing linkage to solution‑level allele calls. This field corresponds directly to **Solution Id** in the `diplotype_summary.csv` file. | | Allele | Star allele associated with the unresolved variant (e.g., \*1, \*2, \*10), if applicable. | | HGVS | Genomic variant represented using HGVS nomenclature, specifying the precise genomic location and sequence change. | | RSID | dbSNP reference SNP identifier for the variant. Provided to support external validation and cross‑reference with public variant databases. | | Reason | Reason the variant could not be resolved in the star‑allele call (e.g., Not on Array, No Call). | | ProbeID | Identifier(s) for the array probe(s) associated with the variant. Provided to support probe‑level investigation and downstream troubleshooting. | ### Variants not Covered on Array CSV The `variants_not_covered_on_array.csv` file lists the variants that are included in the PGx database used for the star allele calling but are not present in the manifest file used for the analysis. | Field Name | Description | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | Gene or Variant | Gene symbol or variant identifier associated with the uncovered variant. This may represent the gene containing the variant or a named variant definition. | | HGVS | Variant described using HGVS nomenclature, indicating the genomic or mitochondrial location and sequence change. | | RSID | dbSNP reference SNP ID (rsID) for the variant, if available. This field is empty if no rsID is assigned. | | Alleles Impacted | List of star alleles or allele definitions that include this variant and may be impacted due to lack of array coverage. | ## Genotype Summary Files The software produces genotype summary files (gt\_sample\_summary.csv and gt\_sample\_summary.json) that contains the following details per sample: * Sample ID * Sample Name (see [Sample Name Determination](/product-guides/input-files#sample-name-determination)) * Sample Folder * Autosomal Call Rate * Call Rate * Log R Ratio Std Dev * Sex Estimate * TGA\_Ctrl\_5716 Norm R * (Optional) User defined fields from the [samplesheet](/product-guides/input-files#section-sample-sheet) The TGA\_Ctrl\_5716 Norm R field is specific to PGx products (e.g., Global Diversity Array with enhanced PGx). The field value is the Normalized R value of one probe and is meant as an assay control where < 1 indicates the sample failed in the TGA (Targeted Gene Amplification) process. If the product does not have this probe, it is not included in the gt\_sample\_summary. The user defined fields from the samplesheet will appear as-is in the gt\_sample\_summary files. e.g. for the given samplesheet: ``` SentrixBarcode_A,SentrixPosition_A,Sample_Name,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,SampleRosalind,NA1231,Group1,F 204753010024,R01C01,SampleGregor,NA1233,Group2,M ``` It would produce something like the following `gt_sample_summary.csv`: ``` Sample ID,Sample Name,Sample Folder,Autosomal Call Rate,Call Rate,Log R Ratio Std Dev,Sex Estimate,SentrixBarcode_A,SentrixPosition_A,Sample_Name,Sample_ID,Sample_Group,MetaData1 204753010023_R01C01,SampleRosalind,/sample/folder,0.99414575,0.98843694,0.14829777,F,204753010023,R01C01,SampleRosalind,NA1231,Group1,F 204753010024_R01C01,SampleGregor,/sample/folder,0.99415575,0.98943694,0.14929777,M,204753010024,R01C01,SampleGregor,NA1233,Group2,M ``` And something like the following `gt_sample_summary.json`: ``` [ { "Sample ID": "204753010023_R01C01", "Sample Name": "SampleRosalind", "Sample Folder": "/sample/folder", "Autosomal Call Rate": 0.99414575, "Call Rate": 0.98843694, "Log R Ratio Std Dev": 0.14829777, "Sex Estimate": "F", "SentrixBarcode_A": "204753010023", "SentrixPosition_A": "R01C01", "Sample_Name": "SampleRosalind", "Sample_ID": "NA1231", "Sample_Group": "Group1", "Sex": "F" }, { "Sample ID": "204753010024_R01C01", "Sample Name": "SampleGregor", "Sample Folder": "/sample/folder", "Autosomal Call Rate": 0.99415575, "Call Rate": 0.98943694, "Log R Ratio Std Dev": 0.14929777, "Sex Estimate": "M", "SentrixBarcode_A": "204753010024", "SentrixPosition_A": "R01C01", "Sample_Name": "SampleGregor", "Sample_ID": "NA1233", "Sample_Group": "Group2", "Sex": "M" } ] ``` **Note:** As of v1.3, samples that fail during genotyping will still be present in this file. See the details in the [release notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes#section-new-features-in-detail). ## Final Report DRAGEN Array Cloud produces a Final Report (gtc\_final\_report.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus per sample: | Field | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | SNP Name | SNP identifier. | | SNP | SNP alleles as reported by assay probes. Alleles on the Design strand (the ILMN strand) are listed in order of Allele A/B. | | Sample ID | Sample identifier. | | Allele 1 – Top | Allele 1 corresponds to Allele A and are reported on the Top strand. | | Allele 2 – Top | Allele 2 corresponds to Allele B and are reported on the Top strand. | | Allele 1 – Forward | Allele 1 corresponds to Allele A and are reported on the Forward strand. | | Allele 2 – Forward | Allele 2 corresponds to Allele B and are reported on the Forward strand. | | Allele 1 – Plus | Allele 1 corresponds to Allele A and are reported on the Plus strand. | | Allele 2 – Plus | Allele 2 corresponds to Allele B and are reported on the Plus strand. | | GC Score | Quality metric calculated for each genotype (data point), and ranges from 0 to 1. | | GT Score | The SNP cluster quality. Score for a SNP from the GenTrain clustering algorithm. | | Log R Ratio | Base-2 log of the normalized R value over the expected R value for the theta value (interpolated from the R-values of the clusters). For loci categorized as intensity only; the value is adjusted so that the expected R value is the mean of the cluster. | | B Allele Freq | B allele frequency for this sample as interpolated from known B allele frequencies of 3 canonical clusters: 0, 0.5 and 1 if it is equal to or greater than the theta mean of the BB cluster. B Allele Freq is between 0 and 1, or set to NaN for loci categorized as intensity only. | | Chr | Chromosome containing the SNP. | | Position | SNP chromosomal position. | *Note: Analyses on products with large numbers of loci (>1 Million) and large numbers of samples (>100) yield a large (50+ Gigabyte) Final Report that are difficult to download and review. It’s recommended to create analysis configurations that do not produce this report if large batches are desired.* For more information on interpreting DNA strand and allele information, see Illumina Knowledge article [How to interpret DNA strand and allele information for Infinium genotyping array data](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001489). ## Locus Summary DRAGEN Array Cloud produces a Locus Summary (locus\_summary.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus: | Field | Description | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Locus\_Name | Locus name from the manifest file. | | Illumicode\_Name | Locus ID from the manifest file. | | #No\_Calls | Number of loci with GenCall scores below the call region threshold. | | #Calls | Number of loci with GenCall scores above the call region threshold. | | Call\_Freq | Call frequency or call rate calculated as follows: #Calls/(#No\_Calls + #Calls) | | A/A\_Freq | Frequency of homozygote allele A calls. | | A/B\_Freq | Frequency of heterozygote calls. | | B/B\_Freq | Frequency of homozygote allele B calls. | | Minor\_Freq | Frequency of the minor allele. | | Gentrain\_Score | Quality score for samples clustered for this locus. | | 50%\_GC\_Score | 50th percentile GenCall score for all samples. | | 10%\_GC\_Score | 10th percentile GenCall score for all samples. | | Het\_Excess\_Freq | Heterozygote excess frequency, calculated as (Observed -Expected)/Expected for the heterozygote class. If $f\_{ab}$ is the heterozygote frequency observed at a locus, and p and q are the major and minor allele frequencies, then het excess calculation is the following: $(f\_{ab} - 2pq)/(2pq + \varepsilon)$ | | ChiTest\_P100 | Hardy-Weinberg p-value estimate calculated using genotype frequency. The value is calculated with 1 degree of freedom and is normalized to 100 individuals. | | Cluster\_Sep | Cluster separation score. | | AA\_T\_Mean | Normalized theta angles mean for the AA genotype. | | AA\_T\_Std | Normalized theta angles standard deviation for the AA genotype. | | AB\_T\_Mean | Normalized theta angles mean for the AB genotype. | | AB\_T\_Std | Standard deviation of the normalized theta angles for the AB genotype. | | BB\_T\_Mean | Normalized theta angles mean for the BB genotypes. | | BB\_T\_Std | Standard deviation of the normalized theta angles for the BB genotypes. | | AA\_R\_Mean | Normalized R value mean for the AA genotypes. | | AA\_R\_Std | Standard deviation of the normalized R value for the AA genotypes. | | AB\_R\_Mean | Normalized R value mean for the AB genotypes. | | AB\_R\_Std | Standard deviation of the normalized R value for the AB genotypes. | | BB\_R\_Mean | Normalized R value mean for the BB genotypes. | | BB\_R\_Std | Standard deviation of the normalized R value for the BB genotypes. | | Plus/Minus Strand | Designated "+" or "-" with respect to the reference genome strand. "U" designates unknown. | ## CN Summary File The sample summary contains per sample key stats for each sample in a batch that contains the following details per sample: * Sample ID * Sample Name * Sample Folder ## Copy Number Batch File The copy number batch summary file (cn\_batch\_summary.csv) shows the total copy number gain, loss, and neutral (CN=2) values for each target region across all the samples in the analysis. Example copy number batch summary file content: `Target Region,Total CN gain,Total CN loss,Total CN neutral` `CYP2A6.exon.1,0,1,47` `CYP2A6.intron.7,0,1,47` `CYP2D6.exon.9,2,4,42` `CYP2D6.intron.2,7,2,39` `CYP2D6.p5,13,2,33` `CYP2E1,2,0,46` `GSTM1,0,42,6` `GSTT1,0,33,15` `SULT1A1,0,0,48` `UGT2B17,0,34,14` `All Target Regions,24,119,337` ## Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Manifest file used to generate GTC is not the same as the manifest file used to generate the CN model. * FASTA files and FASTA index files do not match. For the following scenarios, the software reports messages to the terminal output (as either a warning or an error): * Indel processing for GTC to VCF conversion failed. * The input folder does not contain the required input files. * An input file is corrupt. Examples of such notifications can include the following: | Error | Type | Cause | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Failed to normalize and gencall sample: {sample\_id}, it will be skipped. Error: The given key '{loci\_id}' was not present in the dictionary. | Warning | This generally occurs because of a mismatch between the manifest (bpm) and cluster file (egt) (i.e., the cluster file was generated via a different manifest). To remedy the issue, use the manifest and cluster files intended for use together. | | Reference allele is not queried for locus: {identifier} | Warning | True reference allele does not match any alleles in the manifest. The error is common for MNVs and will be addressed in future versions of the software. | | Skipping non-mapped locus: {identifier} | Warning | Locus has no chromosome position (usually 0) These loci may be used for quality purposes or CNV calling only. | | Skipping intensity only locus: {identifier} | Warning | Similar to non-mapped loci, intensity only probes have applications outside creating variants for SNV VCFs such as CNV calling. | | Skipping indel: {identifier} | Warning | Indel context (deletion/insertion) could not be determined. | | Failed to process entry for record: {identifier} | Warning | Unable to determine reference allele for indel. | | Incomplete match of source sequence to genome for indel: {identifier} | Warning | Indel not properly mapped to the reference genome. | | Failed to combine genotypes due to inconsistencies - exm1068284 (InfiniumII): TT, ilmnseq\_rs1131690890\_mnv (InfiniumII): AA, rs1131690890\_mnv (InfiniumII): AA | Warning | Detailed information about a NoCall ("./.”) in the VCF as a result of combining multiple probes that assay the same variant with conflicting results. The example here is two probes with homozygous REF genotypes (AA) and one probe with homozygous ALT probe (TT) | | Failed to combine genotypes all were No Calls - exm1068284 (InfiniumII): NC, ilmnseq\_rs1131690890\_mnv (InfiniumII): NC, rs1131690890\_mnv (InfiniumII): NC | Warning | Detailed information about a NoCall ("./.”) in the VCF as a result of combining multiple probes that assay the same variant in which all the probes were No Calls. | | Cluster file ({GTC.egt}) is not the same as CN Model Cluster file ({CN\_Model.egt}). | Warning | Cluster file used to generated GTCs used for copy number calling is not the same as was used for the GTCs used during copy number training that created the input CN model. Though CNV model is robust to minor cluster file updates, CNV training should be considered when there are significant updates in the cluster file. To remove the warning, copy number training needs to be re-run with the new GTCs generated via the new cluster file during genotyping, a different CN model with the expected cluster file needs to be used, or different GTCs should be used for copy number calling that were generated using the same cluster file as was used during the generation of the input CN model. | |

{numPassingSamples} sample(s) passed QC.

Requires at least {minPassingSamples} samples to proceed.

| Error | CNV calling is batch dependent and requires a certain number of samples with high-quality to make accurate calls. More high-quality samples need to be added to analysis batch to resolve error. | | Invalid manifest file path {manifestPath} | Error | Application could not find manifest file provided or user error. | | Failed to load cluster file: {e.Message} | Error | Corrupted file or unsupported version. | | System.IO.EndOfStreamException: Unable to read beyond the end of the stream. | Error | Likely failure to read a GTC file, see this [known issue](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes#section-known-issues) for more details on root cause and a workaround | ## Star allele JSON File The star allele JSON file is produced per sample. It contains the fields present in the [star allele CSV file](#star_allele_csv) as well as additional meta data and annotations. Fields included in the star allele JSON header are described below. | Field | Description | | ------------------------- | ------------------------------------------------------------------------------------------------------------- | | softwareVersion | DRAGEN Array software version, e.g. dragena 1.0.0. | | genomeBuild | Genome build, e.g hg38. | | starAlleleDatabaseSources | Public databases with versions used as the sources of the star allele definitions and population frequencies. | | phenotypeDatabaseSources | Public databases with versions used as the sources of the star allele phenotypes. | | mappingFile | The PGx database file used for the star allele calling. | | pgxGuideline | The PGx guidelines used for metabolizer status/phenotype annotations, e.g. CPIC or DPWG | | sampleId | Sentrix barcode and position of the sample. | | locusAnnotations | The star allele call information. | Fields included in the star allele call (locusAnnotations) information are described below. | Field | Description | | --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | gene | The gene symbol. | | callType | ‘Star Allele’ or ‘Variant’ PGx calling type. | | genotype | Most likely star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. More than one solution meeting threshold requirements can be reported. Multiple top solutions are separated by a semi-colon. | | arrayEquivalentAlleles | Star alleles for the given alleleId that are indistinguishable from the reported solution allele given the content of the genotyping array. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotypeDatabaseAnnotation | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), the variant ID (id), the variant rsid (rsid), and the associated star allele IDs (alleleIds).

| | variantNoCalls | Variants that are no calls in the sample SNV VCF | | allelesImpactedByNoCalls | Star alleles in the solution or array equivalent alleles, whose definitions are impacted by no‑call variants. | | candidateSolutions | The set of alternative star allele calling solutions, this is only relevant for genes of the ‘Star Allele’ call type. | | variantsNotCoveredOnArray | All core variants that are not covered on the array for star allele calling for this gene. For star alleles, the field provides an array (list) of variant "id" and impacted "alleleIds" pairs | | allelesTested | Alleles that are covered by the star allele caller. The capability to call star alleles is also dependent on array content coverage and data quality. This field is defined by the array's content and will be the same across all samples. | Fields included in the candidateSolution section, only available for star allele call type, are described below. | Field | Description | | --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | genotype | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotypeDatabaseAnnotation | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | alleles | The composite alleles of the candidate genotype solution. | | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele(s) (alts), Genotype (gt), GenCall score (gs), B-allele frequency (baf), HGVS tag (hgvs), variant RSID (rsid) and the associated star allele IDs (alleleIds).

| | unresolvedVariantSites | All variants not present in the array or not called in the SNV VCF file for the star allele solution. The field provides an array (list) of missing variants. | | arrayEquivalentAlleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Star-Allele Id: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | backgroundAllele | Background star-allele haplotype for a given structural variant in the solution | | copyNumberRegions | Gene regions for the copy numbers listed in CopyNumberSolution. | | copyNumberSolution | Estimated copy number for each gene region listed in CopyNumberRegions | Example of JSON file content: ```json { "softwareVersion": "dragena 1.4.0", "genomeBuild": "38", "starAlleleDatabaseSources": [ "PharmVar Version: 6.1", "PharmGKB Database Version: Snapshot-2024.05.16", "UGT Alleles Nomenclature: 2010.12.21", "The Human Cytochrome P450 (CYP) Allele Nomenclature Database, July 2024" ], "phenotypeDatabaseSources": [ "CPIC Database Version: 1.38.0", "DPWG Database Version: June 2023" ], "mappingFile": "DAv1.4.0-pgx-mapping-sha.b164b4aa02f34cc9b72aba020c3a8735cb8e4421.zip", "pgxGuideline": "CPIC", "sampleId": "207895980079_R09C01", "locusAnnotations": [ { "gene": "CYP2D6", "callType": "Star Allele", "genotype": "*27/*36x2+*83+*10", "arrayEquivalentAlleles": [ { "alleleId": "*27", "ArrayEquivalentAlleles": "" }, { "alleleId": "*36x2+*83+*10", "ArrayEquivalentAlleles": "" } ], "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2D6 Indeterminate", "qualityScore": "0.5963", "rawScore": "0.9981", "supportingVariants": [ { "CN:": "1", "chrom:": "22", "pos:": "42126938", "ref:": "C", "alts": "T", "gt": "0/1", "gs": "0.1205", "baf": "0.31852543", "hgvs": "NC_000022.11:g.42126938C>T", "rsid": "rs769157652", "alleleIds": "*27" }, { "CN:": "3", "chrom:": "22", "pos:": "42126611", "ref:": "C", "alts": "G", "gt": "0/1", "gs": "0.1776", "baf": "0.5491097", "hgvs": "NC_000022.11:g.42126611C>G", "rsid": "rs1135840", "alleleIds": "*36x2+*83+*10" }, { "CN:": "3", "chrom:": "22", "pos:": "42130692", "ref:": "G", "alts": "A", "gt": "0/1", "gs": "0.5194", "baf": "0.72425556", "hgvs": "NC_000022.11:g.42130692G>A", "rsid": "rs1065852", "alleleIds": "*36x2+*83+*10" } ], "variantNoCalls": "", "allelesImpactedByNoCalls": "", "candidateSolutions": [ { "rank": 1, "genotype": "*27/*36x2+*83+*10", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2D6 Indeterminate", "qualityScore": 0.5963, "rawScore": 0.9981, "alleles": [ { "alleleName": "*27", "supportingVariants": [ { "CN:": "1", "chrom:": "22", "pos:": "42126938", "ref:": "C", "alts": "T", "gt": "0/1", "gs": "0.1205", "baf": "0.31852543", "hgvs": "NC_000022.11:g.42126938C>T", "rsid": "rs769157652" } ], "unresolvedVariantSites": [], "arrayEquivalentAlleles": "", "backgroundAllele": "" } ], "variantNoCalls": "", "allelesImpactedByNoCalls": "", "variantChange": "", "copyNumberRegions": "p5, intron.2, exon.9", "copyNumberSolution": "5, 5, 2" } ], "variantsNotCoveredOnArray": [ { "hgvs": "NC_000022.11:g.42126715C>T", "rsid": "rs760940331", "alleleIds": "*155" }, { "hgvs": "NC_000022.11:g.42126962C>G", "rsid": "rs1602566413", "alleleIds": "*169" }, { "hgvs": "NC_000022.11:g.42126982_42126983del", "rsid": "rs757396767", "alleleIds": "*124" }, { "hgvs": "NC_000022.11:g.42127605T>G", "rsid": "rs141824015", "alleleIds": "*158" }, { "hgvs": "NC_000022.11:g.42128201dup", "rsid": "rs368858603", "alleleIds": "*161" }, { "hgvs": "NC_000022.11:g.42128326GGA[1]", "rsid": "rs777691989", "alleleIds": "*166" }, { "hgvs": "NC_000022.11:g.42128340G>A", "rsid": "rs140900383", "alleleIds": "*152" }, { "hgvs": "NC_000022.11:g.42128796del", "rsid": "", "alleleIds": "*92" }, { "hgvs": "NC_000022.11:g.42128820dup", "rsid": "rs763609904", "alleleIds": "*36+*150, *150" }, { "hgvs": "NC_000022.11:g.42128864C>T", "rsid": "rs755518310", "alleleIds": "*168" }, { "hgvs": "NC_000022.11:g.42128926T>C", "rsid": "rs1459127426", "alleleIds": "*151" }, { "hgvs": "NC_000022.11:g.42128932C>T", "rsid": "rs556882139", "alleleIds": "*165" }, { "hgvs": "NC_000022.11:g.42129044G>A", "rsid": "rs201006451", "alleleIds": "*157" }, { "hgvs": "NC_000022.11:g.42129130C>G", "rsid": "rs1058164", "alleleIds": "*1+*90, *1, *1x2+*83, *1x2, *1x3N, *1x3, *2, *2x2, *2x3N, *2x3, *4, *4.013, *4.013+*4, *4.013+*4x2, *4.013x2, *4.013x2+*4, *4M, *4x2, *4x3N, *4x3, *8, *10x2, *10x3N, *10x3, *10, *11, *12, *13+*1x2, *13+*1, *13+*2x2, *13+*2, *13+*68+*4, *13+*68x2+*4, *14, *17x2, *17, *19, *20, *21, *28x2, *28, *29x2, *29, *30, *31, *32, *35x2, *35, *36+*10x2, *36+*10, *36+*150, *36x2+*10x2, *36x2+*10, *36x2+*83+*10, *36x3N+*10, *37, *39, *40, *41x2, *41x3N, *41x3, *41, *42, *45x2, *45x3, *45, *46, *47, *49, *51, *52, *54, *55, *56, *58, *59, *64, *65, *68+*2, *68+*45, *68+*4, *68x2+*45, *68x2+*4, *69, *70, *72, *73, *84, *85, *87, *88, *94, *95, *98, *99, *100, *101, *102, *103, *104, *105, *111, *117, *121, *123, *125, *126, *128, *129, *132, *133, *135, *136, *138, *141, *142, *146, *146x2, *147, *148, *149, *150, *154, *155, *156, *157, *158, *159, *160, *161, *162, *163, *164+*2, *164, *165, *166, *171, *172" }, { "hgvs": "NC_000022.11:g.42129156A>G", "rsid": "rs376636053", "alleleIds": "*163" }, { "hgvs": "NC_000022.11:g.42129740T>C", "rsid": "", "alleleIds": "*167" }, { "hgvs": "NC_000022.11:g.42129753C>G", "rsid": "rs563185985", "alleleIds": "*172" }, { "hgvs": "NC_000022.11:g.42129770G>T", "rsid": "rs28371706", "alleleIds": "*82" }, { "hgvs": "NC_000022.11:g.42129887A>C", "rsid": "rs1456026511", "alleleIds": "*128" }, { "hgvs": "NC_000022.11:g.42130638G>A", "rsid": "rs536109057", "alleleIds": "*156" }, { "hgvs": "NC_000022.11:g.42130670G>A", "rsid": "rs373243894", "alleleIds": "*159" } ], "allelesTested": "*5, *68, *13, *1, *1+*90, *1x2, *1x2+*83, *1x3, *1x3N, *2, *2x2, *2x3, *2x3N, *3, *3x2, *4, *4.013, *4.013+*4, *4.013+*4x2, *4.013x2, *4.013x2+*4, *4M, *4x2, *4x3, *4x3N, *6, *6x2, *7, *8, *9x2, *9, *10x2, *10x3N, *10x3, *10, *11, *12, *13+*1, *13+*1x2, *13+*2, *13+*2x2, *13+*68+*4, *13+*68x2+*4, *14, *15, *17x2, *17, *18, *19, *20, *21, *22, *23, *24, *25, *26, *27x2, *27, *28x2, *28, *29x2, *29, *30, *31, *32, *33, *34, *35x2, *35, *36, *36+*10x2, *36+*10, *36+*150, *36x2, *36x2+*10x2, *36x2+*10, *36x2+*83+*10, *36x3, *36x3N+*10, *37, *38, *39, *40, *41x2, *41x3N, *41x3, *41, *42, *43x2, *43, *44, *45x2, *45x3, *45, *46, *47, *48, *49, *50, *51, *52, *53, *54, *55, *56, *58, *59, *60, *62, *64, *65, *68+*2, *68+*4, *68+*45, *68x2+*4, *68x2+*45, *69, *70, *71, *72, *73, *74, *75, *81, *82, *83, *84, *85, *86, *87, *88, *89, *90, *91, *92, *93, *94, *95, *96, *97, *98, *99, *100, *101, *102, *103, *104, *105, *106, *107, *108, *109, *110, *111, *112, *113, *114, *115, *116, *117, *118, *119, *120, *121, *122, *123, *124, *125, *126, *127, *128, *129, *130, *131, *132, *133, *134, *135, *136, *137, *138, *139, *140, *141, *142, *143, *144, *145, *146, *146x2, *147, *148, *149, *150, *151, *152, *153, *154, *155, *156, *157, *158, *159, *160, *161, *162, *163, *164+*2, *164, *165, *166, *167, *168, *169, *170, *171, *172" } ] } ``` ### Guidance on alternative star-allele results Typically, the star allele solution with highest quality score is accepted as the final genotype (i.e. star allele diplotype) for the PGx locus. In rare cases, there are lower ranked star allele solutions with quality scores no less than 50% of the highest quality score, these lower ranked solutions are considered feasible and they are all listed in the genotype field of the locus annotation of the PGx gene in the PGx JSON file. Alternative solutions should also be considered if there are supporting variants for those solutions with low (less than 0.15) GS scores. The clustering of low GS scoring supporting variants should also be evaluated for cluster quality and any potential cluster shift. ## Cytogenetics Annotation JSON File DRAGEN Array produces one cytogenetics annotation JSON (\*.json) per sample to report more sample-level, chromosome-level, and event-level metrics and annotations. Example of JSON file content: ```json { "annotateDb": "CytoAnnotateData_DAv1.2.0.zip", "softwareVersion": "dragena 1.4.0 Cyto", "referenceGenome": "file://genome.fa", "annotationType": "Constitutional", "genomeBuild": "hg19", "databaseSources": "RefSeq (Version: GCF_000001405.40-RS_2023_10; Release Date: 2023-10-07),Ensembl (Version: 112; Release Date: 2024-05-14)", "iscnVersion": "ISCN 2020", "sampleId": "209033880028_R02C01", "sampleName": "VCYTO566", "gcCorrect": true, "minDelProbes": 10, "minDupProbes": 10, "minLOHProbes": 500, "minDelSize": "25kb", "minDupSize": "50kb", "minLOHSize": "3000kb", "minGainlohSize": "500kb", "minMdelSize": "25kb", "minMdupSize": "50kb", "minMlohSize": "3000kb", "minQual": 20, "minGainlohProbes": 300, "minMdelProbes": 10, "minMdupProbes": 10, "minMlohProbes": 500, "overallPloidy": 1.953, "callRate": 0.9941418170928955, "logRDev": 0.15898671746253967, "medianLogRDev": 0.04553778046484919, "bafDev": { "AA": 0.016101494796886523, "AB": 0.04407375828026317, "BB": 0.016792803588064394 }, "numLOHOver1M": 2, "numLOHOver8M": 1, "totalSizeLOHOver1M": 16992771, "copyNumberMedian": 2.0, "percentLOH": "0.55%", "sexEstimate": "Male", "traditionalNomenclature": "del(1)(p36.33p36.33),del(1)(q31.3q31.3),del(3)(q26.32q26.32),dup(4)(p16.3p16.3),dup(9)(p24.3q21.11),del(17)(q21.31q21.31),dup(17)(q25.3q25.3)", "microarrayNomenclature": "arr[GRCh37] del(1)(p36.33p36.33)(1567924_1632825x1),1q31.3(196716621_196801989)x1,3q26.32(175887314_175916348)x1,4p16.3(3562944_3627954)x3,9p24.3q21.11(14326_69444970)x3,14q11.1q11.2(19021424_20422182)x3 hmz,16p11.2q11.2(31382412_46974423)x2 hmz,17q21.31(44225485_44342404)x1,17q25.3(81083660_81152211)x3", "chromosomeAnnotations": [ { "id": "chr1", "size": 249250621, "percentHet": "11.5671%", "hasMosaicism": false, "lrrMedian": 0.00040547107346355915, "lrrMean": -0.005009292215779495, "lrrDev": 0.05427333144723565, "numLOHOver1M": 0, "numLOHOver8M": 0, "totalSizeLOHOver1M": 0, "percentLOH": "0%", "copyNumberMedian": 2.0, "copyNumberMean": 1.992449897296397, "minLogRRatio": -2.3587277163751423, "maxLogRRatio": 0.2589639766374603, "medianMosaicFraction": ".", "numberDel": 2, "numberDup": 0, "numberLOH": 0, "numberGainLOH": 0, "numberMosaic": 0 }, ... ], "locusAnnotations": [ { "id": "DEL:1:1567924:1632825", "chrom": "chr1", "start": 1567923, "end": 1632825, "callType": "DEL", "mosaicState": false, "mosaicFraction": ".", "copyNumber": 1, "qualityScore": 60.0, "size": 64902, "effectiveSize": 64902, "probeCount": 56, "percentHet": "0%", "lrrMedian": -0.31568841007538134, "lrrDev": 0.16213641913238874, "bafDev": 0.400400865592166, "startCytoBand": "1p36.33", "endCytoBand": "1p36.33", "traditionalNomenclature": "del(1)(p36.33p36.33)", "microarrayNomenclature": "arr[GRCh37] del(1)(p36.33p36.33)(1567924_1632825x1)", "geneCount": 7, "genes": [ "MMP23B", "CDK11B", "RP11-345P4.10", "SLC35E2B", "RP11-345P4.7", "RP11-345P4.6", "MMP23A" ] }, ... ] } ``` The fields in the annotation JSON for each sample are described as follows. | Field | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | annotateDb | File name of the database annotation file. | | softwareVersion | Version of DRAGEN Array used for the analysis. | | referenceGenome | File name of the reference genome. | | annotationType | Integer representing annotation methodology where 0=Constitutional and 1=Oncology. | | genomeBuild | Genome build e.g. hg19, hg38. | | databaseSources | Release versions of annotation data. | | iscnVersion | Release date of ISCN formatting used. | | sampleId | ID string assigned to the sample. | | sampleName | See [Sample Name Determination](/product-guides/input-files#sample-name-determination) | | gcCorrect | Boolean indicating whether GC correction was enabled. | | minDelProbes | Deletions must contain this many probes to be reported. | | minDupProbes | Duplications must contain this many probes to be reported. | | minLOHProbes | LOH variants must contain this many probes to be reported. | | minDelSize | Minimum length filter for reporting deletions in kilobases (kb). | | minDupSize | Minimum length filter for reporting a duplication in kb. | | minLOHSize | Minimum length filter for reporting a loss-of-heterzygozity (LOH) variant in kb. | | minQual | Minimum quality score filter for reporting a variant. | | overallPloidy | Arithmetic mean of the ploidy across the genome. This value accounts for the length of all variant calls. The baseline ploidy value without any variants will differ by sex. | | callRate | Frequency of expected calls i.e. #Calls/(#No\_Calls + #Calls). | | logRDev | Standard deviation of the Log R ratio values for all probes. | | bafDev | Standard deviation of the B allele frequency values for each assigned genotype (AA/AB/BB). | | numLOHOver1M | Count of LOH (including GAINLOH) variants detected > 1 Mbp in length. | | numLOHOver8M | Count of LOH (including GAINLOH) variants detected > 8 Mbp in length. | | totalSizeLOHOver1M | Cumulative length of all detected LOH (including GAINLOH) variants > 1 Mbp in length. | | copyNumberMedian | Length-normalized genome-wide median copy number value. Copy number values are assigned to contiguous segements of variable size in the genome by the algorithm. The length-weighted copy numbers of each variant are aggregated to calculate the overall median. | | percentLOH | Percent of the genome comprised of LOH variants. | | sexEstimate | Detected sex of the sample. | | traditionalNomenclature | Simplified ISCN format designation for all detected variants in the sample. | | microarrayNomenclature | ISCN format designation for all detected variants in the sample. | | chromosomeAnnotations | Counts of each type of variant detected per chromosome, including mosaic calls. | | locusAnnotations | Locus level statistics (see additional table for locus-level statistics). | The fields for each chromosome under the chromosomeAnnotations field of the Cyto annotation JSON are described below. | Field | Description | | -------------------- | ------------------------------------------------------------------------------------- | | id | Chromosome name. | | size | Chromosome size. | | percentHet | Percent of probes in the chromosome called as heterzygous i.e. AB. | | hasMosaicism | Boolean indicating presence of any mosaic variant on the chromosome. | | lrrMedian | Median log R ratio value of the probes within the chromosome. | | lrrMean | Mean log R ratio value of the probes within the chromosome. | | lrrDev | Standard deviation of the log R ratio values of the probes within the chromosome. | | numLOHOver1M | Count of LOH variants (excluding GAINLOH) detected > 1 Mbp in length. | | numLOHOver8M | Count of LOH variants (excluding GAINLOH) detected > 8 Mbp in length. | | totalSizeLOHOver1M | Cumulative length of all detected LOH (excluding GAINLOH) variants > 1 Mbp in length. | | percentLOH | Percent of the genome comprised of LOH variants. | | copyNumberMedian | Copy number median of the chromosome. | | copyNumberMean | Copy number mean of the chromosome. | | minLogRRatio | Minimum Log R ratio of the chromosome. | | maxLogRRatio | Maximum Log R ratio of the chromosome. | | medianMosaicFraction | Median mosaic fraction of mosaic events on the chromosome. | | numberDel | Number of deletion events on the chromosome. | | numberDup | Number of duplication events on the chromosome. | | numberLOH | Number of LOH events on the chromosome. | | numberGainLOH | Number of GAIN LOH events on the chromosome. | | numberMosaic | Number of mosaic events on the chromosome. | The fields within each variant (CNV/LOH event) under the locusAnnotations field of the Cyto annotation JSON are described below. | Field | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | id | Unique variant ID containing variant type, chromosome and the start and end positions. | | chrom | Chromosome of the variant. | | start | Variant start position. | | end | Variant end position. | | callType | Variant class (DEL, DUP, LOH). | | mosaicState | Boolean indicating whether the locus is a mosaic variant. | | copyNumber | Copy number of the locus. | | qualityScore | Phred-scaled score of the variant call quality. | | size | Length of the variant. | | effectiveSize | Gap-excluded length of the variant. A gap is defined when probe spacing is more than 150 times the median probe spacing. In that case, the gap is replaced with the median probe spacing. | | probeCount | Number of probes contained in the called variant region. | | percentHet | Percent of probes in the region call as heterzygous i.e. AB. | | lrrMedian | Median log R ratio value of the probes within the variant. | | lrrDev | Standard deviation of the log R ratio values of the probes within the variant. | | bafDev | Standard deviation of the B allele frequency values of the probes within the variant. | | startCytoBand | Cytoband in which the variant starts. | | endCytoBand | Cytoband in which the variant ends. | | traditionalNomenclature | Simplified ISCN format designation for the detected variant. | | microarrayNomenclature | ISCN format designation for the detected variant. | | geneCount | Count of annotated genes within the variant region. | | genes | List of names of all annotated genes within the variant region. | The traditionalNomenclature field is used to describe individual and cumulative copy number variants at a coarse resolution. They show gains (dup) and losses (del) according to their chromosome number, arm (p or q), and band (e.g., p36.13). The microarrayNomenclature field follows the Comparative Genomic Hybridization or SNP array conventions. Values are prefixed with arr\[] to indicate array data as the source, along with the genome build e.g. GRCh38. These data can more precisely describe the location (start\_end in bp) and copy number (x1 for loss, x3 for gain, etc). The genes list field for each variant include those with transcript coordinates that intersect with the described variant. The values included are a combination of HGNC gene symbols taken from NCBI RefSeq database (e.g. [TBC1D3I](https://www.ncbi.nlm.nih.gov/gene/102724862)), as well as the subset of Ensembl gene accessions unmatched to a gene symbol ([ENSG00000278395](https://useast.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000278395;r=17:36241701-36241806;t=ENST00000612652)). ## TBI Index File The TBI (TABIX) index file is associated with the bgzipped VCF files. It allows for data line lookup in VCF files for quick data retrieval. The format is a tab-delimited genome index file developed by Samtools as part of the HTSlib utilities. For more information, visit the [Samtools](http://www.htslib.org/doc/tabix.html) website. ## Methylation Control Probe Output File The software produces a control probe output file ({BeadChipBarcode}\_{Position}\_ctrl.tsv.gz) per sample that includes the raw methylated and unmethylated values for each control probe. Each control probe has an address, type, color channel, name, and probe ID. It also provides the raw signal for methylated green (MG), methylated red (MR), unmethylated green (UG) and unmethylated red (UR). The file can help identify which probes are available on a given BeadChip. ## Methylation CG Output File The software produces a CG output file ({BeadChipBarcode}\_{Position}\_cgs.tsv.gz) per sample that includes beta values, m-values and detection p-values for each CG site. Beta values measure methylation levels in a linear fashion for easy interpretation. Unmethylated probes are close to zero and methylated probes are close to 1. M-values are a log transformed beta value which provides a more representative measure of methylation. Detection p-values measure the likelihood that the signal is background noise. It is recommended that p-value >0.05 are excluded from analysis as they are likely background noise. see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note for further detail on calculation of these metrics. ## Methylation Sample QC Summary Files The software produces methylation sample QC summary in .xlsx and .tsv file formats (sample\_qc\_summary.xlsx and sample\_qc\_summary.tsv) per analysis batch, which provides per sample QC data for all samples in the batch. The QC summary provides details on 21 controls metrics (see tables below), which are computed in same way as in the BeadArray Controls Reporter software from Illumina. In addition, it provides average red and green raw and normalized signals, time of scanning, proportion of probes passing, overall sample pass/fail status, and the failure codes for control metrics that did not pass. The sample pass status is defined as the passing of all 21 control metrics. The QC summary .xlsx file further highlights failing parameters for easy viewing. The QC summary files contain the following fields: | Field | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Sentrix\_ID | 12-digit BeadChip Barcode associated with the sample. | | Sentrix\_Position | Row and column on the BeadChip ie R01C01 | | Sample\_ID | Optional field that can be indicated using IDAT Sample Sheet | | User Defined Meta Data | Optional field(s) that can be indicated using IDAT Sample Sheet. Any number of fields indicated will appear in this output file. | | restoration |
  • The default threshold is 0.
  • If using the FFPE DNA Restore Kit, the restoration control identifies success of the FFPE restoration chemistry. Change the threshold from 0 to 1 if the FFPE DNA Restore Kit was used.
  • The green channel intensity is higher than Background. Therefore, the metric provided is the Green Channel Intensity/Background.
| |

staining\_green

staining\_red

|
  • Staining controls are used to examine the efficiency of the staining step in both the red and green channels. These controls are independent of the hybridization and extension step.
  • The green channel shows a higher signal for biotin staining when compared to biotin background, whereas the red channel shows higher signal for DNP staining when compared to DNP background.
  • The metric provided for green is the (Biotin High value)/ (Biotin Bkg) and the metric provided for red is (DNP High value)/(DNP Bkg value)
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

extension\_green

extension\_red

|
  • Extension controls test the extension efficiency of A, T, C, and G nucleotides from a hairpin probe, and are therefore sample independent.
  • In the green channel, the lowest intensity for C or G is always greater than the highest intensity for A or T.
  • The metric provided is the (lowest of the C or G intensity)/ (highest of A or T extension) for a single sample.
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

hybridization\_high\_medium

hybridization\_medium\_low

|
  • Hybridization controls test the overall performance of the Infinium Assay using synthetic targets instead of amplified DNA. These synthetic targets complement the sequence on the array, allowing the probe to extend on the synthetic target as a template. Synthetic targets are present in the Hybridization Buffer at 3 levels, monitoring the response from high-concentration (5 pM), medium concentration (1 pM), and low concentration (0.2 pM) targets. All bead type IDs result in signals with various intensities, corresponding to the concentrations of the initial synthetic targets.
  • The value for high concentration is always higher than medium and the value for medium concentration is always higher than low.
  • The metric provided is the value of high/medium and the value of medium/low.
  • The default thresholds are 1. Do not change the default threshold.
| |

target\_removal1

target\_removal2

|
  • Target removal controls test the efficiency of the stripping step after the extension reaction. In contrast to allele-specific extension, the control oligos are extended using the probe sequence as a template. This process generates labeled targets. The probe sequences are designed such that extension from the probe does not occur. All target removal controls result in low signal compared to the hybridization controls, indicating that the targets were removed efficiently after extension. Target removal controls are present in the Hybridization Buffer.
  • The Background for the same sample is close to or larger than either control.
  • The metric provided is Background/Control Intensity.
  • The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion1\_green

bisulfite\_conversion1\_background\_green

bisulfite\_conversion1\_red

bisulfite\_conversion1\_background\_red

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium I probe design and allele-specific single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "C" (Converted) probes matches the converted sequence and get extended. If the sample has unconverted DNA, the "U" (Unconverted) probes get extended. There are no underlying C bases in the primer landing sites, except for the query site itself.

  • The calculation is done in both the green and red channels separately to provide 2 unique sets of values:

    • Green Channel

      • Lowest value of C1 or C2 / Highest value of U1 or U2. The default threshold is 1. This value can be increased for some scanners.
      • Background/(U1, or U2). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.

    • Red Channel

      • Lowest value of C3, 4, or 5 / Highest value of U3, 4, or 5. The default threshold is 1. This value can be increased for some scanners.
      • Background /(Highest value of U4, U5, or U6). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion2

bisulfite\_conversion2\_background

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium II probe design and single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "A" base gets incorporated and the probe has intensity in the red channel. If the sample has unconverted DNA, the "G" base gets incorporated across the unconverted cytosine, and the probe has elevated signal in the green channel.
  • The calculation is done using both channels for 1 set of numbers returned.

  • The following metrics are provided:

    • (Lowest of red C 1, 2, 3, or 4) / (Highest of green C 1, 2, 3, or 4). The default threshold is 1. This value can be increased for some scanners.
    • Background/(Highest C1, C2, C3, or C4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

specificity1\_green

specificity1\_red

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor allele-specific extension for Infinium I probes. The methylation status of a particular cytosine is carried out following bisulfite treatment of DNA by using query probes for unmethylated and methylated state of each CpG locus. In assay oligo design, the A/T match corresponds to the unmethylated status of the interrogated C, and G/C match corresponds to the methylated status of C. G/T mismatch controls check for nonspecific detection of methylation signal over unmethylated background. PM controls correspond to A/T perfect match and give high signal. MM controls correspond to G/T mismatch and give low signal.
  • The metrics provided are the ratio of the lowest PM/highest MM in each channel.
  • The default threshold is 1. Do not change the default threshold.
| |

specificity2

specificity2\_background

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor extension specificity for Infinium II probes and check for potential nonspecific detection of methylation signal over unmethylated background. Specificity II probes incorporate the "A" base across the nonpolymorphic T and have intensity in the Red channel. If there was nonspecific incorporation of the "G" base, the probe has elevated signal in the Green channel.

  • The following metrics are provided:

    • (Lowest intensity of S1, S2, or S3 red) / (Highest intensity of S1, S2, or S3 green). The default threshold is 1. Do not change the default threshold.
    • Background/(Highest intensity S1, S2, S3, or S4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

nonpolymorphic\_green

nonpolymorphic\_red

|
  • Nonpolymorphic controls test the overall performance of the assay, from amplification to detection, by querying a particular base in a nonpolymorphic region of the genome. They let you compare assay performance across different samples. One nonpolymorphic control has been designed for each of the 4 nucleotides (A, T, C, and G).
  • In the green channel, the lowest intensity of C or G is always greater than the highest intensity of A or T.
  • The metric provided is the (lowest intensity for C or G) /(highest intensity for A or T) for a single sample.
  • The default threshold is 5. This value can be increased for some scanners.
| |

avg\_green\_raw

avg\_red\_raw

|
  • Average green and red raw signal for the given sample.
| |

avg\_green\_norm

avg\_red\_norm

|
  • Average green and red signal after dye bias correction and noob normalization for the given sample.
| | ScanTime |
  • The date (MM/DD/YY) and time (HH:MM) that the sample was scanned by the iScan system.
| | NProbes |
  • Number of probes on the BeadChip, including SNP and CG probes
| | NPassDetection |
  • Number of probes on the BeadChip that passed detection p-value at the threshold defined.
| | prop\_probes\_passing |
  • The proportion of probes passing defined as the number of probes passing detection p-value divided by the total number of probes on the BeadChip.
| | passQC |
  • 1 = sample passed all QC metrics for the thresholds defined
  • 0 = sample did not pass all QC metrics for the thresholds defined
| | failCodes |
  • The list of parameters that failed QC for the thresholds defined.
| The control metrics in the QC summary files are calculated as follows. The default value for background correction offset (x) of 3,000 can be modified and applies to all background calculations indicated with (bkg + x). Note that the table uses default thresholds for EPIC arrays as an example, and the default thresholds change with the methylation arrays. See section [Threshold Adjustment](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc#section-methylation-qc-threshold-adjustment) for additional details.
ControlCalculationAdditional Information
Restoration Green > bkg(Green/(bkg+x))> 0
  • If using the FFPE Restore kit, change the default threshold from 0 to 1.
  • bkg = Extension Green highest A or T intensity

Staining Green

Biotin High > Biotin Bkg

(High/Biotin Bkg) > 5

Staining Red

DNP High > DNP Bkg

(High/DNP Bkg) > 5
Extension Green Lowest CG/Highest AT(C or G/A or T) > 5Green channel—Lowest C or G intensity is used; highest A or T intensity is used.

Extension Red

Lowest AT/Highest CG

(A or T/C or G) > 5Red channel—Lowest A or T intensity is used; highest C or G intensity is used.
Hybridization Green High > Medium > Low(High/Med) > 1
(Med/Low) > 1
Target Removal Green ctrl 1 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity
Target Removal Green ctrl 2 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity

Bisulfite Conversion I Green

C1, 2 > U1, 2

(C/U) > 1
  • Lowest C intensity is used. Highest U intensity is used.

Bisulfite Conversion I Green

U ≤ bkg

((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Green channel—bkg = Extension Green highest AT
Bisulfite Conversion I Red C3, 4, 5 > U3, 4, 5(C/U) >1
  • Lowest C intensity is used. Highest U intensity is used.
Bisulfite Conversion I Red U ≤ bkg((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Red Channel—bkg = Extension Red highest CG
Bisulfite Conversion II C Red > C Green(C Red/ C Green) > 1
  • For MSA arrays, the default is 0.5
  • Lowest C Red intensity is used. Highest C Green intensity is used.
Bisulfite Conversion II C green ≤ bkg((bkg + x)/C Green) > 1
  • For MSA arrays, the default is 0.5
  • Highest C Green intensity is used.
  • Green channel—bkg = Extension Green highest AT
Specificity I Green PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used
Specificity I Red PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used

Specificity II

S Red > S Green

(S Red/ S Green) > 1
  • Lowest S Red intensity is used. Highest S Green intensity is used.

Specificity II

S Green ≤ bkg

((bkg + x)/ S green) > 1
  • bkg = Extension Green highest A or T intensity
  • Highest S Green intensity is used.
Nonpolymorphic Green Lowest CG/ Highest AT(C or G/ A or T) > 5
  • Lowest C or G intensity is used; highest A or T intensity is used
  • For MSA arrays, the default threshold is 2.5
Nonpolymorphic Red Lowest AT/ Highest CG(A or T/ C or G) >5
  • Lowest A or T intensity is used; highest C or G intensity is used
  • For MSA arrays, the default threshold is 3
## Methylation Sample QC Summary Plots The software produces methylation sample QC summary plots (sample\_qc\_summary.pdf) per analysis batch which provides visual depictions of two QC summary plots for quick visual review. The file contains the following control plots: | Control Plot | Description | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Proportion of Probes Passing Threshold | Histogram of the proportion of probes passing the p-value detection threshold. Samples passing QC are shown in one color, and samples failing QC are shown in another color. | | Principal Component Analysis (PCA) | Uses beta values for all analytical probes to compare samples. Principal component analysis (PCA) is applied to the beta values to reduce the dimensionality of the data to two “principal components” that reflect the most variation across samples. If more than 100 samples are used in the analysis, a random subset of 10,000 probes are used for the PCA analysis to reduce computational burden. PCA control plot assigns unique colors to each sample group defined by the IDAT Sample Sheet. If no groups were assigned, all samples will appear the same color. Sample groups may cluster together and can be used to explain some of the variation. Coordinates used to plot each sample in the PCA control plot are provided in the pcs.tsv.gz output file (see below). | ## Methylation Principal Component Summary The software produces a methylation principal component summary file (pcs.tsv.gz) per analysis batch which provides principal component data for each sample within the batch. This can be used to identify the specific samples associated with points on the PCA control plot within the Methylation Sample QC Control Plots output file. The files contain the following fields: | Field | Description | | --------------------- | -------------------------------------------------------------------------------------------------------------------- | | blank | BeadChip Barcode and Position ie 123456789101\_R01C01 | | principal component 1 | The variable of the first axis for the Principal Component Analysis | | principal component 2 | The variable of the second axis for the Principal Component Analysis | | Sample\_Group | Sample group defined by the user in the IDAT Sample Sheet. If no sample group was defined, all samples will show NA. | ## Methylation Manifest Files The software produces two methylation manifest files 1. Manifest in Sesame format (probes.csv) 2. Additional information for control probes (controls.csv) The probes.csv file has the following columns: | Field | Description | | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | | U | This is corresponds to the AddressA\_ID column in the standard Illumina manifest format. | | M | This corresponds to the AddressB\_ID column in the standard Illumina manifest format. | | col | This is the color channel for Infinium I probes (R/G). For Infinium I probes, this column will be NA. | The controls.csv file has the following columns: | Field | Description | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Address | The address of the probe | | Type | The control probe type | | Color\_Channel | A color used to denote certain control probes in legacy software | | Name | A human readable identifier for certain control probes | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | ## Methylation Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Missing IDATs or manifest * Incorrect sample sheet formatting * Duplicate BeadChip Barcode and Position within the sample sheet * Missing control or assay probes * Missing required columns in the manifest * Unable to compute certain metrics Examples of such notifications can include the following: | **Log** | **Error** | **Type** | **Cause** | | ----------------------------------- | ---------------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | write\_samplesheet.log | No IDATs found | Error | No IDATs provided for analysis | | format\_samplesheet.log | No samples in sample sheet | Error | No samples in user’s sample sheet input | | format\_samplesheet.log | Sample sheet not correctly formatted | Error | Sample sheet is not in CSV format or header lines do not start with “<” | | format\_samplesheet.log | beadChipName and sampleSectionName columns are required for the sample sheet. | Error | Sample sheet does not contain required columns: beadChipName and sampleSectionName. | | format\_samplesheet.log | Warning: \ samples have duplicate Sample\_ID | Warning | X lines in the sample sheet have duplicate \\_\. Duplicates are dropped from analysis. | | convert\_manifest\_ilmn\_sesame.log | Missing control probes in manifest | Error | Missing “\[Controls]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Probe section not found | Error | Missing “\[Assay]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Missing required columns: IlmnID, AddressA\_ID, AddressB\_ID, Color\_Channel | Error | Missing one of required columns in Assay section of manifest | | convert\_manifest\_ilmn\_sesame.log | Controls not formatted correctly. Must have 4 columns (Address,Type,Color\_Channel,Name) | Error | Missing one of required columns in Control section of manifest | | run\_sesame\_gs.log | Missing sample: \ | Error | Missing idats for a particular sample | | run\_sesame\_gs.log | No scan time available | Warning | No scan time in idat | | run\_sesame\_gs.log | Prep failed | Error | Dye bias correction or noob failure for sample | | run\_sesame\_gs.log |

Warning: missing control probe types

\

| Warning | Missing control probe types to compute a BACR metric. Metric will be set to NA. | | run\_sesame\_gs.log |

Warning: missing control probe names

\

| Warning | Missing control probes to compute a BACR metric. Metric will be set to NA. | | qc.log | No features, skipping PCA plot | Warning | No common betas found in all samples. This may occur if a sample has no signal intensity in the IDAT files. | ## QC metrics files The DRAGEN Array CLI produces QC metrics CSV files per analysis batch to support quality review and downstream QC reporting. These files capture raw control-probe intensities and summarized per-sample control QC metrics. These files can be generated for any array-type using the `qc call` command, and they are also generated automatically with any `genotype call` command for genotyping arrays, including in cloud pipelines that use this command. | File Name | Description | | -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `controls.raw_metrics.csv` | Raw control-probe intensity file with one row per control probe measurement per sample. This file includes fields such as sample ID, control category, beadtype, control name, color channel, and green/red intensity values. It is used to support probe-level QC review and visualizations in the QC report. | | `controls.qc_metrics.csv` | Summarized per-sample control QC metrics file with one row per sample. This file includes sample metadata and derived control QC metrics such as staining, extension, hybridization, target removal, stringency, non-specific binding, non-polymorphic, and other array-type-specific QC metrics. It is used by the QC report for sample-level QC evaluation. | These files are the required control-metrics inputs for the local `qc report` command. The `controls.raw_metrics.csv` file contains the following fields. | Field | Description | | -------------- | -------------------------------------------------------------------------------------------------------- | | SampleId | Unique sample identifier. | | Category | High-level control category, such as Staining, Extension, Hybridization, Stringency, or Non-Polymorphic. | | Beadtype | Control probe beadtype identifier from the array design. | | Control | Human-readable control probe name, such as `Biotin (High)`, `DNP (Bgnd)`, or `String (PM)`. | | Color | Legacy control color annotation associated with the probe. | | GreenIntensity | Raw green-channel intensity for the control probe measurement. | | RedIntensity | Raw red-channel intensity for the control probe measurement. | The `controls.qc_metrics.csv` file contains the following fields, along with a set of QC metric columns. | Field | Description | | -------------- | --------------------------------------------------- | | SampleId | Unique sample identifier. | | ImagingDate | Imaging date extracted from IDAT files. | | SamplePlate | Sample plate extracted from IDAT files. | | SampleWell | Sample well extracted from IDAT files. | | ScannerId | iScan identifier extracted from IDAT files. | | ScannerVersion | Scanner software version extracted from IDAT files. | The set of QC metric columns depends on the array type. Columns that do not apply to the given array type are omitted from the CSV. The metrics are calculated from the raw probe intensities as follows. In the formulas below, `bkg_grn` is the highest green-channel intensity of Extension (A) or Extension (T), and `bkg_red` is the highest red-channel intensity of Extension (C) or Extension (G). The background-correction offset `x` is 3000 for calculations that use `(bkg + x)`. | Control Metric | Genotyping Calculation | Methylation Calculation | Additional Information | | ----------------------------- | ------------------------------------------------------------------ | ------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | RestorationQC | Restoration green / (bkg\_grn + x) | Restoration green / (bkg\_grn + x) |
  • If using the FFPE DNA Restore Kit, the restoration control identifies success of the FFPE restoration chemistry.
| | StainingGreenQC | Biotin (High) green / Biotin (Bgnd) green | Biotin (High) green / Biotin (Bkg) green |
  • Staining controls are used to examine the efficiency of the staining step in both the red and green channels. These controls are independent of the hybridization and extension step.
  • The green channel shows a higher signal for biotin staining when compared to biotin background.
| | StainingRedQC | DNP (High) red / DNP (Bgnd) red | DNP (High) red / DNP (Bkg) red |
  • Red channel equivalent metric for StainingGreenQC using DNP.
| | ExtensionGreenQC | Lowest Extension (C or G) green / Highest Extension (A or T) green | Lowest Extension (C or G) green / Highest Extension (A or T) green |
  • Extension controls test the extension efficiency of A, T, C, and G nucleotides from a hairpin probe, and are therefore sample independent.
  • C and G extension is probed in green.
| | ExtensionRedQC | Lowest Extension (A or T) red / Highest Extension (C or G) red | Lowest Extension (A or T) red / Highest Extension (C or G) red |
  • Red channel equivalent metric for ExtensionGreenQC.
  • A and T extension is probed in red.
| | HybridizationHighMediumQC | Hyb (High) green / Hyb (Medium) green | Hyb (High) green / Hyb (Medium) green |
  • Hybridization controls test the overall performance of the Infinium Assay using synthetic targets instead of amplified DNA. These synthetic targets complement the sequence on the array, allowing the probe to extend on the synthetic target as a template. Synthetic targets are present in the Hybridization Buffer at 3 levels, monitoring the response from high-concentration (5 pM), medium concentration (1 pM), and low concentration (0.2 pM) targets. All bead type IDs result in signals with various intensities, corresponding to the concentrations of the initial synthetic targets.
  • The value for high concentration is always higher than medium and the value for medium concentration is always higher than low.
| | HybridizationMediumLowQC | Hyb (Medium) green / Hyb (Low) green | Hyb (Medium) green / Hyb (Low) green |
  • Medium to low equivalent of HybridizationHighMediumQC
| | TargetRemovalIQC | (bkg\_grn + x) / Target Removal green | (bkg\_grn + x) / Target Removal 1 green |
  • Target removal controls test the efficiency of the stripping step after the extension reaction. In contrast to allele-specific extension, the control oligos are extended using the probe sequence as a template. This process generates labeled targets. The probe sequences are designed such that extension from the probe does not occur. All target removal controls result in low signal compared to the hybridization controls, indicating that the targets were removed efficiently after extension. Target removal controls are present in the Hybridization Buffer.
  • The Background for the same sample should be close to or larger than either control.
| | TargetRemovalIIQC | — | (bkg\_grn + x) / Target Removal 2 green |
  • See Target Removal 1.
| | StringencyQC | String (PM) red / String (MM) red | — |
  • In the red channel strong positive signals are expected for String (PM) data points, while String (MM) data points are expected to be at low levels approaching background levels.
| | NonSpecificBindingGreenQC | (bkg\_grn + x) / Highest NSB (Bgnd) green | — |
  • The Non-Specific Binding Controls test sample quality and specificity of the assay. The probe sequences for Non-specific Binding Controls are complementary to bacterial sequences, and signal intensities at background are expected under standard hybridization conditions in both channels.
| | NonSpecificBindingRedQC | (bkg\_red + x) / Highest NSB (Bgnd) red | — |
  • Red channel equivalent metric for NonSpecificBindingGreenQC.
| | NonPolymorphicGreenQC | Lowest NP (C or G) green / Highest NP (A or T) green | Lowest NP (C or G) green / Highest NP (A or T) green |
  • The Non-Polymorphic Controls assess sample quality and the overall performance of the assay by querying non-polymorphic regions of the human genome.
  • Strong positive signals are expected for the green NP (C) and blue NP (G) data points, while the red NP (A) and purple NP (T) signals are expected to be at background levels.
| | NonPolymorphicRedQC | Lowest NP (A or T) red / Highest NP (C or G) red | Lowest NP (A or T) red / Highest NP (C or G) red |
  • Red channel equivalent metric for NonPolymorphicGreenQC.
| | SpecificityIGreenQC | — | Lowest GT Mismatch 1–3 (PM) green / Highest GT Mismatch 1–3 (MM) green |
  • Specificity I controls are designed to monitor potential nonspecific primer extension for Infinium I. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor allele-specific extension for Infinium I probes. The methylation status of a particular cytosine is carried out following bisulfite treatment of DNA by using query probes for unmethylated and methylated state of each CpG locus. In assay oligo design, the A/T match corresponds to the unmethylated status of the interrogated C, and G/C match corresponds to the methylated status of C. G/T mismatch controls check for nonspecific detection of methylation signal over unmethylated background. PM controls correspond to A/T perfect match and give high signal. MM controls correspond to G/T mismatch and give low signal.
| | SpecificityIRedQC | — | Lowest GT Mismatch 4–6 (PM) red / Highest GT Mismatch 4–6 (MM) red |
  • Red channel equivalent of SpecificityIGreenQC
| | SpecificityIIQC | — | Lowest Specificity II red / Highest Specificity II green |
  • Specificity II are designed to monitor extension specificity for Infinium II probes and check for potential nonspecific detection of methylation signal over unmethylated background. Specificity II probes incorporate the "A" base across the nonpolymorphic T and have intensity in the Red channel. If there was nonspecific incorporation of the "G" base, the probe has elevated signal in the Green channel.
| | SpecificityIIBgQC | — | (bkg\_grn + x) / Highest Specificity II green |
  • See SpecificityIIQC. This metric ensures lack of nonspecific incorporation of the "G" base.
| | BisulfiteConversionIGreenQC | — | Lowest BS Conversion I-C1–C2 green / Highest BS Conversion I-U1–U2 green |
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium I probe design and allele-specific single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "C" (Converted) probes matches the converted sequence and get extended. If the sample has unconverted DNA, the "U" (Unconverted) probes get extended. There are no underlying C bases in the primer landing sites, except for the query site itself.
| | BisulfiteConversionIGreenBgQC | — | (bkg\_grn + x) / Highest BS Conversion I-U1–U2 green |
  • See BisulfiteConversionIGreenQC. This metric captures whether unconverted DNA is at background levels.
| | BisulfiteConversionIRedQC | — | Lowest BS Conversion I-C3–C5 red / Highest BS Conversion I-U3–U5 red |
  • Red channel equivalent metric to BisulfiteConversionIGreenQC
| | BisulfiteConversionIRedBgQC | — | (bkg\_red + x) / Highest BS Conversion I-U3–U5 red |
  • Red channel equivalent metric to BisulfiteConversionIGreenBgQC.
| | BisulfiteConversionIIQC | — | Lowest BS Conversion II red / Highest BS Conversion II green |
  • These controls use Infinium II probe design and single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "A" base gets incorporated and the probe has intensity in the red channel. If the sample has unconverted DNA, the "G" base gets incorporated across the unconverted cytosine, and the probe has elevated signal in the green channel.
| | BisulfiteConversionIIBgQC | — | (bkg\_grn + x) / Highest BS Conversion II green |
  • See BisulfiteConversionIIQC. This metric captures whether unconverted DNA is at background levels.
| ## QC report For local analysis, the `qc report` command generates a comprehensive QC report designed to facilitate quality control review of genotyping and DNA methylation array datasets. The output always includes an HTML report for interactive review, plus a sample QC table for downstream review. In cloud analysis, this same QC report is automatically generated for the [genotyping](/overview/our-features#section-dragen-array-genotyping) and [cytogenetics](/overview/our-features#section-dragen-array-cytogenetics-analysis) pipelines. For more information about the HTML report and QC metrics, see [DRAGEN Array QC Report](/product-guides/dragen-array-local-analysis/qc-report). | File Name | Description | | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `DRAGENArray_QC_Report_YYYY_MM_DD.html` | Interactive HTML report intended to be distributable and viewable offline in a web browser. The report includes dashboards such as Control Dashboard, Automated QC, Sample QC Heatmaps, Trend Analysis, and a QC Metric Config menu for threshold customization. On local analysis, one report can combine multiple datasets for cross-run Trend Analysis. Current cloud reports are generated per dataset, so Trend Analysis summarizes a single dataset at a time. | | `DRAGENArray_QC_table_YYYY_MM_DD.` | Sample QC table (one row per sample) containing functional metrics (when available), derived control metrics, and raw control‑probe intensities. The file extension depends on `--output-format` (`xlsx` or `csv`). | {% hint style="info" %} When generating a QC report, the HTML report is always included. The QC table format is controlled by `--output-format` (default: `xlsx`). {% endhint %} [^1]: If using the FFPE Restore kit, change the default threshold from 0 to 1. [^2]: For MSA arrays, the default is 0.5. [^3]: For MSA arrays, the default is 2.5. [^4]: For MSA arrays, the default is 3. # PGx CNV Coverage Copy number variation can be detected for genes and regions listed below. The chromosome locations are GRCh38 based. | Gene | Region Name | Chromosome | Start | End | | ------- | --------------- | ------------------- | --------- | --------- | | GSTM1 | GSTM1 | 1 | 109687842 | 109693526 | | UGT2B17 | UGT2B17 | 4 | 68537222 | 68568499 | | CYP2E1 | CYP2E1 | 10 | 133527374 | 133539096 | | SULT1A1 | SULT1A1 | 16 | 28603587 | 28613544 | | CYP2A6 | CYP2A6.intron.7 | 19 | 40844791 | 40845293 | | CYP2A6 | CYP2A6.exon.1 | 19 | 40850267 | 40850414 | | CYP2D6 | CYP2D6.exon.9 | 22 | 42126498 | 42126752 | | CYP2D6 | CYP2D6.intron.2 | 22 | 42129188 | 42129734 | | CYP2D6 | CYP2D6.p5 | 22 | 42130886 | 42131379 | | GSTT1 | GSTT1 | 22\_KI270879v1\_alt | 270316 | 278477 | # PGx Allele Definitions and PGx Guidelines ## PGx Allele Definitions and PGx Guidelines DRAGEN Array star allele calling leverages the star allele definitions provided by PharmVar and PharmGKB. DRAGEN Array star allele phenotype annotation, using the “star-allele annotate” command, is achieved through direct lookup into public PGx guidelines CPIC or DPWG, which is selected by the user when running DRAGEN Array. See table below for details of the data sources. | Data Source | Version | URL | | ----------------------------------------------------------------------- | ------------------- | -------------------------------------------------------------------------------------- | | PharmVar | 6.1 | | | PharmGKB | Snapshot-2024.05.16 | | | UGT Alleles Nomenclature | 2010.12.21 | | | Human Cytochrome P450 (CYP) Allele Nomenclature Database Legacy Content | July 2024 | | | CPIC guidelines | 1.38.0 |

| | DPWG guidelines | June 2023 | | DRAGEN Array “star-allele annotate” command provides both metabolizer status and activity score annotations for genes covered by the CPIC and DPWG guidelines. Specifically, CPIC metabolizer/phenotype annotations are supported for CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, MT-RNR1, NUDT15, RYR1, SLCO1B1, TPMT, UGT1A1, CFTR, IFNL3/IFNL4 and VKORC1, among them activity scores are supported for CYP2C9, CYP2D6, and DPYD. DPWG metabolizer/phenotype annotations are supported for CYP1A2, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1 and F5, among them activity scores are supported for CYP2D6 and DPYD. ## Extended Multi-allelic variants based on the designs in the supported PGx products * DRAGEN Array PGx extends any single allele variant definitions obtained from PharmVar or PharmGKB that have multiple alleles in Illumina's product files to include all alleles of the Multi Allelic Variant (MAV). The table below shows the MAVs that were extended in the DRAGEN Array Database to cover all alleles for that MAV that are in the product files. Allele Name describes the allele that was added to the database. | Gene Symbol | Allele Name | Hgvs | | ----------------- | --------------------------- | ---------------------------- | | CACNA1S.rs1800559 | rs1800559.C>A | NC\_000001.11:g.201060815C>A | | CFTR.rs113993958 | rs113993958.G>A | NC\_000007.14:g.117530953G>A | | CFTR.rs113993958 | rs113993958.G>T | NC\_000007.14:g.117530953G>T | | CFTR.rs11971167 | rs11971167.G>T | NC\_000007.14:g.117642528G>T | | CFTR.rs121908755 | rs121908755.G>T | NC\_000007.14:g.117587800G>T | | CFTR.rs121909005 | rs121909005.T>C | NC\_000007.14:g.117587801T>C | | CFTR.rs121909020 | rs121909020.G>C | NC\_000007.14:g.117611640G>C | | CFTR.rs150212784 | rs150212784.T>C | NC\_000007.14:g.117611595T>C | | CFTR.rs193922525 | rs193922525.G>C | NC\_000007.14:g.117664770G>C | | CFTR.rs267606723 | rs267606723.G>T | NC\_000007.14:g.117642451G>T | | CFTR.rs397508288 | rs397508288.A>C | NC\_000007.14:g.117590409A>C | | CFTR.rs397508759 | rs397508759.G>T | NC\_000007.14:g.117534363G>T | | CFTR.rs74551128 | rs74551128.C>T | NC\_000007.14:g.117548795C>T | | CFTR.rs75039782 | rs75039782.C>G | NC\_000007.14:g.117639961C>G | | CFTR.rs77834169 | rs77834169.C>A | NC\_000007.14:g.117530974C>A | | CFTR.rs77834169 | rs77834169.C>G | NC\_000007.14:g.117530974C>G | | CFTR.rs77932196 | rs77932196.G>C | NC\_000007.14:g.117540270G>C | | CFTR.rs77932196 | rs77932196.G>T | NC\_000007.14:g.117540270G>T | | CFTR.rs78655421 | rs78655421.G>C | NC\_000007.14:g.117530975G>C | | CFTR.rs78655421 | rs78655421.G>T | NC\_000007.14:g.117530975G>T | | COMT.rs13306278 | rs13306278.C>G | NC\_000022.11:g.19941504C>G | | DPYD.rs114096998 | rs114096998.2.G>C | NC\_000001.11:g.97078987G>C | | DPYD.rs140602333 | rs140602333.G>T | NC\_000001.11:g.97573919G>T | | DPYD.rs142619737 | rs142619737.C>G | NC\_000001.11:g.97515851C>G | | DPYD.rs143154602 | rs143154602.G>T | NC\_000001.11:g.97593289G>T | | DPYD.rs145548112 | rs145548112.C>A | NC\_000001.11:g.97306195C>A | | DPYD.rs190951787 | rs190951787.G>T | NC\_000001.11:g.97515889G>T | | DPYD.rs200687447 | rs200687447.2.C>A | NC\_000001.11:g.97193209C>A | | DPYD.rs3918289 | rs3918289.G>A | NC\_000001.11:g.97450059G>A | | DPYD.rs3918290 | rs3918290.C>G | NC\_000001.11:g.97450058C>G | | DPYD.rs6670886 | rs6670886.C>A | NC\_000001.11:g.97699506C>A | | DPYD.rs72549304 | rs72549304.G>C | NC\_000001.11:g.97549609G>C | | DPYD.rs72549304 | rs72549304.G>T | NC\_000001.11:g.97549609G>T | | DPYD.rs748620513 | rs748620513.C>A | NC\_000001.11:g.97573799C>A | | DPYD.rs748639205 | rs748639205.A>G | NC\_000001.11:g.97082415A>G | | DPYD.rs760663364 | rs760663364.G>C | NC\_000001.11:g.97515928G>C | | DPYD.rs777425216 | rs777425216.C>A | NC\_000001.11:g.97515815C>A | | RYR1.38499667G>A | NC\_000019.10:g.38499667G>T | NC\_000019.10:g.38499667G>T | | RYR1.rs118192116 | rs118192116.C>T | NC\_000019.10:g.38451850C>T | | RYR1.rs118192151 | rs118192151.G>C | NC\_000019.10:g.38584974G>C | | RYR1.rs118204423 | rs118204423.G>A | NC\_000019.10:g.38457539G>A | | RYR1.rs142474192 | rs142474192.G>T | NC\_000019.10:g.38443790G>T | | RYR1.rs143988412 | rs143988412.A>G | NC\_000019.10:g.38580066A>G | | RYR1.rs1801086 | rs1801086.G>T | NC\_000019.10:g.38446710G>T | | RYR1.rs186983396 | rs186983396.C>G | NC\_000019.10:g.38442434C>G | | RYR1.rs193922762 | rs193922762.C>A | NC\_000019.10:g.38448673C>A | | RYR1.rs193922767 | rs193922767.G>A | NC\_000019.10:g.38452996G>A | | RYR1.rs193922772 | rs193922772.G>A | NC\_000019.10:g.38457546G>A | | RYR1.rs193922826 | rs193922826.C>G | NC\_000019.10:g.38504319C>G | | RYR1.rs193922838 | rs193922838.G>A | NC\_000019.10:g.38529036G>A | | RYR1.rs193922842 | rs193922842.C>T | NC\_000019.10:g.38543821C>T | | RYR1.rs370634440 | rs370634440.G>T | NC\_000019.10:g.38463499G>T | ### Exceptions to Star Allele Definitions #### G6PD With the changes of reference genomes, the definition for a star allele sometimes need to be updated accordingly. `Mediterranean Haplotype` and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` are defined by two variants rs5030868 and rs2230037. In genome build GRCh37, `Mediterranean Haplotype` is defined by rs2230037 G>A and rs5030868 G>A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs5030868 G>A, with rs2230037 reference allele G. In genome build GRCh38, `Mediterranean Haplotype` is defined by rs5030868 G>A, with rs2230037 reference allele A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs2230037 A>G and rs5030868 G>A. Variant rs2230037 is ignored in all other G6PD alleles except in the two Mediterranean alleles. #### \*0 Star Allele Definition A \*0 allele refers to a full gene deletion of the analyzed gene, if there is no existing star allele name for the deletion allele from source databases, such as PharmVar and PharmGKB. # PGx Star Allele Coverage ## PGx Star Allele Coverage for Specific PGx Products PGx star alleles can only be called when the related variants in the star allele definition are present in a PGx product. An auxiliary file (`[Product]_GS_import.txt`) is provided for each product with the PGx variants and associated star alleles. The product files pages that contain the auxiliary files are listed in the table below. The auxiliary file covers SNPs and indels only; it does not contain SV-defined star alleles. Instructions on how to use the auxiliary file can be found here: [How to use the auxiliary file](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000008505). | Product | GS Import File Name | Product Files Link | | ---------- | ---------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | GDA-ePGx | GDAePGx\_G2\_GS\_import.txt | [GDA-ePGx G2 product files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html) | | GSAv4-ePGx | GSAePGx\_E2\_GS\_import.txt | [GSAv4-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | | GCRA-ePGx | GCRAePGx\_E2\_GS\_import.txt | [GCRA-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | ## PGx Software Database Coverage DRAGEN Array v1.1 and above include support for the PGx genes and star/variant allele definitions listed below. Inclusion in this table does not imply that every supported allele can be identified on every PGx microarray, as calling depends on the variants present on the specific array design. These definitions are based on PharmVar and PharmGKB and represent the alleles that can be identified and reported by the software when the required variants are present in the input microarray data. Detection of a specific allele depends on whether the variants required for its definition are included on the microarray being analyzed. PGx coverage for individual microarray products is described in [PGx Star Allele Coverage for Specific PGx Products](#pgx-star-allele-coverage-for-specific-pgx-products). Star alleles that are not included in the supported allele definition database, as well as novel star alleles, are not reported. For HLA-A, HLA-B, and IFNL3/IFNL4, results are based on tagging variants rather than direct star allele determination. Specifically, the following tagging variants are used: HLA-A\*31:01 (rs1061235 A>T), HLA-B\*15:02 (rs144012689 T>A), HLA-B\*57:01 (rs2395029 T>G), HLA-B\*58:01 (rs9263726 G>A), and IFNL3/IFNL4 rs12979860 (T). Support for HLA-B\*15:02 (rs144012689 T>A) is dependent on the specific microarray content and may not be available on all PGx array designs. The variant HLA-B\*15:02 (rs144012689 T>A) is not in GDA-ePGx. The reliability of these tagging variants varies by population. Additional information about PGx gene result types (variant-based versus star allele-based) is available in [Introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Gene | PGx Alleles | | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ABCG2 | Reference;rs2231142.G>T | | ADH1B | Reference;rs1229984.T>C;rs1229984.T>G;rs1229985.A>G;rs17033.T>C;rs1789891.C>A;rs2018417.C>A;rs2018417.C>T;rs2066702.G>A;rs75967634.C>T | | ALDH2 | Reference;rs671.G>A | | ANK3 | Reference;rs143414470.T>C | | ANKK1 | Reference;rs1800497.G>A;rs2587550.G>A;rs2734849.A>C;rs2734849.A>G;rs4938013.A>C;rs4938013.A>G;rs4938013.A>T;rs7118900.G>A;rs7118900.G>C | | APOE | E2;E3;E4 | | ATM | Reference;rs11212570.G>A;rs11212570.G>T;rs11212617.C>A;rs1801516.G>A;rs620815.T>A;rs620815.T>C | | BDNF | Reference;rs10835210.C>A;rs10835210.C>G;rs11030101.A>G;rs11030101.A>T;rs11030104.A>G;rs11030118.G>A;rs11030119.G>A;rs11030119.G>T;rs1491850.T>C;rs16917234.T>A;rs16917234.T>C;rs1967554.A>C;rs2030324.A>G;rs61888800.G>T;rs6265.C>T;rs7103411.C>T;rs7124442.C>G;rs7124442.C>T;rs7127507.T>C;rs7934165.G>A;rs962369.T>C;rs988748.C>G | | CACNA1C | Reference;rs1006737.G>A;rs1034936.C>A;rs1034936.C>G;rs1034936.C>T;rs1051375.G>A;rs1051375.G>C;rs10774053.A>C;rs10774053.A>G;rs10848635.T>A;rs10848635.T>C;rs11062040.C>T;rs12813888.A>C;rs12813888.A>T;rs2041135.T>C;rs215976.C>G;rs215976.C>T;rs215994.T>C;rs216008.C>T;rs216013.A>G;rs2238032.T>C;rs2238032.T>G;rs2238087.C>G;rs2238087.C>T;rs2239050.G>A;rs2239050.G>C;rs2239128.T>A;rs2239128.T>C;rs2283271.T>A;rs723672.C>A;rs723672.C>G;rs723672.C>T;rs7295250.T>C;rs7316246.G>A;rs7316246.G>C;rs758723.T>A;rs758723.T>C | | CACNA1S | Reference;rs1800559.C>A;rs1800559.C>T;rs772226819.G>A | | CFTR | Reference;rs113993958.G>A;rs113993958.G>C;rs113993958.G>T;rs115545701.C>T;rs11971167.G>A;rs11971167.G>T;rs121908752.T>G;rs121908753.G>A;rs121908755.G>A;rs121908755.G>T;rs121908757.A>C;rs121909005.T>C;rs121909005.T>G;rs121909013.G>A;rs121909020.G>A;rs121909020.G>C;rs121909041.T>C;rs141033578.C>T;rs150212784.T>C;rs150212784.T>G;rs186045772.T>A;rs193922525.G>A;rs193922525.G>C;rs200321110.G>A;rs202179988.C>T;rs267606723.G>A;rs267606723.G>T;rs368505753.C>T;rs397508256.G>A;rs397508288.A>C;rs397508288.A>G;rs397508387.G>T;rs397508442.C>T;rs397508513.A>C;rs397508537.C>A;rs397508759.G>A;rs397508759.G>T;rs397508761.A>G;rs74503330.G>A;rs74551128.C>A;rs74551128.C>T;rs75039782.C>G;rs75039782.C>T;rs75527207.G>A;rs75541969.G>C;rs76151804.A>G;rs77834169.C>A;rs77834169.C>G;rs77834169.C>T;rs77932196.G>A;rs77932196.G>C;rs77932196.G>T;rs78655421.G>A;rs78655421.G>C;rs78655421.G>T;rs78769542.G>A;rs80224560.G>A;rs80282562.G>A | | COMT | Reference;rs13306278.C>T;rs165599.G>A;rs165599.G>C;rs165722.C>T;rs165728.C>A;rs165728.C>G;rs165728.C>T;rs165774.G>A;rs174675.T>C;rs174696.C>A;rs174696.C>T;rs174699.C>T;rs2020917.C>T;rs2075507.G>A;rs2075507.G>C;rs2075507.G>T;rs2239393.A>G;rs4633.C>T;rs4646312.T>C;rs4646316.C>G;rs4646316.C>T;rs4680.G>A;rs4818.C>G;rs4818.C>T;rs5746849.A>G;rs5993882.T>C;rs5993882.T>G;rs5993883.T>G;rs6267.G>A;rs6267.G>T;rs6269.A>G;rs6269.A>T;rs7287550.T>C;rs7287550.T>G;rs737865.A>G;rs737866.T>A;rs737866.T>C;rs740603.A>G;rs9332377.C>A;rs9332377.C>T;rs933271.T>A;rs933271.T>C;rs9606186.C>A;rs9606186.C>G;rs9606186.C>T | | CYP1A2 | \*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1A;\*1B;\*1C;\*1D;\*1E;\*1F;\*1G;\*1J;\*1K;\*1L;\*1M;\*1N;\*1P;\*1Q;\*1R;\*1S;\*1T;\*1U;\*1V;\*2;\*20;\*21;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2A6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*31;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*6;\*7;\*8;\*9 | | CYP2B6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*3;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*6;\*7;\*8;\*9 | | CYP2C19 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*22;\*23;\*24;\*25;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*38;\*39;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C8 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C9 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*57;\*58;\*59;\*6;\*60;\*61;\*62;\*63;\*64;\*65;\*66;\*67;\*68;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*76;\*77;\*78;\*79;\*8;\*80;\*81;\*82;\*83;\*84;\*85;\*9 | | CYP2D6 | \*1;\*1-\*90;\*10;\*100;\*101;\*102;\*103;\*104;\*105;\*106;\*107;\*108;\*109;\*10x2;\*11;\*110;\*111;\*112;\*113;\*114;\*115;\*116;\*117;\*118;\*119;\*12;\*120;\*121;\*122;\*123;\*124;\*125;\*126;\*127;\*128;\*129;\*13;\*13-\*1;\*13-\*2;\*13-\*4-\*68;\*130;\*131;\*132;\*133;\*134;\*135;\*136;\*137;\*138;\*139;\*13x2-\*1;\*13x2-\*2;\*14;\*140;\*141;\*142;\*143;\*144;\*145;\*146;\*147;\*148;\*149;\*15;\*150;\*151;\*152;\*153;\*154;\*155;\*156;\*157;\*158;\*159;\*160;\*161;\*162;\*163;\*164;\*165;\*166;\*167;\*168;\*169;\*17;\*170;\*171;\*172;\*17x2;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*29x2;\*2x2;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*35x2;\*36;\*36;\*36-\*10;\*36-\*10x2;\*36x2-\*10;\*36x3-\*10;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*43x2;\*44;\*45;\*46;\*47;\*48;\*49;\*4M;\*4N-\*4;\*4x2;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*58;\*59;\*6;\*60;\*62;\*64;\*65;\*68;\*68-\*4;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*8;\*81;\*82;\*83;\*84;\*85;\*86;\*87;\*88;\*89;\*9;\*90;\*91;\*92;\*93;\*94;\*95;\*96;\*97;\*98;\*99;\*9x2 | | CYP2E1 | \*1A;\*1B;\*2;\*3;\*4;\*5A;\*5B;\*6;\*7A;\*7B;\*7C | | CYP3A4 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*5;\*6;\*7;\*8;\*9 | | CYP3A5 | \*1;\*3;\*6;\*7;\*8;\*9 | | CYP4F2 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | DPYD | Reference;rs111858276.T>C;rs112766203.1.G>A;rs112766203.2.G>C;rs114096998.1.G>T;rs114096998.2.G>A;rs114096998.2.G>C;rs115232898.T>C;rs116364703.T>A;rs1180771326.T>C;rs137878450.C>A;rs137999090.C>T;rs138391898.C>T;rs138545885.C>A;rs138616379.C>T;rs139459586.A>C;rs139834141.C>T;rs140039091.C>G;rs140114515.C>T;rs140602333.G>A;rs140602333.G>T;rs140989814.C>G;rs141044036.T>C;rs141439344.C>T;rs141462178.T>C;rs141726921.C>T;rs142512579.C>T;rs142619737.C>G;rs142619737.C>T;rs143154602.G>A;rs143154602.G>T;rs143815742.1.C>A;rs143815742.2.C>T;rs143879757.1.G>T;rs143879757.2.G>A;rs143986398.G>C;rs144395748.1.G>C;rs144395748.2.G>T;rs144935781.T>C;rs145112791.G>A;rs145529148.T>C;rs145548112.C>A;rs145548112.C>T;rs145773863.C>T;rs146356975.T>C;rs146529561.G>A;rs147545709.G>A;rs147601618.A>G;rs148799944.C>G;rs148994843.C>T;rs150036960.G>C;rs150385342.1.C>T;rs150385342.2.C>A;rs150437414.A>G;rs151074666.C>T;rs17376848.A>G;rs1801158.C>T;rs1801159.T>C;rs1801160.C>T;rs1801265.A>G;rs1801266.G>A;rs1801267.C>T;rs1801268.C>A;rs183105782.A>G;rs183385770.C>T;rs186169810.A>C;rs187713395.A>G;rs188052243.T>C;rs190577302.G>C;rs190951787.G>C;rs190951787.G>T;rs199549923.G>T;rs199634007.G>T;rs199646142.C>T;rs199777072.C>T;rs200064537.A>T;rs200296941.T>C;rs200562975.T>C;rs200643089.A>C;rs200687447.1.C>T;rs200687447.2.C>A;rs200687447.2.C>G;rs200693895.A>G;rs200709381.T>G;rs201018345.C>T;rs201035051.T>G;rs201268750.G>T;rs201433243.C>T;rs201615754.1.C>A;rs201615754.2.C>T;rs201648613.C>G;rs201785202.G>A;rs202144771.G>A;rs202212118.C>A;rs2297595.T>C;rs267598785.G>A;rs267598786.C>T;rs267598789.G>A;rs367619008.T>C;rs368146607.T>G;rs368152149.T>C;rs368327291.C>G;rs368519011.T>C;rs368970772.G>T;rs369103276.A>G;rs369575517.G>A;rs370569731.1.C>G;rs370569731.2.C>T;rs370615432.C>A;rs370707404.A>G;rs371258350.C>T;rs371313778.C>T;rs371587702.1.G>A;rs371587702.2.G>C;rs371792178.1.G>A;rs371792178.2.G>C;rs372058915.T>C;rs372307932.A>T;rs372909322.T>C;rs374527058.A>G;rs374531732.C>T;rs374825099.1.G>T;rs374825099.2.G>C;rs374827081.G>C;rs375436137.C>T;rs375990187.A>G;rs376073289.1.C>T;rs376073289.2.C>A;rs376128878.G>T;rs376273539.G>C;rs377143350.C>T;rs377169736.C>G;rs3918289.G>A;rs3918289.G>C;rs3918290.C>G;rs3918290.C>T;rs45589337.T>C;rs527580106.T>C;rs528152707.C>A;rs528430685.G>A;rs528768620.C>T;rs529019871.T>C;rs532341730.A>T;rs536577604.T>C;rs538336580.T>A;rs538703919.G>A;rs547099198.G>A;rs548783838.C>T;rs55674432.C>A;rs556933127.A>C;rs557220418.G>A;rs558354142.G>A;rs55886062.1.A>C;rs55886062.2.A>T;rs559427764.C>A;rs55971861.T>G;rs56005131.G>T;rs56038477.C>T;rs568169006.T>C;rs568367673.C>A;rs569661196.A>G;rs570122671.G>A;rs571114616.A>G;rs573299212.C>T;rs575763449.G>A;rs575853463.C>T;rs576409484.T>A;rs57918000.G>A;rs59086055.G>A;rs60139309.T>C;rs60511679.A>C;rs61622928.C>T;rs61757362.G>A;rs6670886.C>A;rs6670886.C>T;rs672601273.1.C>A;rs672601273.2.C>T;rs672601275.T>G;rs672601276.C>A;rs672601282.G>A;rs672601284.C>T;rs672601285.T>C;rs672601287.T>G;rs672601288.C>A;rs67376798.T>A;rs72547601.T>C;rs72547602.T>A;rs72549303.del;rs72549304.G>A;rs72549304.G>C;rs72549304.G>T;rs72549305.T>C;rs72549306.1.C>A;rs72549306.2.C>T;rs72549307.T>C;rs72549308.T>G;rs72549309.ATGA\[1];rs72549310.G>A;rs72975710.1.G>A;rs72975710.2.G>C;rs745512069.G>A;rs745704371.G>C;rs745833535.T>C;rs745911874.C>T;rs745982505.1.T>C;rs745982505.2.T>A;rs746115989.C>T;rs746329786.T>A;rs746777181.C>T;rs747132274.C>G;rs747161261.C>T;rs747627716.A>C;rs747633945.C>T;rs747858350.G>A;rs747872037.C>A;rs748214188.A>T;rs748235192.1.T>A;rs748235192.2.T>C;rs748266854.G>A;rs748320430.A>C;rs748620513.C>A;rs748620513.C>G;rs748639205.A>C;rs748639205.A>G;rs748853941.T>C;rs748958293.G>A;rs748974194.G>A;rs749157068.C>A;rs749269410.C>T;rs749354734.A>T;rs749586100.T>A;rs749699298.A>C;rs749982106.G>A;rs750147471.T>C;rs75017182.G>C;rs750224169.G>A;rs750423752.A>C;rs750687600.C>T;rs750721736.A>T;rs751049055.C>A;rs751104498.T>C;rs751113340.G>A;rs751190912.G>A;rs751340819.A>G;rs751374989.T>A;rs751399062.G>T;rs751841116.1.C>A;rs751841116.2.C>T;rs751848058.T>A;rs752020412.C>T;rs752228747.G>A;rs752388408.C>T;rs752518145.C>A;rs752985272.C>A;rs753166888.C>G;rs753217888.G>C;rs753296078.C>G;rs753419296.C>G;rs753527420.C>G;rs753707032.G>A;rs753710779.G>A;rs753820482.T>C;rs753950237.G>A;rs754028972.A>G;rs754125729.1.G>A;rs754125729.2.G>T;rs754467630.G>A;rs754786483.T>C;rs755155824.C>A;rs755407188.T>G;rs755416212.C>T;rs755428442.C>G;rs755645831.A>C;rs755692084.T>G;rs755729055.T>C;rs756020314.G>C;rs756372042.A>G;rs756613407.T>C;rs756684474.T>C;rs756890859.T>C;rs756992995.C>T;rs757155354.T>C;rs757227327.C>T;rs757342874.C>T;rs757376267.C>A;rs757695236.C>T;rs757954074.C>T;rs757958938.T>C;rs757994597.G>A;rs758154803.A>G;rs758489611.C>T;rs758514990.C>T;rs758649719.C>T;rs758699471.T>C;rs759082282.C>A;rs759249769.G>T;rs759424419.A>T;rs759479759.T>C;rs759562628.T>G;rs759766897.T>C;rs759967863.A>G;rs760038956.C>T;rs760222167.T>C;rs760235888.C>T;rs760485592.G>A;rs760553268.G>C;rs760570391.A>G;rs760663364.G>A;rs760663364.G>C;rs761302217.T>C;rs761351410.G>A;rs761479700.G>C;rs761555670.T>C;rs761609256.T>G;rs762083671.T>A;rs762102298.A>C;rs762198241.G>A;rs762430779.G>T;rs762446803.A>C;rs762468894.G>C;rs762523739.T>A;rs762533012.C>T;rs762598766.T>C;rs762779297.T>C;rs762858106.C>T;rs762911226.T>A;rs763008163.T>G;rs763061658.A>G;rs763449831.C>T;rs763506271.T>C;rs763557204.A>G;rs763572567.T>G;rs763623595.A>C;rs763784786.G>C;rs763862486.C>T;rs763893877.T>C;rs763984510.G>C;rs764111543.C>T;rs764270260.G>A;rs764555085.A>G;rs764635955.G>T;rs764666241.C>A;rs764679468.A>C;rs764945792.C>T;rs765001324.C>T;rs765034707.C>A;rs765075551.T>C;rs765131182.G>A;rs765247038.G>A;rs765309287.G>T;rs765465250.T>C;rs765640386.C>A;rs765990958.G>A;rs766411970.A>C;rs766438205.T>C;rs766635900.C>T;rs766700777.C>G;rs766761199.T>G;rs766833304.G>C;rs766885021.A>C;rs767200577.T>C;rs767376585.C>G;rs767437717.G>T;rs767464878.C>A;rs767468952.C>T;rs767482279.A>G;rs767547827.G>C;rs767818267.C>T;rs767836989.T>C;rs767986711.T>G;rs768117152.T>C;rs768157853.G>C;rs768200107.T>G;rs768288280.T>C;rs768501828.T>C;rs768507975.A>T;rs768680499.G>T;rs768915005.C>T;rs769190350.T>A;rs769306962.C>T;rs769466648.1.T>G;rs769466648.2.T>C;rs769514867.G>T;rs769696395.T>C;rs769709846.T>C;rs769820114.C>T;rs769847078.T>C;rs769932607.G>A;rs770229152.T>A;rs770566506.A>G;rs770958862.G>A;rs771194906.A>G;rs771534236.T>C;rs771536388.C>T;rs771573678.T>A;rs771646887.C>T;rs771648776.T>C;rs771885007.A>G;rs771930534.1.A>T;rs771930534.2.A>G;rs772097379.G>A;rs772264512.G>A;rs772320654.T>C;rs772358811.C>G;rs772544099.G>T;rs772826416.A>G;rs772906420.C>T;rs773159364.C>G;rs773407491.T>C;rs773584401.C>A;rs773652644.T>C;rs773815814.1.C>A;rs773815814.2.C>T;rs773868825.C>T;rs773983635.A>T;rs774134971.T>C;rs774500505.A>T;rs774579695.1.C>T;rs774799003.G>A;rs774883578.A>C;rs775494607.G>A;rs775526810.C>A;rs775570841.G>C;rs775601164.G>A;rs775926386.G>C;rs776082092.C>T;rs776236081.C>T;rs776289153.C>T;rs776321529.G>C;rs776662759.T>G;rs776973423.C>T;rs776984091.T>C;rs777220476.1.C>T;rs777220476.2.C>A;rs777238016.T>C;rs777347164.C>T;rs777368221.A>C;rs777425216.C>A;rs777425216.C>T;rs777560627.G>A;rs777673186.G>C;rs777902288.T>A;rs778022685.C>T;rs778054451.C>T;rs778141885.T>C;rs778298325.C>T;rs778601245.C>T;rs778754188.A>G;rs778760295.C>G;rs778776264.T>C;rs778867644.T>C;rs778911905.A>C;rs779465366.A>G;rs779557503.G>A;rs779573574.T>A;rs779728902.A>T;rs779925747.T>G;rs779967271.T>C;rs780025995.G>A;rs780047918.T>C;rs780120302.T>C;rs78060119.C>A;rs780813130.C>T;rs780873985.T>C;rs780885126.T>C;rs781184141.T>C;rs80081766.C>T;rs866110709.C>T;rs866869468.C>A;rs867143119.C>A;rs867226255.C>T;rs867232786.C>T;rs867600987.C>T;rs868047175.C>T;rs868235016.C>T | | DRD2 | Reference;rs1076560.C>A;rs1076560.C>G;rs1076563.A>C;rs1079596.C>A;rs1079596.C>T;rs1079597.C>T;rs1079598.A>G;rs1079598.A>T;rs1110976.T>G;rs11214607.T>G;rs1124491.G>A;rs1124491.G>C;rs1124493.T>G;rs1125394.T>C;rs12364283.A>G;rs12574471.C>G;rs12574471.C>T;rs17601612.G>C;rs1799732.\_113475530insG;rs1799732.dup;rs1799978.T>C;rs1800497.G>A;rs1800498.G>A;rs1801028.G>C;rs2075652.G>A;rs2234689.G>C;rs2283265.C>A;rs2440390.T>C;rs2514218.C>T;rs2587548.G>A;rs2587548.G>C;rs2734833.G>A;rs2734841.A>C;rs2734841.A>G;rs2734841.A>T;rs2734842.G>C;rs4274224.G>A;rs4274224.G>C;rs4436578.C>G;rs4436578.C>T;rs4460839.C>G;rs4460839.C>T;rs4648317.G>A;rs4648318.T>A;rs4648318.T>C;rs4648318.T>G;rs4936274.A>G;rs4936274.A>T;rs6275.A>G;rs6277.G>A;rs6279.G>C;rs7122246.G>A;rs7131056.A>C;rs7131056.A>G;rs7131440.C>T | | F13A1 | Reference;rs5985.C>A;rs5985.C>T | | F2 | Reference;rs1799963.G>A;rs3136516.G>A;rs5896.C>G;rs5896.C>T | | F5 | Reference;rs6025.C>T | | FKBP5 | Reference;rs1360780.T>A;rs1360780.T>C;rs17614642.T>C;rs3800373.C>A;rs3800373.C>G;rs4713916.A>C;rs4713916.A>G;rs4713916.A>T;rs73748206.C>T;rs9380524.C>A;rs9380524.C>T | | G6PD | 202G>A\_376A>G\_1264C>G;A;A- 202A\_376G;A- 680T\_376G;A- 968C\_376G;Aachen;Abeno;Acrokorinthos;Alhambra;Amazonia;Amiens;Amsterdam;Anadia;Ananindeua;Andalus;Arakawa;Asahi;Asahikawa;Aures;Aveiro;B (reference);Bajo Maumere;Bangkok;Bangkok Noi;Bao Loc;Bari;Belem;Beverly Hills, Genova, Iwate, Niigata, Yamaguchi;Brighton;Buenos Aires;Cairo;Calvo Mackenna;Campinas;Canton, Taiwan-Hakka, Gifu-like, Agrigento-like;Cassano;Chatham;Chikugo;Chinese-1;Chinese-5;Cincinnati;Cleveland Corum;Clinic;Coimbra Shunde;Cosenza;Costanzo;Covao do Lobo;Crispim;Dagua;Durham;Farroupilha;Figuera da Foz;Flores;Fukaya;Fushan;Gaohe;Georgia;Gidra;Gond;Guadalajara;Guangzhou;Haikou;Hammersmith;Harilaou;Harima;Hartford;Hechi;Hermoupolis;Honiara;Ierapetra;Ilesha;Insuli;Iowa, Walter Reed, Springfield;Iwatsuki;Japan, Shinagawa;Kaiping, Anant, Dhon, Sapporo-like, Wosera;Kalyan-Kerala, Jamnaga, Rohini;Kambos;Kamiube, Keelung;Kamogawa;Kawasaki;Kozukata;Krakow;La Jolla;Lages;Lagosanto;Laibin;Lille;Liuzhou;Loma Linda;Ludhiana;Lynwood;Madrid;Mahidol;Malaga;Manhattan;Mediterranean Haplotype;Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham;Metaponto;Mexico City;Miaoli;Minnesota, Marion, Gastonia, LeJeune;Mira d'Aire;Mizushima;Montalbano;Montpellier;Mt Sinai;Munich;Murcia Oristano;Musashino;Namouru;Nankang;Nanning;Naone;Nara;Nashville, Anaheim, Portici;Neapolis;Nice;Nilgiri;No name;North Dallas;Olomouc;Omiya;Orissa;Osaka;Palestrina;Papua;Partenope;Pawnee;Pedoplis-Ckaro;Piotrkow;Plymouth;Praha;Puerto Limon;Quing Yan;Radlowo;Rehevot;Rignano;Riley;Riverside;Roubaix;S. Antioco;Salerno Pyrgos;Santa Maria;Santiago;Santiago de Cuba, Morioka;Sao Borja;Seattle, Lodi, Modena, Ferrara II, Athens-like;Seoul;Serres;Shenzen;Shinshu;Sibari;Sierra Leone;Sinnai;Songklanagarind;Split;Stonybrook;Sugao;Sumare;Sunderland;Surabaya;Suwalki;Swansea;Taipei, Chinese-3;Telti, Kobe;Tenri;Tokyo, Fukushima;Toledo;Tomah;Tondela;Torun;Tsukui;Ube Konan;Union,Maewo, Chinese-2, Kalo;Urayasu;Utrecht;Valladolid;Vancouver;Vanua Lava;Viangchan, Jammu;Villeurbanne;Volendam;Wayne;West Virginia;Wexham;Wisconsin;Yunan | | GRIK1 | Reference;rs2832407.C>A;rs2832407.C>T | | GRIK4 | Reference;rs12800734.G>A;rs1954787.T>C | | GRIN2B | Reference;rs1019385.C>A;rs1072388.G>A;rs1072388.G>C;rs1806191.G>A;rs1806191.G>T;rs1806201.G>A;rs2058878.T>A;rs2058878.T>C;rs2160733.A>C;rs2160734.C>G;rs2160734.C>T;rs2284411.C>T;rs890.A>C;rs890.A>G | | HLA-A | \*31:01;Reference | | HLA-B | \*15:02;\*57:01;\*58:01;Reference | | HMGCR | Reference;rs10474433.T>C;rs10474433.T>G;rs12654264.A>T;rs17238540.T>G;rs17244841.A>T;rs17671591.C>T;rs3846662.A>G;rs3846662.A>T | | HTR2A | Reference;rs17288723.T>C;rs17289304.T>C;rs17289304.T>G;rs1928040.G>A;rs1928040.G>C;rs2274639.C>G;rs2274639.C>T;rs2770296.C>G;rs2770296.C>T;rs3742278.A>G;rs3803189.T>G;rs6305.G>A;rs6311.C>A;rs6311.C>T;rs6312.C>A;rs6312.C>G;rs6312.C>T;rs6313.G>A;rs6313.G>C;rs6314.G>A;rs659734.G>A;rs659734.G>C;rs659734.G>T;rs7997012.A>C;rs7997012.A>G;rs7997012.A>T;rs9316233.C>A;rs9316233.C>G;rs9316233.C>T;rs9567746.A>C;rs9567746.A>G | | HTR2C | Reference;rs1023574.C>G;rs1023574.C>T;rs12836771.A>G;rs1414334.C>G;rs2497538.A>C;rs3813928.G>A;rs3813929.C>G;rs3813929.C>T;rs498207.G>A;rs518147.C>A;rs518147.C>G;rs539748.C>T;rs6318.C>G;rs6318.C>T;rs9698290.T>A;rs9698290.T>C | | IFNL3/4 | Reference;rs12979860 variant (T) | | IL6 | Reference;rs10242595.G>A;rs10242595.G>C;rs10242595.G>T;rs10499563.T>C;rs1524107.C>G;rs1524107.C>T;rs1800795.C>G;rs1800795.C>T;rs1800796.G>A;rs1800796.G>C;rs1800797.A>C;rs1800797.A>G;rs1800797.A>T;rs2066992.G>A;rs2066992.G>C;rs2066992.G>T;rs2069835.T>C;rs2069837.A>C;rs2069837.A>G;rs2069840.C>G | | ITGB3 | Reference;rs11871251.G>A;rs11871251.G>C;rs2317676.A>G;rs3785873.G>A;rs3785873.G>T;rs58847127.G>A;rs58847127.G>C;rs58847127.G>T;rs5918.T>C;rs8069732.C>A;rs8069732.C>T | | KIF6 | Reference;rs20455.A>G;rs9462535.C>A;rs9462535.C>G;rs9462535.C>T;rs9471077.A>G | | LPA | Reference;rs10455872.A>G;rs3798220.T>C | | MT-RNR1 | NC\_012920.1:m.1520T>C;NC\_012920.1:m.1537C>T;NC\_012920.1:m.1556C>T;NC\_012920.1:m.669T>C;NC\_012920.1:m.747A>G;NC\_012920.1:m.786G>A;NC\_012920.1:m.807A>C;NC\_012920.1:m.807A>G;NC\_012920.1:m.839A>G;NC\_012920.1:m.896A>G;NC\_012920.1:m.930A>G;NC\_012920.1:m.960delC;NC\_012920.1:m.988G>A;Reference;rs1556422499.delT;rs200887992.G>A;rs267606617.A>G;rs267606618.T>C;rs267606619.C>T;rs28358569.A>G;rs28358571.T>C;rs28358572.T>C;rs3888511.T>G;rs56489998.A>G | | MTHFR | Reference;rs1476413.C>G;rs1476413.C>T;rs17367504.A>G;rs17421511.G>A;rs1801131.T>G;rs1801133.G>A;rs1801133.G>C;rs2274976.C>T;rs3737967.G>A;rs4846051.G>A;rs4846051.G>C;rs4846051.G>T | | NUDT15 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | OPRD1 | Reference;rs1042114.G>C;rs1042114.G>T;rs10753331.G>A;rs10753331.G>T;rs12749204.A>G;rs204047.G>C;rs204047.G>T;rs204055.T>A;rs204055.T>C;rs204069.A>G;rs204076.T>A;rs204076.T>C;rs204076.T>G;rs2234918.C>G;rs2234918.C>T;rs2236855.C>A;rs2236855.C>G;rs2236857.T>C;rs2236861.G>A;rs2298895.A>T;rs2298896.T>G;rs2298897.C>G;rs3766951.T>C;rs419335.A>G;rs421300.A>C;rs421300.A>G;rs4654327.G>A;rs4654327.G>T;rs482387.G>A;rs482387.G>C;rs508448.A>G;rs529520.A>C;rs529520.A>G;rs533123.G>A;rs533123.G>C;rs569356.A>G;rs581111.A>C;rs581111.A>G;rs581111.A>T;rs6669447.T>C;rs678849.C>G;rs678849.C>T;rs680090.G>A;rs760589.G>A;rs797397.G>A | | OPRK1 | Reference;rs10111937.C>T;rs1051660.C>A;rs1051660.C>G;rs1051660.C>T;rs16918842.C>A;rs16918842.C>T;rs16918875.G>A;rs16918909.A>G;rs16918941.A>G;rs3802279.C>T;rs3802281.T>C;rs3808627.C>G;rs3808627.C>T;rs6473797.T>C;rs6473799.A>G;rs6985606.T>A;rs6985606.T>C;rs7016778.A>T;rs702764.T>C;rs702764.T>G;rs7813478.T>C;rs963549.C>T;rs997917.T>C | | OPRM1 | Reference;rs10457090.A>G;rs10457090.A>T;rs10485057.A>G;rs10485058.A>G;rs10485060.C>A;rs1074287.A>G;rs11575856.G>A;rs12190259.A>C;rs12205732.G>A;rs12209447.C>T;rs12210856.T>G;rs1294092.A>G;rs1319339.T>A;rs1319339.T>C;rs13195018.A>C;rs13195018.A>T;rs13203628.A>G;rs1323040.A>G;rs1323042.G>C;rs1323042.G>T;rs1381376.C>A;rs1381376.C>G;rs1381376.C>T;rs1461773.G>A;rs17174629.A>G;rs17174794.C>G;rs17174794.C>T;rs17174801.A>G;rs17180982.dup;rs17181352.A>G;rs1799971.A>G;rs1799972.C>A;rs1799972.C>G;rs1799972.C>T;rs1852629.T>A;rs1852629.T>C;rs1852629.T>G;rs2010884.G>A;rs2075572.G>C;rs2236256.C>A;rs2236257.G>C;rs2236258.C>G;rs2236258.C>T;rs2236259.T>A;rs2236259.T>C;rs2236259.T>G;rs2281617.C>G;rs2281617.C>T;rs3778148.G>T;rs3778150.T>C;rs3778151.T>C;rs3778152.A>G;rs3778156.A>G;rs3798676.C>T;rs3798677.A>G;rs3798678.A>C;rs3798678.A>G;rs3798683.G>A;rs3798688.G>T;rs3823010.G>A;rs483481.G>A;rs483481.G>C;rs4870266.G>A;rs495491.A>G;rs497976.G>A;rs497976.G>T;rs499796.A>G;rs506247.A>C;rs510769.C>T;rs511435.C>G;rs511435.C>T;rs518596.G>A;rs524731.C>A;rs527434.T>A;rs527434.T>C;rs538174.T>C;rs540825.A>C;rs540825.A>G;rs540825.A>T;rs544093.G>A;rs544093.G>T;rs548646.T>A;rs548646.T>C;rs548646.T>G;rs553202.C>T;rs558025.A>G;rs558948.C>G;rs558948.C>T;rs562859.C>A;rs562859.C>G;rs562859.C>T;rs563649.C>T;rs569284.A>C;rs583664.T>C;rs589046.C>T;rs598160.G>A;rs598160.G>C;rs598682.A>C;rs598682.A>G;rs598682.A>T;rs599548.G>A;rs606545.G>A;rs606545.G>C;rs609148.G>A;rs609148.G>T;rs609623.T>A;rs609623.T>C;rs610231.G>A;rs610231.G>C;rs613355.C>A;rs613355.C>G;rs613355.C>T;rs618207.A>C;rs618207.A>G;rs618207.A>T;rs62436463.C>T;rs62638690.G>T;rs632499.A>C;rs632499.A>G;rs632499.A>T;rs639855.C>A;rs639855.C>G;rs642489.G>A;rs642489.G>T;rs644261.G>A;rs644261.G>C;rs644261.G>T;rs645027.A>G;rs647192.G>A;rs647192.G>C;rs648007.A>C;rs648007.A>G;rs648893.A>G;rs650825.G>A;rs6557337.C>A;rs6557337.C>T;rs658156.A>C;rs658156.A>G;rs658156.A>T;rs671531.A>G;rs671531.A>T;rs675026.A>C;rs675026.A>G;rs677830.C>A;rs677830.C>G;rs677830.C>T;rs681243.T>A;rs681243.T>C;rs6902403.T>C;rs6912029.G>T;rs73576470.A>G;rs7748401.T>G;rs7763748.C>A;rs7763748.C>T;rs7776341.A>C;rs79910351.C>T;rs9282815.C>A;rs9282815.C>T;rs9322446.G>A;rs9322447.A>C;rs9322447.A>G;rs9322447.A>T;rs9322453.G>C;rs9371773.G>A;rs9371776.G>A;rs9384174.C>G;rs9384174.C>T;rs9384179.G>A;rs9384179.G>T;rs9397685.A>G;rs9397685.A>T;rs9397687.C>T;rs9479757.G>A;rs9479779.A>G | | RYR1 | NC\_000019.10:g.38440818G>C;NC\_000019.10:g.38444179C>A;NC\_000019.10:g.38444252G>T;NC\_000019.10:g.38444257A>C;NC\_000019.10:g.38444257A>G;NC\_000019.10:g.38448680\_38448681insGGA;NC\_000019.10:g.38448715G>A;NC\_000019.10:g.38451785C>A;NC\_000019.10:g.38452985C>T;NC\_000019.10:g.38455253C>G;NC\_000019.10:g.38455254T>C;NC\_000019.10:g.38455347T>C;NC\_000019.10:g.38455504G>T;NC\_000019.10:g.38466392G>A;NC\_000019.10:g.38469404A>C;NC\_000019.10:g.38485679T>C;NC\_000019.10:g.38486095A>G;NC\_000019.10:g.38490642A>C;NC\_000019.10:g.38494454G>A;NC\_000019.10:g.38496455G>A;NC\_000019.10:g.38499234T>C;NC\_000019.10:g.38499642C>A;NC\_000019.10:g.38499667G>A;NC\_000019.10:g.38499667G>T;NC\_000019.10:g.38499680T>A;NC\_000019.10:g.38499683G>A;NC\_000019.10:g.38499696C>G;NC\_000019.10:g.38499719A>G;NC\_000019.10:g.38499730G>A;NC\_000019.10:g.38499985A>T;NC\_000019.10:g.38500000G>A;NC\_000019.10:g.38502669C>G;NC\_000019.10:g.38504298G>A;NC\_000019.10:g.38506508C>G;NC\_000019.10:g.38506865C>T;NC\_000019.10:g.38507821C>T;NC\_000019.10:g.38512279G>A;NC\_000019.10:g.38515052C>T;NC\_000019.10:g.38516181T>C;NC\_000019.10:g.38516208G>C;NC\_000019.10:g.38517470T>C;NC\_000019.10:g.38517523T>A;NC\_000019.10:g.38519424C>A;NC\_000019.10:g.38519432A>T;NC\_000019.10:g.38519447A>G;NC\_000019.10:g.38525432C>T;NC\_000019.10:g.38527710G>C;NC\_000019.10:g.38528372G>T;NC\_000019.10:g.38529002G>C;NC\_000019.10:g.38529042C>T;NC\_000019.10:g.38543380A>T;NC\_000019.10:g.38543566G>A;NC\_000019.10:g.38543810C>T;NC\_000019.10:g.38548253A>T;NC\_000019.10:g.38561140G>C;NC\_000019.10:g.38561213C>T;NC\_000019.10:g.38561362G>A;NC\_000019.10:g.38561363G>T;NC\_000019.10:g.38565023T>G;NC\_000019.10:g.38570649C>G;NC\_000019.10:g.38577931A>C;NC\_000019.10:g.38578205G>T;NC\_000019.10:g.38580039\_38580040delinsAA;NC\_000019.10:g.38580041C>A;NC\_000019.10:g.38580126C>G;NC\_000019.10:g.38580397G>C;NC\_000019.10:g.38580416C>T;NC\_000019.10:g.38585078A>G;NC\_000019.10:g.38585099G>A;NC\_000019.10:g.38586190A>G;NC\_000019.10:g.38587362G>C;NC\_000019.10:g.38587363G>C;Reference;rs111272095.C>T;rs111364296.G>A;rs111565359.G>A;rs111657878.T>C;rs111888148.G>A;rs112151058.G>A;rs112196644.A>G;rs112563513.G>A;rs112596687.T>A;rs112772310.G>A;rs113210953.A>G;rs113332073.G>A;rs113332073.G>T;rs117886618.C>G;rs118192113.C>A;rs118192116.C>G;rs118192116.C>T;rs118192121.A>C;rs118192122.G>A;rs118192123.T>C;rs118192124.C>T;rs118192126.A>G;rs118192130.G>A;rs118192135.G>A;rs118192140.C>T;rs118192151.G>A;rs118192151.G>C;rs118192158.G>A;rs118192159.C>G;rs118192160.G>A;rs118192160.G>T;rs118192161.C>T;rs118192162.A>C;rs118192162.A>G;rs118192163.G>A;rs118192163.G>C;rs118192163.G>T;rs118192167.A>G;rs118192168.G>A;rs118192170.T>C;rs118192172.C>T;rs118192175.C>T;rs118192176.G>A;rs118192177.C>G;rs118192177.C>T;rs118192178.C>G;rs118192178.C>T;rs118192181.C>T;rs118204421.C>T;rs118204422.T>C;rs118204423.G>A;rs118204423.G>C;rs121918592.G>A;rs121918592.G>C;rs121918593.G>A;rs121918594.G>A;rs121918594.G>T;rs121918595.C>T;rs121918596.\_38499648delGAG;rs137932199.G>A;rs137933390.A>G;rs138874610.G>A;rs139161723.G>A;rs139647387.A>G;rs140152019.G>A;rs140616359.G>A;rs141646642.C>G;rs141942845.G>A;rs142474192.G>A;rs142474192.G>T;rs143398211.G>A;rs143520367.C>T;rs143987857.G>A;rs143988412.A>G;rs143988412.A>T;rs144336148.G>A;rs144685735.C>T;rs145573319.A>G;rs145801146.C>T;rs146306934.G>A;rs146429605.A>G;rs146504767.G>A;rs146876145.C>T;rs147136339.A>G;rs147213895.A>G;rs147303895.G>A;rs147707463.C>T;rs147723844.A>G;rs148399313.G>A;rs148623597.G>A;rs150396398.G>C;rs151029675.C>T;rs151119428.G>A;rs1801086.G>A;rs1801086.G>C;rs1801086.G>T;rs180714609.G>A;rs186983396.C>G;rs186983396.C>T;rs192863857.C>T;rs193922744.T>G;rs193922745.\_38440752delTGA;rs193922746.A>G;rs193922747.T>C;rs193922748.C>T;rs193922749.C>A;rs193922750.C>A;rs193922751.G>A;rs193922752.A>G;rs193922753.G>A;rs193922753.G>T;rs193922754.G>A;rs193922755.G>A;rs193922756.A>G;rs193922757.C>T;rs193922759.G>A;rs193922760.A>T;rs193922761.G>T;rs193922762.C>A;rs193922762.C>T;rs193922764.C>A;rs193922764.C>G;rs193922764.C>T;rs193922766.G>A;rs193922766.G>T;rs193922767.G>A;rs193922767.G>T;rs193922768.C>A;rs193922768.C>T;rs193922769.T>C;rs193922769.T>G;rs193922770.C>T;rs193922772.G>A;rs193922772.G>T;rs193922775.C>T;rs193922776.C>T;rs193922777.C>T;rs193922781.C>T;rs193922782.T>G;rs193922783.T>A;rs193922788.G>C;rs193922789.G>A;rs193922790.A>T;rs193922791.C>T;rs193922792.G>T;rs193922793.T>A;rs193922795.G>A;rs193922797.G>A;rs193922798.G>C;rs193922799.G>A;rs193922801.A>G;rs193922802.G>A;rs193922803.C>T;rs193922804.A>G;rs193922805.T>G;rs193922806.C>G;rs193922807.G>C;rs193922809.G>A;rs193922810.G>A;rs193922810.G>T;rs193922812.C>T;rs193922813.G>C;rs193922815.G>A;rs193922815.G>C;rs193922816.C>T;rs193922817.C>T;rs193922818.G>A;rs193922819.T>C;rs193922822.C>G;rs193922822.C>T;rs193922824.C>T;rs193922826.C>G;rs193922826.C>T;rs193922827.G>C;rs193922828.G>A;rs193922829.G>A;rs193922830.C>T;rs193922831.T>A;rs193922832.G>A;rs193922833.G>A;rs193922834.G>A;rs193922838.G>A;rs193922838.G>T;rs193922839.G>A;rs193922840.T>G;rs193922842.C>G;rs193922842.C>T;rs193922843.G>T;rs193922844.C>A;rs193922848.A>T;rs193922849.C>A;rs193922850.T>C;rs193922852.G>C;rs193922852.G>T;rs193922853.A>T;rs193922855.C>T;rs193922860.G>A;rs193922862.\_38572267delinsCT;rs193922863.C>T;rs193922864.T>C;rs193922865.T>G;rs193922866.G>A;rs193922867.C>T;rs193922868.G>A;rs193922873.G>A;rs193922873.G>T;rs193922874.T>C;rs193922876.C>T;rs193922877.delA;rs193922878.C>G;rs193922879.G>A;rs193922880.C>G;rs193922883.T>C;rs193922888.G>A;rs193922895.C>A;rs193922896.G>T;rs193922898.T>A;rs199738299.A>G;rs199870223.C>T;rs200766617.G>A;rs201321695.A>G;rs2145447772.G>A;rs2145447772.G>C;rs28933396.G>A;rs28933396.G>T;rs28933397.C>T;rs34390345.A>G;rs34694816.A>G;rs34934920.C>T;rs35180584.C>G;rs35364374.G>T;rs370634440.G>A;rs370634440.G>T;rs372958050.T>C;rs373406011.C>T;rs375626634.T>C;rs375915752.C>T;rs376149732.C>T;rs4802584.C>G;rs537994744.G>A;rs549201486.C>T;rs551223467.C>T;rs553055844.G>A;rs55876273.G>C;rs587784372.C>T;rs63749869.G>A;rs727504129.C>T;rs746818096.T>A;rs747177274.G>C;rs748575133.T>A;rs749040743.G>A;rs751180702.G>A;rs752652072.C>T;rs754476250.C>T;rs754785770.A>G;rs755088027.G>A;rs756850145.A>G;rs757753317.G>A;rs759500310.T>C;rs761616815.G>A;rs762401851.G>A;rs763112609.C>T;rs763352221.C>T;rs767553612.A>G;rs768360593.G>A;rs768535909.T>C;rs769482889.C>T;rs770593660.G>C;rs771058055.G>A;rs771741606.C>T;rs773040531.A>G;rs778241277.G>A;rs781104539.A>G;rs781126470.C>T;rs901087791.G>A;rs914804033.G>A;rs914804033.G>C;rs917523269.C>T;rs936513262.G>A;rs959170123.G>A;rs976108591.A>G;rs995399438.T>C | | SLCO1B1 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*19;\*2;\*20;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*5;\*6;\*7;\*8;\*9 | | TNF | Reference;rs1799724.C>T;rs1799964.T>C;rs1800610.G>A;rs1800629.G>A;rs1800630.C>A;rs1800750.G>A;rs2736195.A>G;rs3093548.C>T;rs3093662.A>G;rs3093726.T>C;rs361525.G>A;rs4248158.C>T;rs4248159.C>A;rs4248160.G>A;rs4248163.C>A;rs4248163.C>G;rs4248163.C>T;rs4647198.C>T;rs4987086.G>A;rs55634887.G>A;rs55994001.C>A;rs55994001.C>T | | TPMT | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*3A;\*3B;\*3C;\*4;\*40;\*41;\*42;\*43;\*44;\*5;\*6;\*7;\*8;\*9 | | UGT1A1 | \*1;\*27;\*28;\*36;\*37;\*6;\*80;\*80+\*28;\*80+\*37 | | UGT1A4 | \*1a;\*1b;\*1c;\*2;\*3a;\*3b;\*4;\*7 | | UGT2B15 | \*1;\*2;\*3;\*4;\*5;\*6;\*7 | | VKORC1 | Reference;rs9923231 variant (T) | | YEATS4 | Reference;rs7297610.C>T | ## Known Limitations of GDA-ePGx, GSAv4-ePGx, and GCRA-ePGx The following known limitations apply to GDA-ePGx, GSAv4-ePGx, and GCRA-ePGx. These limitations arise from missing marker coverage or probe design constraints and may affect the ability to detect, distinguish, or report specific alleles. Where applicable, the expected reporting behavior is described below. * APOE: GSAv4-ePGx and GCRA-ePGx do not support calling E2 and E4 due to the lack of functional probes for rs7412 and rs429358. Illumina is in the process of developing a permanent solution to resolve these probes across our PGx arrays. In the meantime, please reach out to your array specialist to discuss interim solutions for calling APOE with our arrays. * CYP2A6: GDA-ePGx does not support \*5 due to lack of coverage for \*5 core variants. * CYP4F2: for all three products * \*1 and \*2 are not distinguishable due to the lack of probes for rs30193105. Samples with \*2 will be called as \*1. * \*3 and \*4 are not distinguishable due to the lack of probes for rs30193105, while \*3 core variant rs2108622 is covered by all three products. Samples with \*4 will be called as \*3. * UGT1A1: \*28 (rs8175347 \[TA]8) and \*37 (rs8175347 \[TA]9) are not covered in all three PGx products due to the lack of functional probes. * UGT2B15: GSAv4-ePGx and GCRA-ePGx do not support \*4 or \*5 due to the lack of probes for rs4148269 and rs1902023. * CYP2D6: Due to the design of the probes, \*40 (rs72549356\[AAAGGGGCG]3) and \*58 (rs72549356\[AAAGGGGCG]2) cannot be distinguished. As a result, both alleles are reported as \*40. * NUDT15: Due to the design of the probes, \*6 (rs746071566dupGAGTCG) and \*9 (rs746071566delGAGTCG) cannot be distinguished. As a result, both alleles are reported as \*6. * HLA-B\*15:02: GDA-ePGx does not support HLA-B\*15:02 due to the lack of coverage for the tagging variant rs144012689 (T>A). ## PGx Variants Masked in DRAGEN Array During DRAGEN Array star allele calling, poorly performing PGx variants are masked and treated as "No Calls". Star alleles that are solely defined by the masked variants will NOT be called by DRAGEN Array. The tables below provide the variants that are masked per product with each row represents a single variant. The Variant\_ID matches the ID field of the corresponding SNV VCF entry of the PGx product. ### GDA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GDA\_PGx-8v1-0\_20042614\_G | CYP1A2 | ilmnseq\_rs35694136\_ilmnfwd;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup1;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GDA\_PGx-8v1-0\_20042614\_G | CYP2D6 | ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_IlmnRep;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup3;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup3 | | GDA\_PGx-8v1-0\_20042614\_G | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GDA\_PGx-8v1-0\_20042614\_G | UGT1A1 | ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel;ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev;ilmnseq\_rs8175347\_ilmnrev\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev\_ilmndup2;ilmnseq\_rs8175347\_ilmnrev\_ilmndup3 | ### GSAv4-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GSA-PGx-48v4-0\_20079540\_E | CYP1A2 | IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs367543002,ilmnseq\_rs367543002\_ilmnfwd,ilmnseq\_rs367543002\_ilmnfwd\_ilmndup1,ilmnseq\_rs367543002\_ilmnrev\_deg3a1b0\_ilmndup1,rs367543002 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1,ilmnseq\_rs17882687\_ilmnrev\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C9 | 10:96701973,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup1,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | ilmnseq\_rs1135836\_ilmnrev\_deg3a3b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev\_F2BT,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GSA-PGx-48v4-0\_20079540\_E | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | | GSA-PGx-48v4-0\_20079540\_E | UGT1A1 | ilmnseq\_rs8175347.2\_ilmnrev\_F2BTindel\_cei\_ilmndup31 | ### GCRA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | ---------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GCRA-PGx-24v1-0\_20084467\_C | COMT | ilmnseq\_rs7287550\_ilmnfwd\_F2BT | | GCRA-PGx-24v1-0\_20084467\_C | CYP1A2 | IlmnSeq\_rs35694136;IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;rs35694136 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs367543002\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C9 | ilmnseq\_rs774607211\_ilmnrev,ilmnseq\_rs774607211\_ilmnrev\_ilmndup2 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | ilmnseq\_rs2004511\_dup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0\_ilmndup1,ilmnseq\_rs769157652\_ilmnrev\_ilmndup1,seq-rs61737947 | | GCRA-PGx-24v1-0\_20084467\_C | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | # Support and Additional Resources ## Technical Support For support, questions, and feedback on DRAGEN Array, please contact Illumina Tech Support at . ## Additional Resources | Resource | Description | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | [DRAGEN Array Webpage](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html) | Product features and benefits and allows product ordering. | | [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) | Support site for DRAGEN Array which includes installers and product documentation. | | [DRAGEN Array Methylation QC analysis](https://developer.illumina.com/news-updates/dragen-array-1-0-now-supporting-methylation-qc-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 Methylation QC. | | [DRAGEN Array PGx Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 PGx analysis. | | [Infinium Lab Setup and Best Practices](http://support-docs.illumina.com/ARR/infinium-labsetup.htm) | Lab setup and maintenance information for Infinium assays. | | [Evaluation of Infinium Genotyping Assay Controls using GenomeStudio](https://support.illumina.com/content/dam/illumina-support/courses/eval-inf-controls/story_content/external_files/Infinium_Controls_Training_Guide.pdf) | Instructions for evaluating assay controls using GenomeStudio | | [GenomeStudio Genotyping: Evaluating Infinium Assay Controls](https://www.youtube.com/embed/MuDBayIegkg?autoplay=1\&rel=0) | Video instructions for evaluating assay controls using GenomeStudio | | [Infinium Assay Consumables & Equipment List](http://support-docs.illumina.com/ARR/infinium-consumables.htm) | List of consumables and equipment used in Infinium assays. | | [iScan System Product Documentation](http://support-docs.illumina.com/ARR/iscan.htm) | Instructions for operating and maintaining the iScan System. | | [Polygenic Risk Score – Predict](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) | Instructions for using the Polygenic Risk Score – Predict Module. | | [BioInsight Platform Core](https://help.connected.illumina.com/illumina-connected-analytics) | Instructions for using the hosted environment BioInsight Platform Core. | | [BaseSpace Sequence Hub](https://help.basespace.illumina.com/) | Instructions for using the hosted environment BaseSpace Sequence Hub. | | [Emedgene](https://help.connected.illumina.com/emedgene) | Instructions for using Emedgene software | # Frequently Asked Questions 1. **Is DRAGEN Array analysis a local (on-premises) or cloud solution?**\ DRAGEN Array analysis is available locally (on-premises) and cloud. DRAGEN Array Local Analysis utilizes a command-line interface for power users to have granular control and flexibility to support large scale microarray genomic studies. Deployed on Windows or Linux operating systems, the local package is CPU-based and does not require a specialized server or hardware. DRAGEN Array Cloud Analysis utilizes the user-friendly, graphical interface of BaseSpace Sequence Hub to simplify analysis setup and kickoff. 2. **Which Infinium arrays is DRAGEN Array compatible with?**\ Refer to the Product and Analysis Compatibility table in the [Applications](/overview/our-features) section. 3. **How many samples are needed per analysis?**\ **Cytogenetics:** As few as one sample can be used for cytogenetics. Multiple analysis batches can be kicked off and run in parallel. **Genotyping:** As few as one sample can be used for genotyping. Multiple analysis batches can be kicked off and run in parallel. **Pharmacogenomics:** A minimum of 24 samples is required for PGx CNV calling with 22 passing QC. Passing QC is defined as Log R Dev < 0.2. 96 samples are recommended for the most accurate CNV results. Multiple analysis batches can be kicked off and run in parallel. 4. **Which PGx CNVs and star alleles are available?**\ Please refer to the DRAGEN Array [release notes](/support-and-updates/release-notes). 5. **Where can I find demo data?**\ Demo data is available in BaseSpace under the “Demo Data” section. All array data starts with “iScan:” and includes the name of the type of analysis. Supported types of analysis can be found in the [Applications](/overview/our-features) section. 6. **Where can I find assay QC metrics?**\ DRAGEN Array provides assay QC outputs directly. Functional QC metrics such as Autosomal Call Rate, Call Rate, Log R Ratio Std Dev (LogRDev), and Sex Estimate are available in the genotype summary files. Control-probe QC metrics are available in the QC metrics files and can be reviewed in the DRAGEN Array QC Report. See [Output Files](/product-guides/output-files#genotype_summary_files), [QC metrics files](/product-guides/output-files#qc_metrics_files), and [DRAGEN Array QC Report](/product-guides/dragen-array-local-analysis/qc-report) for details. DRAGEN Array QC Report replaces GenomeStudio control dashboard. For legacy assay control dashboard in GenomeStudio, see [Support and Additional Resources](/support-and-updates/support-and-additional-resources). 7. **How does DRAGEN Array handle rescans or duplicate IDATs for the same sample?**\ Duplicate IDAT pairs that resolve to the same Sentrix barcode+position identifier (`SentrixBarcode_A` + `SentrixPosition_A`) are not supported. If multiple IDAT pairs for the same `SentrixBarcode_A` + `SentrixPosition_A` combination are present in the folder tree provided to `--idat-folder`, the software processes only one pair, and the selected pair can differ between operating systems such as Linux and Windows. Before running analysis, ensure the input folder contains only one IDAT pair per `SentrixBarcode_A` + `SentrixPosition_A` combination. # Release Notes The following versions of DRAGEN Array have been released: * [DRAGEN Array v1.4.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.4.0-release-notes) * [DRAGEN Array v1.4.0 + Emedgene V100.40.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.4.0-release-notes/dragen-array-v1.4.0-emg-release-notes) * [DRAGEN Array v1.3.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes) * [DRAGEN Array v1.3.0 + Emedgene V100.39.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes/dragen-array-v1.3.0-emg-release-notes) * [DRAGEN Array v1.2.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.2.0-release-notes) * [DRAGEN Array v1.2.0 EMGv38 Automatic Case Creation Release Notes](/support-and-updates/release-notes/dragen-array-v1.2.0-release-notes/dragen-array-v1.2.0-cyto-emg-release-notes) * [DRAGEN Array v1.1.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.1.0-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.1 Release Notes](/support-and-updates/release-notes/dragen-array-v1.0.1-cloud-methylqc-release-notes) * [DRAGEN Array v1.0.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes) * [DRAGEN Array Genotyping Cloud v1.0.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-genotype-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-methylqc-release-notes) # DRAGEN Array v1.4.0 Release Notes ## **RELEASE DATE** May 2026 ## **RELEASE HIGHLIGHTS** * **New QC Report capability** enabling comprehensive Infinium Array sample QC review via an interactive HTML report, including: * Control Dashboard (replacing the GenomeStudio control dashboard) * Automated QC * Sample QC Heatmaps * Trend Analysis * **Major cytogenetics algorithm and feature enhancements**, including new GAINLOH calling support and improved mosaic detection. * **PGx workflow improvements**, encompassing structural variant star‑allele calling, enhanced handling of variant and star‑allele no‑calls, and redesigned output formats. * **Improved multi‑allelic variant (MAV) calling**, with more robust handling of Infinium I no‑call scenarios. ## **NEW FEATURES IN DETAIL** * Cytogenetics * Added support for detecting GAINLOH (Gain with Loss of Heterozygosity) variants, which are genomic regions characterized by increased copy number (gain) and loss of heterozygosity (LOH). * Added variant size and probe filters for each variant type (e.g., deletion, duplication, LOH, GAINLOH, mosaic) to improve flexibility. * Enhanced mosaic variant detection. The algorithm detects mosaicism at approximately >15% for mosaic states with a one-copy difference (e.g., CN2/CN1, CN2/CN3 in diploid regions; CN1/CN0, CN1/CN2 in haploid regions). * Support detection of mosaic duplication on the male Y chromosome. * CNV and LOH variants are validated against expected LRR patterns (e.g., most probes in an autosomal CN gain region should exhibit positive LRR); variants that do not conform are filtered out. * Fixed an issue in the cyto annotation JSON file where the first variant was missing ISCN annotation. * Removed the 5% mosaic fraction hard cutoff so variants below this threshold can still be reported. * LOH regions smaller than 500 kb that are flanked by copy-neutral regions are removed to reduce false positives. * QC Report * Generates a comprehensive QC report to support quality control review of genotyping and DNA methylation microarray datasets processed with DRAGEN Array. * Enables review of sample‑level and dataset‑level QC metrics, including control metrics and plots, to identify data quality issues and outliers. * Supports single‑dataset QC in the cloud; the local CLI additionally supports multi‑dataset analysis and cross‑run summary views. * Provides interactive visual dashboards, including Control Dashboard, Automated QC, Sample QC Heatmaps, and Trend Analysis. * Output includes sample QC table and interactive HTML report. * Supports configurable QC thresholds via YAML configuration files and in‑report QC Metric Configuration. * For usage instructions, input requirements, and output details, see the [QC Report documentation](/product-guides/dragen-array-local-analysis/qc-report). * PGx * Improved PGx CNV calling robustness through algorithm updates that reduce sensitivity to outlier samples. * Enhanced star‑allele calling accuracy via targeted algorithm improvements across supported PGx genes. * Clear separation of no‑call conditions, explicitly distinguishing "Per‑sample variant no‑calls" and "Variants not represented on the array product". These are now reported separately, with affected star alleles explicitly identified. * Ranked candidate genotypes (star-allele diplotypes) for samples with star-allele no‑call results, reporting the closest star‑allele solutions along with variant delta annotations that describe differences between the sample input and each candidate genotype. * Expanded variant traceability by including RSIDs and probe IDs in both supporting and no‑call variant outputs, facilitating data review, troubleshooting, and audit workflows. * Direct reporting of PGx annotations in CSV outputs, including: genotype, associated metabolizer status, PGx guideline applied. * Redesigned CSV reporting architecture to simplify downstream parsing, improve auditability, and support custom reporting workflows. Output is now organized into multiple clearly defined files, each serving a distinct purpose. * Fixed rare intermittent memory issues occurring during star‑allele calling. * Corrected CYP2D6 exon 9 conversion reporting, resolving an issue where non‑\*36 star alleles (e.g., \*83) were incorrectly reported as \*36 with an underlying \*83 allele. * Genotyping & Core * Sample name used in downstream outputs now follows a defined precedence: See [Sample Name Determination](/product-guides/input-files#sample-name-determination). * The genotyping cloud pipeline now outputs Infinium assay control-based QC metrics and the QC report. * Improved accuracy for MAV calling via the `--use-infI-nc-info` option in [genotype gtc-to-vcf](/product-guides/dragen-array-local-analysis#section-genotype-gtc-to-vcf) ## **BUG FIXES** * Genotyping & Core * The [samplesheet](/product-guides/input-files#section-sample-sheet) now handles empty columns. * During locus combination logic for `gtc-to-vcf`, duplicate INDELS with HET calls on opposite strands (i.e., probe calls of `D/I` + `I/D`) now correctly report HET (`0/1`) in the VCF instead of a no call (`./.`). ## **IMPORTANT NOTES** * DRAGEN Array v1.4 is a self-contained .NET 9 application. While the .NET runtime is bundled with the application, Linux users may need to install native OS-level dependencies. See [Linux Dependencies](/product-guides/dragen-array-local-analysis#linux_dependencies) in the installation guide for details. ## **KNOWN ISSUES** * PGx * Unlike DRAGEN Array v1.3, cluster file mismatches between GTCs and the PGx CNV model are no longer allowed in DRAGEN Array v1.4. The entire analysis run will terminate with an error, rather than only emitting warnings as in previous versions. The recommended workaround is to update the PGx CNV model with the correct cluster file. * There can be some minor differences when running `pgx star-allele call` on Windows vs. Linux. See [DRAGEN Array v1.3.0 Release Notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes). All overall solutions tested for comparison were found to be concordant. * Occasional star-allele solution unresolved variants discordance between Linux and Windows OS with concordant solution ranking. * For PGx star-allele solution no calls, the PGx caller may return alternate closest solutions with a large variant edit distance >= 3 when low quality variant calls are present in the input sample. * Some simple variants have REF and ALT delimited by `_` instead of `>` in the `star_alleles.csv` and metabolizer status JSON files (e.g., `ryr1.38577931a_c` instead of `ryr1.38577931a>c`) * The JSON output for star-allele candidate solutions includes supporting details for only the first allele in the candidate diplotype. This issue is limited to the JSON file; complete supporting information is correctly reported in the CSV outputs. * Genotyping & Core * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * Some multi-nucleotide variant (MNV) designs reverse complement the "Allele1/2 Top" fields in the Final Report * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set its value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. * SNV and indel variants are always treated as separate variants and are not collapsed in gtc-to-vcf even if they are designed to the same locus. * Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * Running `genotype call` or `qc call` with mismatched IDATs and manifest files could produce inaccurate results, but the software will not produce any errors. Users should double-check they are using the correct manifest file for their array if genotype call rates are unexpectedly low and/or QC values have many NaN values. * Very large samplesheets (i.e., >1K samples) can significantly slow analysis. The recommended workaround is to split analyses into batches of at most 1K samples. ## **KNOWN LIMITATIONS** * Cytogenetics * If the genotyping module reports an unknown sex and the cytogenetic caller cannot resolve it, the caller assumes the sample is male. As a result, sex chromosome detection may be inaccurate if the sample is actually female. This behavior is not currently output in the log. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 kb in length. This is often cited as a minimum size limit used to define copy number variants. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions may possibly be false positives. * ISCN annotations are not fully supported for LOH and mosaic variants in the cytogenetic annotation JSON output file. * DRAGEN Array Cytogenetics analysis is intended for constitutional samples only, oncology samples are not supported at this time. * DRAGEN Array Cytogenetics analysis is validated only for specific array platforms: Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, and Infinium CytoSNP-850K BeadChip (iScan and NextSeq550). * The Cytogenetics algorithm assumes that most genomic regions are diploid for optimal performance. As a result, variants in triploid samples may be missed. * DRAGEN Array Cytogenetics analysis may call large events that are broken into smaller pieces and require visual confirmation. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * DRAGEN Array Cytogenetics analysis does not produce mosaic ISCN notation at this time. * Older Cyto model files and cloud configurations are not compatible with v1.4. * QC Report * Hover text in the Control Dashboard overall box plot is truncated at 40 characters, which may hide additional columns. * For very large datasets (many plates or barcodes), buttons in the heatmap menu may appear misaligned. * Faceting behavior groups categorical levels beyond six into an "Other" category rather than displaying all levels individually. * In the Control Dashboard, after viewing a dataset with more than 12,000 samples, you may be unable to switch to a dataset with fewer than 1,000 samples; if this occurs, reload the HTML report. * In the Control Dashboard, once samples are selected, moving or resizing the selection can cause the HTML interface to become unresponsive and the operation may take a long time to finish. * PGx * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. * Star allele calling does not support novel alleles; only alleles defined in the PharmVar and PharmGKB databases are supported. * Genotyping & Core * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * Tabix indexing from DRAGEN Array is not exactly the same as [bcftools index --tbi](https://samtools.github.io/bcftools/bcftools.html#index). For instance, if you run `bcftools index --stats in.vcf.gz` or `bcftools index --nrecords in.vcf.gz`, with certain versions of bcftools, you may get the following error: `index of in.snv.vcf.gz does not contain any count metadata. Please re-index with a newer version of bcftools or tabix.`. If these tools are critical to user's bioinformatics pipelines a workaround would be to unzip and re-index DRAGEN Array VCFs using bcftool's tabix. But please note, these index files may not work in downstream VCF-based DRAGEN Array commands like `pgx star-allele call`. Please use DRAGEN Array end-to-end for analysis flows like the ones detailed in the [Quick Start](/product-guides/dragen-array-local-analysis#section-quick-start) guide. # DRAGEN Array v1.4.0 + Emedgene V100.40.0 Release Notes ## **RELEASE DATE** June 2026 ## **RELEASE HIGHLIGHTS** * Release of `DRAGEN Array – Cytogenetics analysis + Emedgene interpretation 1.4.0` ## **NEW FEATURES IN DETAIL** * See new features for DRAGEN Array – Cytogenetics analysis in the [1.4.0 release notes](/support-and-updates/release-notes/dragen-array-v1.4.0-release-notes#section-v14-new-features-in-details). * See Emedgene V100.40.0 new features in the [V100.40.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.40.0-march-30th-2026). ## **KNOWN ISSUES** * See known issues for DRAGEN Array – Cytogenetics analysis in the [1.4.0 release notes](/support-and-updates/release-notes/dragen-array-v1.4.0-release-notes#section-v14-known-issues). * See known issues for Emedgene in the [V100.40.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.40.0-march-30th-2026#known-issues). ## **KNOWN LIMITATIONS** * See known limitations for DRAGEN Array – Cytogenetics analysis in the [1.4.0 release notes](/support-and-updates/release-notes/dragen-array-v1.4.0-release-notes#section-v14-known-limitations). * See Emedgene V100.40.0 limitations in the [V100.40.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.40.0-march-30th-2026#limitations). * See the [Prerequisites](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-cytogenetics-analysis#section-prerequisites) section in the cloud setup guide for detailed setup instructions. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all users regardless of Emedgene (EMG) subscription status or SNS notification settings. The software does not enforce an EMG subscription in the workgroup. Without an EMG subscription and SNS configuration, the analysis will start and run as the "DRAGEN Array – Cytogenetics analysis" type; however, "Automatic Case Creation on EMG" will not occur. # DRAGEN Array v1.3.0 Release Notes ## **RELEASE DATE** August 2025 ## **RELEASE HIGHLIGHTS** * Provides mosaic fraction estimation for mosaic events. * Improved accuracy of sex chromosome calling, including pseudo-autosomal regions (PAR). * New QC metrics available in cytogenetics JSON output. ## **NEW FEATURES IN DETAIL** * Genotyping & Core * GenomeStudio backwards compatible samplesheet support and related deprecation of separate IDAT and GTC samplesheets. * User-defined data from the samplesheet will get passed to gt\_sample\_summary files during genotyping. * Samples that fail IDAT->GTC conversion during `genotype call` will be added to the gt\_sample\_summary instead of skipped. For these samples, the `Autosomal Call Rate` and `Call Rate` will be set to 0 while the `Log R Ratio Std Dev` and `TGA_Ctrl_5716 Norm R` (when applicable for PGx products) are set to `NaN`. * Removed `--smoothing` parameter from `genotype gtc-to-bedgraph` which was causing wrong values in the LogR Ratios (LRR) bedgraph. * Cytogenetics * Fixed an issue causing cyto calling to crash due to overflow errors for noisy samples in v1.2. * Fixed a memory issue in v1.2 that limited the number of samples able to run to about 200. * Improved accuracy of length normalized median copy number calculation by removing lower limit on included variant size (1 kb). * Added reporting of mosaic fraction for mosaic events. * Added a method for promoting mosaic events above user-defined mosaic fraction. * Reduced verbosity in STDOUT messages produced by annotate command. * Added sample-level Median Log R Dev statistic to annotate JSON output. * Added chromosome-level QC metrics to the annotation JSON output. * Added an event-level QC metric `effective size` to the JSON output. * Variants are filtered by their effective size in the `cyto call` command. In the `cyto annotate` command, they are filtered by the raw size. * Fixed a bug whereby the minimum deletion/LOH/duplication thresholds were shown in the wrong units in the annotation JSON, when set higher than the calling thresholds. * Fixed a bug that prevented cyto CNV variants with quality scores of 0 from appearing in the output json files. * The cytogenetic caller now attempts to resolve sample sex if previously classified as unknown by the upstream genotyping module, enabling more accurate results. A log message is generated when sex is resolved, e.g., "Sample XXX sex updated from Unknown to Male." * Fixed an issue where the `CytoPlatform` was incorrectly always reported as `LCG` in the log regardless of product used. * Pharmacogenomics * Fixed a bug in the `pgx star-allele annotate` command, sample with a reference allele for ABCG2 genes will now be annotated properly using default annotation "Normal" for reference alleles. * Corrected the CYP2A6 \*1 definition. Removed NC\_000019.10:g.40848264\_40848265delinsT variant that was incorrectly added to the CYP2A6 \*1 definition ## **KNOWN ISSUES** * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * Some multi-nucleotide variant (MNV) designs reverse complement the "Allele1/2 Top" fields in the Final Report * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set its value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. * SNV and indel variants are always treated as separate variants and are not collapsed in gtc-to-vcf even if they are designed to the same locus. * Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * During locus combination logic for `gtc-to-vcf`, duplicate INDELS with HET calls on opposite strands (i.e., probe calls of `D/I` + `I/D`) incorrectly report a no call (`./.`) in the VCF instead of HET (`0/1`). * The [samplesheet](/product-guides/input-files#section-sample-sheet) does not handle empty columns. For example this samplesheet: ``` SentrixBarcode_A,SentrixPosition_A,, 204753010023,R02C01,, ``` Will throw the following error: `System.ArgumentException : Duplicate column found. Column names are case-insensitive. Please remove or rename the column from the samplesheet and re-process.` And this example: ``` SentrixBarcode_A,SentrixPosition_A, 204753010023,R02C01, ``` Will produce an empty column/field in the [Genotype Sample Summary files](/product-guides/output-files#genotype_summary_files), e.g., ``` { "SentrixBarcode_A": "204753010023", "SentrixPosition_A": "R02C01", "Sample ID": "204753010023_R02C01", "Sample Name": "204753010023_R02C01", "Sample Folder": "/tmp", "Autosomal Call Rate": 0.99, "Call Rate": 0.99, "Log R Ratio Std Dev": 0.15, "Sex Estimate": "F", "": "" } ``` * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To work around the issue, the user should restart star allele calling or run it on a machine with more memory. * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Very large samplesheets (i.e., >1K samples) can significantly slow analysis. The recommended workaround is to split analyses into batches of at most 1K samples. ## **KNOWN LIMITATIONS** * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * If the genotyping module reports an unknown sex and the cytogenetic caller cannot resolve it, the caller assumes the sample is male. As a result, sex chromosome detection may be inaccurate if the sample is actually female. This behavior is not currently output in the log. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 kb in length. This is often cited as a minimum size limit used to define copy number variants. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions are likely to be false positives. * ISCN annotations are not provided for LOH variants in the cytogenetic annotation JSON output file. * DRAGEN Array Cytogenetics analysis is intended for constitutional samples only, oncology samples not supported at this time. * DRAGEN Array Cytogenetics analysis is validated only for specific array platforms: Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, and Infinium CytoSNP-850K BeadChip (iScan System). * **Note:** DRAGEN Array can process IDAT files from the NextSeq550 for cytogenetic analysis, but this setup hasn’t been formally validated. If you're interested in trying it, check out the demo data in the ‘Demo Data’ section on BaseSpace, which was generated using the iScan system. * DRAGEN Array Cytogenetics analysis may call large events that are broken into smaller pieces and require visual confirmation. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * Tabix indexing from DRAGEN Array is not exactly the same as [bcftools index --tbi](https://samtools.github.io/bcftools/bcftools.html#index). For instance, if you run `bcftools index --stats in.vcf.gz` or `bcftools index --nrecords in.vcf.gz`, with certain versions of bcftools, you may get the following error: `index of in.snv.vcf.gz does not contain any count metadata. Please re-index with a newer version of bcftools or tabix.`. If these tools are critical to user's bioinformatics pipelines a workaround would be to unzip and re-index DRAGEN Array VCFs using bcftool's tabix. But please note, these index files may not work in downstream VCF-based DRAGEN Array commands like `pgx star-allele call`. Please use DRAGEN Array end-to-end for analysis flows like the ones detailed in the [Quick Start](/product-guides/dragen-array-local-analysis#section-quick-start) guide. * There can be some minor differences when running `pgx star-allele call` on Windows vs. Linux. During verification testing, out of 1576 samples, we noticed the following discordance: | Field name | Number of differences | | ---------------------------- | --------------------- | | Collapsed Star-Alleles | 2 | | Missing/Masked Core Variants | 1 | | Solution Long | 1 | | Supporting Variants | 2 | * **Note:** All overall solutions tested for comparison were found to be concordant. * DRAGEN Array v1.3 is not compatible with Emedgene (EMG) v38. I.e., it does not support automatic case creation and you can't manually upload [Cytogenetics VCF Files](/product-guides/output-files#cyto_vcf_file) from v1.3 into EMG. Users should continue to use `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation 1.2.0` for DRAGEN Array + EMG cyto analyses. * Star allele calling does not support novel alleles; only alleles defined in the PharmVar and PharmGKB databases are supported. * CYP2D6 non-`*36` star alleles with exon 9 conversion, such as `*83`, are reported as `*36` with `*83` as an underlying allele. # DRAGEN Array v1.3.0 + Emedgene V100.39.0 Release Notes ## **RELEASE DATE** October 2025 ## **RELEASE HIGHLIGHTS** * Release of `DRAGEN Array – Cytogenetics analysis + Emedgene interpretation 1.3.0` * VCFs generated on Windows now compatible with Emedgene ## **NEW FEATURES IN DETAIL** * See new features for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes#section-new-features-in-detail). * See Emedgene V100.39.0 new features in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#support-for-dragen-array-v1.3). ## **KNOWN ISSUES** * See known issues for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes#section-known-issues). * See known issues for Emedgene in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#known-issues). ## **KNOWN LIMITATIONS** * See known limitations for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/support-and-updates/release-notes/dragen-array-v1.3.0-release-notes#section-known-limitations). * See Emedgene V100.39.0 limitations in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#limitations). * See the [Prerequisites](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-cytogenetics-analysis#section-prerequisites) section in the cloud setup guide for detailed setup instructions. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all users regardless of Emedgene (EMG) subscription status or SNS notification settings. The software does not enforce an EMG subscription in the workgroup. Without an EMG subscription and SNS configuration, the analysis will start and run as the "DRAGEN Array – Cytogenetics analysis" type; however, "Automatic Case Creation on EMG" will not occur. # DRAGEN Array v1.2.0 Release Notes ## **RELEASE DATE** February 2025 ## **RELEASE HIGHLIGHTS** * Whole-genome copy number and loss of heterozygosity (LOH) calling, with VCF output format, for any human genotyping array. * B-allele frequency bedgraph output file to power informative CNV visualizations. * Additional outputs including ISCN and cytoband nomenclature to support cytogenetics applications. ## **NEW FEATURES IN DETAIL** * Cytogenetics Calling and VCF Output * Ability to obtain output files for any human genotyping array. Detection abilities vary by array probe density and spacing. * Detects copy number up to 4+. * Provides Phred scaled quality score to assess the event quality. * Addition of mosaic tagging to detect mosaic deletions and duplications. * Three arrays tested for performance including: * Infinium Global Diversity Array with Cytogenetics-8 * Infinium Global Screening Array with Cytogenetics-24 * Infinium CytoSNP-850K BeadChip using the iScan System * Ability to adjust minimum size and probe number for copy number and LOH event calling * BAF and LRR Bedgraph files * Additional bedgraph file output for B-allele frequency (BAF) for use in visualization. Updated file extensions to differentiate BAF.bedgraph and LRR.bedgraph files. * Added a smoothing parameter to the genotype gtc-to-bedgraph command for LRR.bedgraph (log R ratio bedgraph file) generation for improved visualization. * Bedgraph files are compatible with IGV (Integrative Genomics Viewer) for visualization purposes. * Cytogenetic annotation and JSON Output * Provides summary statistics per sample and per CNV/LOH event. Includes gene count and gene names within each event based on the RefSeq database. * Annotates each event using International System for Human Cytogenomic Nomenclature (ISCN) 2020 and cytoband nomenclature based on Ensembl database. * Pharmacogenomics * Added root command *pgx* for grouping PGx copy number and star allele calling. * Fixed issue causing pgx star-allele annotate command to fail mid-analysis when a particular allele is unknown (e.g. from CYP2E1), an issue introduced in version 1.1. * Added compatibility with new license server (`license.dragen.illumina.com`) for local star-allele calling. ## **KNOWN ISSUES** * Some multi-nucleotide variant (MNV) designs reverse complement the "Allele1/2 Top" fields in the Final Report * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set its value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. * SNV and indel variants are always treated as separate variants and are not collapsed in gtc-to-vcf even if they are designed to the same locus. * Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * During locus combination logic for `gtc-to-vcf`, duplicate INDELS with HET calls on opposite strands (i.e., probe calls of `D/I` + `I/D`) incorrectly report a no call (`./.`) in the VCF instead of HET (`0/1`). * If a sample's sex estimate is called as unknown in the genotyping module, the cytogenetic caller will assume the sample is male. Consequently, detection results on sex chromosomes could be inaccurate if the sample is actually female. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 kb in length. This is often cited as a minimum size limit used to define copy number variants. * ISCN annotations are not provided for LOH variants in the cytogenetic annotation JSON output file. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions are likely to be false positives. * The `cyto annotate` subcommand produces extraneous logs (e.g., `No credential is provided`) that can be safely ignored. * During `cyto call`, there is a log for the `CytoPlatform` currently hardcoded to `LCG` regardless of the product used. This has no bearing on the underlying algorithm and is just what is reported in the log. It can be safely ignored. * A non-default value of the `--smoothing` parameter for the [genotype gtc-to-bedgraph](/product-guides/dragen-array-local-analysis#section-genotype-gtc-to-bedgraph) command triggers a bug causing wrong values in the LogR Ratios (LRR) bedgraph. It is advised users use the default (0), which produces a valid LRR bedgraph with raw signal for visualization purposes. The --smoothing parameter will be disabled in next release of DRAGEN Array. * The `cyto call` command may throw an overflow error in very rare cases when no variants are detected in noisy or low-quality samples. Contact if you encounter this issue. * The minimum deletion/LOH/duplication thresholds shown in the cyto annotation JSON may be shown in the wrong units when set higher than the cyto calling thresholds. * Cyto CNV/LOH variants with quality scores of 0 seen in the cyto call VCF files cannot be passed into the annotation output json files. * CYP2A6 \*1 definition incorrectly includes NC\_000019.10:g.40848264\_40848265delinsT. * `DRAGEN Array – Cytogenetics Calling` and `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` cloud analyses may fail around 200 samples in one batch due to high memory usage. Recommended workaround is to run smaller batches. * Sample with a reference allele for ABCG2 genes will have missing phenotype annotations when running the command `pgx star-allele annotate`. * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To work around the issue, the user should restart star allele calling or run it on a machine with more memory. * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Occasional star-allele solution score discordance between Linux and Windows OS with concordant solution ranking. ## **KNOWN LIMITATIONS** * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * DRAGEN Array Cytogenetics analysis is intended for constitutional samples only, oncology samples not supported at this time. * DRAGEN Array Cytogenetics analysis was only validated for specific array platforms (Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, Infinium CytoSNP-850K BeadChip using the iScan System). * DRAGEN Array Cytogenetics analysis may call large events that are broken into smaller pieces and require visual confirmation. * DRAGEN Array Cytogenetics analysis does not produce mosaic fraction estimation or mosaic ISCN notation at this time. * When using CytoSNP-850Kv1-4\_iScan\_B, GSACyto-24v1\_20044998\_C, or GDACyto-8v1-0\_20047166\_E manifests, DRAGEN Array Cytogenetics analysis will be unable to call events or visualize probes in the PAR (pseudo-autosomal regions). Please reach out to for additional details. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * IDATs originating from NextSeq550 not tested. * Star allele calling does not support novel alleles; only alleles defined in the PharmVar and PharmGKB databases are supported. * CYP2D6 non-`*36` star alleles with exon 9 conversion, such as `*83`, are reported as `*36` with `*83` as an underlying allele. * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. # DRAGEN Array v1.2.0 Emedgene V38.0 Automatic Case Creation Release Notes ## **RELEASE DATE** June 2025 ## **RELEASE HIGHLIGHTS** * Automatic case creation in Emedgene (EMG) following the successful completion of a `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` analysis from BaseSpace (powered by Platform Core). ## **NEW FEATURES IN DETAIL** * See existing features for DRAGEN Array Cytogenetics analysis in the [1.2.0 release notes](/support-and-updates/release-notes/dragen-array-v1.2.0-release-notes) * For more details on the EMGv38 features, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN ISSUES** * See existing issues for DRAGEN Array Cytogenetics analysis in the [1.2.0 release notes](/support-and-updates/release-notes) * For more details on the EMGv38 known issues, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN LIMITATIONS** * Please see the [Prerequisites](/product-guides/dragen-array-cloud-analysis/overview/dragen-array-cytogenetics-analysis#section-prerequisites) section in the cloud setup page for detailed guidance on how to setup this analysis. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all users regardless of Emedgene (EMG) subscription status or SNS notification settings. The software does not enforce an EMG subscription in the workgroup. Without an EMG subscription and SNS configuration, the analysis will start and run as the "DRAGEN Array – Cytogenetics analysis" type; however, "Automatic Case Creation on EMG" will not occur. # DRAGEN Array v1.1.0 Release Notes ## **RELEASE DATE** September 2024 ## **RELEASE HIGHLIGHTS** * New EX PGx beadchips enabled for PGx analysis * Increased coverage of high priority PGx genes * Custom optimized .egt files accepted in PGx analysis * Up-to-date database reflecting latest versions of public PGx resources * DPWG guidelines now available for metabolizer status calling on cloud analysis ## **NEW FEATURES IN DETAIL** * DRAGEN Array supports multiple PGx products * Two new EX PGx beadchips enabled through genotyping, PGx CNV calling, and star allele annotation * Infinium Global Screening Array with Enhanced PGx-48 v4.0 Kit * Infinium Global Clinical Research Array with Enhanced PGx-24 v1.0 Kit * In total 3 PGx products supported: GDA-ePGx, GSAv4-ePGx, GCRA-ePGx. See the [Product & Analysis Compatibility table](/overview/our-features#product_compatibility) for more details. * Increased coverage of high priority PGx genes * Star allele annotation now covers CYP2E1, CYP1A2, ABCG2, CYP2C8, HMGCR, UGT1A4, UGT2B15, F13A1, and HLA-B\*15:02 * CNV calling now covers SULT1A1 * Extended bi-allelic PGx variants from source databases to multi-allelic variants based on the designs in the supported PGx products. * See [PGx Star Allele Coverage](/pharmacogenomics-pgx-reference/pgx-star-allele-coverage) and [PGx CNV Coverage](/pharmacogenomics-pgx-reference/pgx-cnv-coverage) for the full coverage lists. * Allows flexibility for GTCs generated with a custom cluster file (.egt) to be used with the commercial CN model file (.dat). This alleviates the burden to retrain the CN model file. * The cluster file is a required input for the genotype call command in DRAGEN Array. The CN (Copy Number) model file is a required input to the copy-number call command to enable accurate copy number calling for pharmacogenomics. Custom cluster files and CN model files may be required for optimal genotyping and PGx performance. See section Optimizing cluster files and copy number models for additional details. * Database revision reflecting [PGx Allele Definitions and PGx Guidelines](/pharmacogenomics-pgx-reference/pgx-allele-definitions-and-pgx-guidelines) updates. * Standardization of star allele JSON output file * Renamed databaseSources to phenotypeDatabaseSources and starAlleleDatabaseSources * Renamed Phenotype to PhenotypeDatabaseAnnotation * Combined missingVariants and allMissingVariants to missingVariantSites * JSONized supportingVariants and missingVariants at the gene and candidate solution allele levels * Removed redundant info in the Alleles fields * Updated VCF tabix indexing, improving performance and disk usage for SNV VCF. * Added support for symbolic link VCFs as inputs to `star-allele call` subcommand. * Fixed an issue causing the local Linux CLI and Cloud offering to not sort `star_alleles.csv` and various fields in the `metabolizer_status.json` ## **KNOWN ISSUES** * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set its value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. * SNV and indel variants are always treated as separate variants and are not collapsed in gtc-to-vcf even if they are designed to the same locus. * Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * During locus combination logic for `gtc-to-vcf`, duplicate INDELS with HET calls on opposite strands (i.e., probe calls of `D/I` + `I/D`) incorrectly report a no call (`./.`) in the VCF instead of HET (`0/1`). * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Some multi-nucleotide variant (MNV) designs reverse complement the "Allele1/2 Top" fields in the Final Report * Occasional star-allele solution score discorcordance between Linux and Windows OS with concordant solution ranking. * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To workaround the issue, user should restart star allele calling or run it on a machine with more memory. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. * Star allele annotation can fail mid-analysis in rare circumstances when a particular allele is unknown (e.g. for CYP2E1). The observed cases have all been mis-calls for CYP2E1 due to cluster drift. See [Optimizing cluster files](/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for more details. ## **KNOWN LIMITATIONS** * Star allele calling does not support novel alleles but those defined in the PharmVar and PharmGKB databases. * CYP2D6 non-\*36 star alleles with exon 9 conversion, such as \*83, are reported as \*36 with \*83 as an underlying allele. * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * DRAGEN Array were only validated and intended to be used for commercial PGx beadchips with specified manifests (see table above). PGx star allele annotation is not backwards compatable with v1.0 manifest version, e.g., GDA\_PGx-8v1-0\_20042614\_E2 is supported in DRAGEN Array v1.0, GDA\_PGx-8v1-0\_20042614\_G2 is supported in DRAGEN Array v1.1. * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. # DRAGEN Array Methylation QC Cloud v1.0.1 Release Notes ## RELEASE DATE February 2026 ## RELEASE HIGHLIGHTS Hot-fix release patching known issue from v1.0 where analysis could fail for the 80-100 sample size range when using large (>900K probes) arrays. Large arrays are now supported in 80-100 sample size range as well. ## KNOWN LIMITATIONS * Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550 * Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips # DRAGEN Array v1.0.0 Release Notes {% hint style="warning" %} As of Q1 2026, the following v1.0.0 cloud analysis pipeline versions have been deprecated: * DRAGEN Array - Methylation QC - 1-0-0 * DRAGEN Array - PGx - Star Allele Annotation - 1-0-0 * DRAGEN Array - Genotyping - 1-0-0 * DRAGEN Array - PGx - CNV Calling - 1-0-0 {% endhint %} ## **RELEASE DATE** December 2023 ## **RELEASE HIGHLIGHTS** * Improved star allele calling accuracy for Global Diversity Array with enhanced PGx (GDA-ePGx) BeadChips. * Reports star allele calls with quality scores for greater transparency and confidence. * Provides missing variant reporting to improve data quality. ## **NEW FEATURES IN DETAIL** * Star Allele Calling * Star allele calling for genes listed in [PGx Star Allele Coverage](/pharmacogenomics-pgx-reference/pgx-star-allele-coverage) * For in-silico datasets, call rate ≥99%, diplotyping accuracy ≥ 90% * Includes reporting of the hybrid star alleles and allelic specific copy number * Provides quality score that estimates confidence in the star allele call as an additional quality metric * Star allele call rate increased through more robust error tolerance and missing data tolerance * Supporting variants and missing variants are listed and can be further reviewed * Quality score indicates confidence in result considering the missing data * Reports alternative ranked PGx star allele solutions * Allows an alternative to be investigated which may be desirable for samples with low confidence calls * Provides quality score (negative log likelihood) for alternative solutions * Function annotations for PGx genes listed in section [PGx Allele Definitions and PGx Guidelines](/pharmacogenomics-pgx-reference/pgx-allele-definitions-and-pgx-guidelines) * Metabolizer and function annotations are supported for two sets of guidelines from CPIC and DPWG respectively * Activity scores are provided for CYP2C9, CYP2D6, and DPYD * CNV VCF * CNV coverage for genes listed in PGx CNVs Coverage * Compressed and indexed files for size reduction and faster reading * Updated VCF header description to indicate copy number of 5 may be reported by the software * Revised filter field delimiter to comply with VCF 4.3 specification which allows VCF parsing software to parse the file successfully * Genotyping VCF * Compressed and indexed files for size reduction and faster reading ## **KNOWN ISSUES** * During locus combination logic for `gtc-to-vcf`, duplicate INDELS with HET calls on opposite strands (i.e., probe calls of `D/I` + `I/D`) incorrectly report a no call (`./.`) in the VCF instead of HET (`0/1`). * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * SNV and indel variants are always treated as separate variants and are not collapsed in gtc-to-vcf even if they are designed to the same locus. * Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * Symbolic links for VCFs are not supported as the inputs to the “star-allele call” subcommand. * The local Linux CLI and Cloud offering do not sort the star\_alleles.csv and various fields in the metabolizer\_status.json. The local Windows CLI does. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set it's value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. ## **KNOWN LIMITATIONS** * PGx CNV calling and star allele calling and annotation were only validated and intended to be used with GDA\_PGx\_E2 product files. * Using subcommands “unsquash-duplicates” and “filter loci” during gtc-to-vcf conversion should not be used when star allele calling is desired. * Only CPIC guidelines are available for star allele annotation (metabolizer status calling) for the cloud offering. For local, CPIC and DPWG are available. # DRAGEN Array Genotyping Cloud v1.0.0 Release Notes {% hint style="warning" %} As of Q1 2026, the DRAGEN Array - Genotyping - 1-0-0 cloud analysis pipeline version has been deprecated. {% endhint %} ## **RELEASE DATE** March 2024 ## **RELEASE HIGHLIGHTS** * Ability to genotype and produce related reports for human and non-human arrays in the cloud. * Configurable interfaces in BaseSpace that allow for flexibility and easy kick off. ## **NEW FEATURES IN DETAIL** * [SNV VCF File](/product-guides/output-files#snv_vcf_file) * [Final Report](/product-guides/output-files#final_report) * [Locus Summary](/product-guides/output-files#locus_summary) ## **KNOWN ISSUES** * Some multi-nucleotide variant (MNV) designs reverse complement the "Allele1/2 Top" fields in the Final Report ## **KNOWN LIMITATIONS** * Genotyping only works on diploid organisms at this time. Polyploid genotyping is not currently supported. # DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes {% hint style="warning" %} As of Q1 2026, the DRAGEN Array - Methylation QC - 1-0-0 cloud analysis pipeline version has been deprecated. {% endhint %} ## RELEASE DATE May 2024 ## RELEASE HIGHLIGHTS * Adjustable thresholds to determine pass/fail status * Data summary plots for a quick visual check of each analysis batch * Determining detection p-value, beta-values, and m-values from each methylation sample * Deployment on BaseSpace™ Sequence Hub user interface for easy analysis kickoff ## NEW FEATURES IN DETAIL * Adjustable thresholds for 21 built in controls, p-value detection, proportion probes passing, and offset correction within BaseSpace Sequence Hub to customize for user’s study needs * Thresholds are used to assign pass (1) or fail (0) status to each sample * Failed metrics can be highlighted for easy viewing * Pinpoint areas of failure including bisulfite conversion, staining, hybridization, etc. to identify assay steps in need of troubleshooting * Quantitative values for each control removing ambiguity with manual interpretation * Data summary plots with information on passing p-value detection and principal component analysis of beta values * Provides detection p-value, beta-values and m-values for each CG site per sample to use in downstream analysis ## KNOWN ISSUES * Analysis may fail for the 80-100 sample size range when using large (>900K probes) arrays. If encountering this issue users are recommended to increase sample size as a workaround. The issue does not affect sample sizes strictly greater than 100. ## KNOWN LIMITATIONS * Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550 * Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips # Document Revision History The version history for DRAGEN Array product documentation: | Version | Date | Description of Change | | ------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 01 | December 2023 | Initial release | | 02 | March 2024 | Added details for DRAGEN Array v1.0.0 cloud genotype pipeline release. | | 03 | May 2024 | Added details for DRAGEN Array methylation QC pipeline v1.0.0 release. Error correction in the CNV VCF example (CN=4 to CN=5). | | 04 | September 2024 | DRAGEN Array v1.1.0 release | | 05 | February 2025 | DRAGEN Array v1.2.0 release | | 06 | February 2025 | Updated DRAGEN Array v1.2.0 release notes | | 07 | June 2025 |

• Updated DRAGEN Array v1.2.0 release notes: added gtc-to-bedgraph LRR smoothing bug. "Bedgraph Smoothing window size" disabled in cloud interface.

• Added details for DRAGEN Array - Cytogenetics analysis + Emedgene interpretation pipeline 1.2.0 release and corresponding release notes

| | 08 | August 2025 | DRAGEN Array v1.3.0 release. Rename of "CNV and LOH Calling" to "Cytogenetics analysis" | | 09 | October 2025 | `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` cloud pipeline v1.3.0 release | | 10 | February 2026 | [DRAGEN Array methylation QC pipeline v1.0.1 release](/support-and-updates/release-notes/dragen-array-v1.0.1-cloud-methylqc-release-notes) | | 11 | March 2026 |

• Added details for the BaseSpace Planned Analyses feature associated with BaseSpace 7.44.0 release

• Major restructuring of Cloud Analysis section

| | 12 | May 2026 | DRAGEN Array v1.4.0 release | | 13 | June 2026 | `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` cloud pipeline v1.4.0 release | | 14 | Aug 2026 | ICA to BioInsight Platform Core branding; BSSH UI update; PGx coverage page and v1.4 limitations updates | # Welcome to DRAGEN Array DRAGEN (Dynamic Read Analysis for GENomics) Array secondary analysis is a powerful bioinformatics software for Illumina Infinium array-based assays. DRAGEN Array uses cutting-edge data analysis tools to provide accurate, comprehensive, and highly efficient secondary analysis to maximize genomic insights and meet your research needs across multiple applications. DRAGEN Array is offered as a local package with command-line interface (no specialized server or hardware required) and as a cloud-based package with an intuitive graphical user interface, as summerized in the table below.
DescriptionKey featuresLocal analysisCloud analysis
GenotypingProvides genotyping results for any human Infinium genotyping array.
  • Greater than 99.5% genotyping accuracy
  • Genotyping VCF in as little as 35 seconds per sample
PGx – CNV callingProvides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Greater than 95% PGx CNV accuracy
PGx – star allele annotationProvides PGx star allele and variant coverage across 2400+ targets for over 50 genes, plus PGx CNV and genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Assess hard to discern PGx genes, including the elusive CYP2D6 with greater than 97% call rate
  • Obtain all PGx analysis results in ~1 minute per sample
Methylation QCProvides high-throughput, quantitative methylation quality control for Infinium methylation arrays.
  • 21 algorithm-based quantitative control metrics with adjustable thresholds
  • Data summary plots
  • Proportion of CG probes passing with user defined p-value threshold
Cytogenetics analysisProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays.
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
Cytogenetics analysis + Emedgene interpretationProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays with added visualization and case management in Emedgene
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
This product documentation describes the installation and setup, analysis execution, and result outputs. For the latest updates and release details, see the [DRAGEN Array Release Notes](/dragen-array-v1.3/reference/release-notes). See [Introducing DRAGEN™ Array 1.0 for Infinium™ Array-Based Pharmacogenomics Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) for additional details on DRAGEN Array genotyping, PGx CNV calling and PGx star allele annotation. # DRAGEN Array Applications The following Types of Analysis are currently supported by DRAGEN Array: * DRAGEN Array – Genotyping * DRAGEN Array – PGx – CNV calling * DRAGEN Array – PGx – Star allele annotation * DRAGEN Array – Methylation QC * DRAGEN Array – Cytogenetics analysis * DRAGEN Array - Cytogenetics analysis + Emedgene interpretation ### Product & Analysis Compatibility These products/beadchips have been verified to be compatible with the following analyses and versions of DRAGEN Array: | Manifest Name | DRAGEN Array Cloud Version(s) | DRAGEN Array Local Version(s) | Analysis | Genome(s) | | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------- | ----------------------------- | -------------------------------------------------------------- | -------------- | | [BovineSNP50\_v3\_A](https://support.illumina.com/array/array_kits/bovinesnp50-beadchip-kit.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | UMD3 | | [GDA-8v1-0\_D](https://support.illumina.com/array/array_kits/infinium-global-diversity-array.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV calling | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0 | v1.0 | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GSA-24v3-0\_A](https://www.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [PRSbooster\_20083382\_A](https://support.illumina.com/array/array_software/gda-prs.html) | v1.1+ | v1.0+ | DRAGEN Array – Genotyping | GRCh37 | | [EPIC-8v1-0\_B5](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [EPIC-8v2-0\_A2](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [MSA-48v1-0\_20102838\_A1](https://support.illumina.com/array/array_kits/infinium-methylation-screening-array.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [CytoSNP-850Kv1-4\_iScan\_B](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis + Emedgene interpretation | GRCh37, GRCh38 | | [CytoSNP-850Kv1-4\_NS550\_B](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | v1.3+ | v1.3+ | DRAGEN Array – Cytogenetics analysis + Emedgene interpretation | GRCh37, GRCh38 | | [GSACyto-24v1\_20044998\_C](https://support.illumina.com/array/array_kits/infinium-global-screening-array-cyto-24.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis + Emedgene interpretation | GRCh37, GRCh38 | | [GDACyto-8v1-0\_20047166\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-array-cyto-8.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis + Emedgene interpretation | GRCh37, GRCh38 | | [CytoSNP-850Kv1-4\_iScan\_B](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis | GRCh37, GRCh38 | | [CytoSNP-850Kv1-4\_NS550\_B](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | v1.3+ | v1.3+ | DRAGEN Array – Cytogenetics analysis | GRCh37, GRCh38 | | [GSACyto-24v1\_20044998\_C](https://support.illumina.com/array/array_kits/infinium-global-screening-array-cyto-24.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis | GRCh37, GRCh38 | | [GDACyto-8v1-0\_20047166\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-array-cyto-8.html) | v1.2+ | v1.2+ | DRAGEN Array – Cytogenetics analysis | GRCh37, GRCh38 | ## DRAGEN Array – Genotyping | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides genotyping results for any human Infinium genotyping array. | | Variant types detected |

SNV

Indel

| | Sample minimum | 1 sample | | Arrays supported | Any human Infinium genotyping array including custom and semi-custom to create a SNV VCF output. Illumina provides [Genome FASTA Files](/dragen-array-v1.3/product-guides/input-files#toc150786139) required to map to the reference genome for human, genome build 37 and 38. DRAGEN Array Cloud offers additional output formats including Locus Summary and Final Report which are applicable for Infinium arrays for human and non-human species. | | Related Local Commands |

genotype call

genotype gtc-to-vcf

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Genotyping** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on cloud and local]

• TBI Index File \[optional on cloud and local]

Per analysis batch:

• Genotype Summary Files

• Final Report \[cloud only]

• Locus Summary \[cloud only]

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array – PGx – CNV calling | Item | Description | | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

pgx copy-number call

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – CNV calling** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array – PGx – Star Allele Annotation | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides PGx annotation on over 50 genes, plus PGx CNV and genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

Star allele diplotype

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf

pgx copy-number call

pgx star-allele call

pgx star-allele annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – Star Allele Annotation** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• PGx Database File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

• Star Allele JSON File

Per analysis batch:

• Star Allele CSV File

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: Per sample analysis.

Cloud: Per sample analysis. iCredits to store data as needed.

Visit the Illumina Product Page to learn more.

| ## DRAGEN Array – Methylation QC | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides methylation QC for Infinium methylation arrays. | | Variant types detected | N/A | | Sample minimum | 1 sample | | Arrays supported | Recommended thresholds and all built-in control probes are available for Methylation Screening Array (MSA) and MethylationEPIC (v1 & v2) originating from iScan. In non-human and custom arrays, availability of built-in QC probes may vary, and failure thresholds must be defined by the user. | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Methylation – QC** from the dropdown. Adjust customizable thresholds as desired. Further detail can be found in Additional information for [DRAGEN Array Methylation QC](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis#dragen-array-methylation-qc). A maximum of 1152 samples are supported. | | Inputs |

• IDAT(s) \[from iScan instrument]

• Manifest Files \[may be pre-setup on cloud]

• Sample Sheet \[optional on cloud]

| | Outputs |

Per sample:

• Methylation Control Probe Output File

• Methylation CG Output File

Per analysis batch:

• Methylation Sample QC Summary Files

• Methylation Sample QC Summary Plots

• Methylation Principal Component Summary

• Methylation Manifest Files

• Methylation Logs and Error Files

| | Cost | Cloud: [iCredits](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. | ## DRAGEN Array – Cytogenetics analysis | Item | Description | | ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatability) | | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

genotype gtc-to-bedgraph

cyto call

cyto annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Cytogenetics analysis** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Cytogenetics Model File \[pre-setup on cloud]

• Cytogenetics Database File \[only necessary for local]

• Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional on cloud]

• SNV VCF File \[optional on local and cloud]

• TBI Index File \[optional on local and cloud for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array - Cytogenetics analysis + Emedgene interpretation | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling. This analysis type integrates with Emedgene via [Automatic Case Creation from ICA](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) on cloud only. | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatability) | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup]

• Cluster File \[may be pre-setup]

• Cytogenetics Model File \[may be pre-setup]

• Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional]

• SNV VCF File \[optional]

• TBI Index File \[optional for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files

Per analysis batch:

• Genotype Summary Files

• Warning/Error Messages

| | Cost | Cloud: [iCredits](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. As well as additional sample-based costs if uploaded into the [Emedgene](https://help.connected.illumina.com/emedgene) interface. | # DRAGEN Array Cloud Analysis DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. This documentation is organized into two main sections: ## [Analysis Type Details](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview) Learn about the specific configuration options, quality control recommendations, and requirements for each type of analysis supported by DRAGEN Array Cloud. Each analysis type has unique settings and best practices that help ensure optimal results. [View Analysis Type Details →](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview) ## [BaseSpace Operations](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1) Step-by-step guidance for using BaseSpace Sequence Hub to run DRAGEN Array analyses, including setup, launching analyses, viewing outputs, managing data, and troubleshooting. [Get Started with BaseSpace →](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1) # Analysis Type Details ## DRAGEN Array Cloud Analysis Overview DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. DRAGEN Array Cloud supports multiple types of analysis, each with specific configuration options and requirements. Select an analysis type below to learn more about its specific configuration options, thresholds, and recommendations: * [DRAGEN Array Genotyping](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview/dragen-array-genotyping) * [DRAGEN Array PGx CNV Calling](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview/dragen-array-pgx-cnv-calling) * [DRAGEN Array PGx Star Allele Annotation](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview/dragen-array-pgx-star-allele) * [DRAGEN Array Cytogenetics Analysis](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview/dragen-array-cytogenetics-analysis) * [DRAGEN Array Methylation QC](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview/dragen-array-methylation-qc) For information about supported products, versions, and genome builds for each analysis type, see [DRAGEN Array Applications](/dragen-array-v1.3/overview/our-features). # DRAGEN Array Cytogenetics Analysis ### DRAGEN Array - Cytogenetics Analysis #### Cytogenetics Threshold Adjustment When using **DRAGEN Array – Cytogenetics analysis** or **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** cloud analysis types, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on results objectives. Adjusting thresholds will impact the number of events called and thus, the output in the VCF and JSON files. The recommended thresholds/settings are pre-set within the software for any new configurations: | Threshold | New Config | Min Value | Max Value | | ------------------------- | ---------- | --------- | --------- | | GTC Output | False | N/A | N/A | | SNV VCF Output | False | N/A | N/A | | CNV minimum size (kb) | 0 | 0 | 250000 | | CNV minimum probes | 10 | 0 | 250000 | | LOH minimum size (kb) | 3000 | 0 | 250000 | | LOH minimum probes | 500 | 0 | 250000 | | CNV Smoothing window size | 5 | 0 | 1000 | ### DRAGEN Array - Cytogenetics analysis + Emedgene interpretation This analysis type integrates with [Emedgene](https://help.connected.illumina.com/emedgene) to display results in a user-friendly interface. **Note:** Only specific versions of Emedgene and DRAGEN Array are compatible with each other. For more details see the [compatibility table](https://help.emg.illumina.com/emedgene-analyze-manual/supported-vcfs-variant-callers#compatibility-with-dragen-and-dragen-array-variant-callers) on the Emedgene help site. #### Prerequisites * You'll need an additional Emedgene subscription to be either "Array", "Professional", or "Enterprise" tier. You can also follow the [Illumina Software Registration Guide](https://help.connected.illumina.com/account-management/rg-registration) to obtain that subscription. * To ensure proper integration with Emedgene (EMG), [ICA notifications](https://help.ica.illumina.com/project/p-notifications) must be enabled for the specific ICA BSSH-managed project. EMG relies on these notifications to detect when an analysis has successfully completed. To configure SNS ([Amazon Web Services Simple Notification Service](https://aws.amazon.com/sns/)) events in your managed ICA BSSH-managed project, follow these steps: * In the [ICA portal](https://ica.illumina.com/ica/), in the ICA BSSH-managed project (e.g. "BSSH Your Workgroup Name") navigate to the **Notifications** section via the left-hand menu. * Click **+ Create**, then select **ICA Event**. * Fill in the required fields as follows: * **Event:** Analysis Success * **Type:** SNS * **Address:** Provide the correct address based on your region (contact if unsure). * **Payload Version:** v4 * **AWS Region:** This will be auto-populated based on the provided address. * (Recommended) Click **Send Test Message** to verify the configuration. * Click **Save** to complete the setup. For more details on the prerequisites for this analysis, see the [Automatic Case Creation from ICA](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) section in the Emedgene User Guide.\ For more details on limitations for this analysis see the [release notes](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/product-guides/reference/release-notes/dragen-array-v1.2.0-release-notes/dragen-array-v1.2.0-cyto-emg-release-notes.md) #### Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/dragen-array-v1.3/overview/our-features#_product_compatability) table. # DRAGEN Array Genotyping ## Custom Configuration Options When using **DRAGEN Array – Genotyping** cloud analysis type, you have the following configuration options available via the "Add Custom Configuration" option in Configuration Settings: * **Output File Selection**: Flexibility to turn off/on specific output files * VCF output can be toggled on or off * Final Report output can be toggled on or off * **GenCall Score Cutoff**: Adjustable threshold for genotype calling quality ## Recommendations * **For non-human species**: It is recommended to turn off VCF output, as VCF generation requires genome mapping which is only provided for human genomes (GRCh37 and GRCh38). * **For large sample numbers**: It is recommended to turn off Final Report output, as Final Report files can become very large with high sample counts and may impact performance. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/dragen-array-v1.3/overview/our-features#_product_compatability) table. # DRAGEN Array Methylation QC ## Methylation QC Threshold Adjustment When using **DRAGEN Array – Methylation – QC** cloud analysis type, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on study objectives. Adjusting thresholds will impact the pass or fail status of samples in the output files. Illumina recommends thresholds for MethylationEPIC v1 & v2 and Methylation Screening Array (MSA). Users may use these thresholds as a starting point when defining thresholds for their custom or semi-custom BeadChip or other Infinium Methylation arrays. Further tuning may be required based on BeadChip used, laboratory conditions, iScan settings, bisulfite conversion methods, FPPE sample type, etc. A dataset deemed acceptable to the user based on proportion probes passing can be used for these additional threshold adjustments. To customize thresholds, use the toggle to allow additional thresholds to be displayed and adjust as desired by typing in a numeric value or using the arrows to adjust up or down. Further detail of these thresholds including calculation method can be found in the [Methylation Sample QC Summary Files](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/product-guides/dragen-array-cloud-analysis/output-files.md#methyl_qc_report) section. The recommended thresholds are pre-set within the software for MethylationEPIC and Methylation Screening Array with the following values: | Threshold | Methylation Screening Array | MethylationEPIC | | ----------------------------------- | --------------------------- | --------------- | | Restoration[^1] | 0 | 0 | | StainingGreen | 5 | 5 | | StainingRed | 5 | 5 | | ExtensionGreen | 5 | 5 | | ExtensionRed | 5 | 5 | | HybridizationHighMedium | 1 | 1 | | HybridizationMediumLow | 1 | 1 | | TargetRemoval1 | 1 | 1 | | TargetRemoval2 | 1 | 1 | | BisulfiteConversion1Green | 1 | 1 | | BisulfiteConversion1BackgroundGreen | 0.5 | 1 | | BisulfiteConversion1Red | 1 | 1 | | BisulfiteConversion1BackgroundRed | 0.5 | 1 | | BisulfiteConversion2 | 0.5 | 1 | | BisulfiteConversion2Background | 0.5 | 1 | | Specificity1Green | 1 | 1 | | Specificity1Red | 1 | 1 | | Specificity2 | 1 | 1 | | Specificity2Background | 1 | 1 | | NonpolymorphicGreen | 2.5 | 5 | | NonpolymorphicRed | 3 | 5 | | BgCorrectionOffset | 3000 | 3000 | | PvalThreshold | 0.05 | 0.05 | The first 21 rows in the tables correspond to the 21 control metrics used in the methylation sample QC. See section [Methylation Sample QC Summary Files](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/product-guides/dragen-array-cloud-analysis/output-files.md#methyl_qc_report) for details. ## DRAGEN Array Methylation QC and GenomeStudio Methylation Module Differences DRAGEN Array Methylation QC software provides automated methylation sample QC using assay control probes on the Infinium Methylation Arrays. Unlike the manual visual QC in GenomeStudio, DRAGEN Array ultilizes 21 numerical metrics defined based on the control probes and uses standard thresholds to determine pass/fail status of a sample. Unlike GenomeStuio, probe detection rate (proportion of probes passing at a given p-value threshold) is not utilized to determine sample pass/fail status in DRAGEN Array. For more information, see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note. DRAGEN Array Methylation QC performs background normalization, dye bias correction, and detection p-value calculation differently in comparison to the GenomeStudio Methylation module, leading to differences in probe detection p-values and detection rates. For the GenomeStudio Methylation Module, non-cancer samples at standard DNA input typically have detection rate > 96%. The detection rates from DRAGEN Array Methylation QC are typically lower compared to GenomeStudio, because the detection p-value from DRAGEN Array is more stringent than that from the GenomeStudio Methylation Module. The table below shows example detection rates from the DRAGEN Array Methylation QC software from MSA (Methylation Screening Array) datasets. | Dataset | Min detection rate | Mean detection rate | Sample Count | | ------- | ------------------ | ------------------- | ------------ | | A | 86% | 93% | 220 | | B | 61% | 83% | 951 | | C | 63% | 85% | 34 | | D | 77% | 85% | 22 | Note that only samples passing QC are included and all samples are at or above 50ng DNA input. Detection p-value threshold 0.05. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/dragen-array-v1.3/overview/our-features#_product_compatability) table. [^1]: If FFPE restore kit is used, Restoration threshold should be increased from 0 to 1. # DRAGEN Array PGx CNV Calling ## Quality Control Recommendations For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. The metric is provided in the genotyping sample summary file (gt\_sample\_summary.csv). For more details, see the explanation for the [local analysis](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#run-dragen-array-local). ## Semi-Custom PGx Products ### Running Semi-Custom PGx Analysis on Cloud Detailed notes on running this analysis for local can be found [here](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/product-guides/dragen-array-cloud-analysis/dragen-array-local-analysis/README.md#pharmacogenomic-analysis-for-semi-custom-arrays). But for cloud, a workaround is necessary because semi-custom product samples are filtered from the BeadChip table in BaseSpace. Follow these steps: 1. Select an existing commercial product configuration (e.g., `GDA_PGx-8v1-0_G4 - GRCh38`) 2. Kick off an analysis using the **Import Sample Sheet** option for the semi-custom product samples ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/dragen-array-v1.3/overview/our-features#_product_compatability) table. # DRAGEN Array PGx Star Allele Annotation ## Quality Control Recommendations For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). For more details, see the explanation for the [local analysis](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#run-dragen-array-local). ## Custom Configuration Options When using **DRAGEN Array – PGx – Star allele annotation** cloud analysis type, you have the following configuration option: * **Metabolizer Status Database**: Option to change the default metabolizer status database * Default: [CPIC](https://cpicpgx.org/) (Clinical Pharmacogenetics Implementation Consortium) * Alternative: [DPWG](https://www.pharmgkb.org/page/dpwg) (Dutch Pharmacogenetics Working Group) ## Semi-Custom PGx Products ### Running Semi-Custom PGx Analysis on Cloud Detailed notes on running this analysis for local can be found [here](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/product-guides/dragen-array-cloud-analysis/dragen-array-local-analysis/README.md#pharmacogenomic-analysis-for-semi-custom-arrays). But for cloud, a workaround is necessary because semi-custom product samples are filtered from the BeadChip table in BaseSpace. Follow these steps: 1. Select an existing commercial product configuration (e.g., `GDA_PGx-8v1-0_G4 - GRCh38`) 2. Kick off an analysis using the **Import Sample Sheet** option for the semi-custom product samples ### Important Notes for Semi-Custom Arrays * PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. * When designing a semi-custom array using a commercial Infinium PGx array backbone (such as the Global Diversity Array with enhanced PGx), it is important to retain all backbone content in the design, as removing content could decrease the quality of results. For detailed information on running complete semi-custom PGx analysis (including steps with semi-custom product files and commercial product files), see the [Pharmacogenomic Analysis for semi-custom arrays](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#toc150786131) section in the DRAGEN Array Local Analysis documentation. ## Applicable Arrays For specific product compatibility, see the [Product & Analysis Compatibility](/dragen-array-v1.3/overview/our-features#_product_compatability) table. ## Additional Resources * [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.3/reference/pgx-allele-definitions-and-pgx-guidelines) * [PGx Star Allele Coverage](/dragen-array-v1.3/reference/pgx-star-allele-coverage) * [PGx CNV Coverage](/dragen-array-v1.3/reference/pgx-cnv-coverage) # BaseSpace Microarray on BaseSpace Sequence Hub provides cloud-based analysis capabilities for Illumina microarray data. The platform supports genotyping, PGx, cytogenetics and other analyses through [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features). It also supports [polygenic risk score calculations](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm). ## Prerequisites The following prerequisites are needed to get started: * **Illumina Connected Analytics subscription**: An ICA Basic, Professional or Enterprise subscription can be used which include access to BaseSpace Sequence Hub. Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) to register the software. * **Workgroup setup**: Workgroups must be created before login. Using a workgroup allows all members of the workgroup to share access to resources, analyses, and data. Learn more about [managing a Workgroup](https://help.basespace.illumina.com/collaborate/manage-workgroups). * The workgroup owner must be a member of the workgroup with the "Has Access" role assigned. Do not use the "Has Access + Archive" role. * Designating a workgroup as 'Collaborative' allows projects to be shared with collaborators or Illumina Tech Support to assist with troubleshooting. To create a collaborative workgroup, select the Enable collaborators outside of this domain checkbox during workgroup creation. * **Software consumables**: iCredits can be purchased for storage on the cloud platform and analysis pipelines with a compute charge. Per sample analysis can be purchased for relevant pipelines as listed in [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm). Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) (found under *Example 3: Configuring the Software Consumables*) to register the software consumables. * **\[Optional] iScan integration**: The iScan System is integrated with Illumina Connected Platform and can send IDATs for further analysis. The iScan System must be running iScan Control Software version 4.2.1 or later. * [Instructions to Use Illumina Connect Analytics (ICA) with the iScan System](http://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/UseICA_fIS.htm) * [Troubleshooting iScan integration](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/troubleshoot-iscan) * **EULA acceptance**: Accept all necessary End User License Agreements in BaseSpace Sequence Hub before scanning begins. * **Internet connection**: For uploading product files or IDATs, a network connection 1 GbE or faster is recommended. Note: Accessioning BeadChips before scanning and starting analysis is no longer a required step and has been automated within the system. ## Accessing the Microarray Hub The Microarray Hub is the central location for managing microarray data and analyses on BaseSpace Sequence Hub. Before using the Microarray Hub, ensure workgroup context is being used so all data and analyses can be viewed by all members of your workgroup. The name of your workgroup should appear in the top right corner. Use the following steps to access the Microarray Hub on BaseSpace Sequence Hub: 1. Select the **Runs** tab 2. Select **New Run** 3. Select **Microarray Analysis Setup** ## Microarray Hub Tabs The Microarray Hub contains two tabs: * [**Data Management**](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/manage-data) - View and manage scanned IDAT files, and start analyses with data that is ready. * [**Planned Analyses**](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/plan-analysis) - View and manage planned analyses that will auto-launch when sample data becomes available. # Manage Data The Data Management tab allows you to view and manage all your scanned IDAT files in the cloud. From this tab, you can also start an analysis with data that is ready. To access this tab, navigate to the [Microarray Hub](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1) and select **Data Management**. ## Viewing Data To view your scanned array data, use the filtering and sorting options: * Filter by **Upload Status** to see files based on their current state. * Sort and filter by **Upload Date** to find files from a specific time period. ## Deleting Data To delete IDAT files: * Check boxes for individual samples on the left-hand side, or * Use the top checkbox to select all samples on the current page for bulk deletion. {% hint style="info" %} Deleting the selected items will permanently delete them and the action cannot be undone. Deleting items can affect ongoing analysis. Ensure there is no ongoing analysis with the selected items before proceeding. {% endhint %} ## Starting an Analysis Select **Start an analysis** to begin an analysis with your scanned data. See [Launch Analysis](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/launch-analysis) for detailed steps on configuring and launching an analysis. # Launch Analysis ## Running Analysis This page describes how to start an analysis immediately using data that has already been scanned and uploaded. To access this workflow, navigate to the [Microarray Hub](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1), select the **Data Management** tab, and select **Start an analysis**. {% hint style="info" %} If you want to set up an analysis before your samples have been scanned, see [Planned Analyses](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/plan-analysis) to create an analysis that will auto-launch when sample data becomes available. {% endhint %} Use the following steps to configure and launch an analysis: 1. Enter the Analysis Name 2. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 3. Select the Type of Analysis\ Further detail of each Type of Analysis is available in [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) 4. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected. Details on file name constraints can be found in the [ICA documentation](https://help.ica.illumina.com/project/p-data#file-folder-naming). Custom configuration options vary by Type of Analysis selected. More details are available in section [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features). 5. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features). 6. Select Next 7. Select either **Import Sample Sheet,** **Select BeadChips,** or **Import IDAT Files** * **Import Sample Sheet** presents a link to upload sample sheet. Users may download a template sample sheet by selecting the Download Template link. * **Select BeadChips** allows users to select BeadChips from the displayed list of available BeadChips. If selecting specific samples within the BeadChip is desired the Import Sample Sheet option should be used. * **Import IDAT Files** allows users to upload the IDAT files from a local folder to the cloud platform for use with the current and future analyses by users within the same workgroup. 8. Select **Launch Analysis** ## View Outputs 1. On the Analyses tab, view the analysis status, e.g., initializing or complete. 2. After the analysis is complete, select the analysis and select the Files tab. 3. From the Files tab, select the Output folder. ## Troubleshooting IDAT Import ### Checking Import Status The best way to check the import status is to go to the [Data Management](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/manage-data) page. ### Sample is Not Ready For Analysis There are background processes necessary for analysis that run after the IDAT upload session has completed. In most cases these processes should take a few minutes at most after which you may launch analysis. If after some time there are samples that are still not marked ready for analysis, try re-importing the IDATs for the affected samples. If the issue persists, please contact Illumina Tech Support at . ### Product Mismatch The system requires a product association for every BeadChip. Please ensure that the product associated with the selected analysis configuration is also associated with the IDATs selected for import. Mismatch between the IDAT product identity and the analysis configuration selected may affect the ability to select samples for analysis. Contact to resolve product mismatch issues. ### Upload Timeout There is an idle timeout that may take effect for longer uploads. This means that if you are inactive for more than the set timeout duration you will be automatically logged out and file upload will stop. To avoid any interruption with the file upload, we recommend that you stay active on the page while your file is uploading. Alternatively, if you plan on uploading large amount of samples at a time, we suggest breaking your upload into multiple batches to avoid hitting the timeout. The timeout duration may be configured for enterprise domains by following the steps found in [Session Management](https://help.basespace.illumina.com/manage-your-account/manage-enterprise-domain#session-management). # Plan Analysis The Planned Analyses tab allows you to view and manage planned analyses that are configured to auto-launch when sample data becomes available. This is useful when you want to set up an analysis before your samples have been scanned. To access this tab, navigate to the [Microarray Hub](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1) and select **Planned Analyses**. {% hint style="info" %} If your sample data is already available and you want to start an analysis immediately, see [Launch Analysis](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1/launch-analysis). {% endhint %} ## Planning an Analysis Planning an analysis allows you to configure an analysis before your sample data has been scanned and uploaded. Once configured, the planned analysis will automatically launch when all sample data becomes available. Select **Plan an analysis** from the Planned Analyses tab to create a new planned analysis. Use the following steps to configure a planned analysis: 1. Enter the Analysis Name 2. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 3. Select the Type of Analysis\ Further detail of each Type of Analysis is available in [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features) and [Polygenic Risk Score Software](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) 4. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected. Details on file name constraints can be found in the [ICA documentation](https://help.ica.illumina.com/project/p-data#file-folder-naming). Custom configuration options vary by Type of Analysis selected. More details are available in section [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features). 5. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [DRAGEN Array Applications](https://help.dragenarray.illumina.com/overview/our-features). 6. Select Next 7. Select **Import Sample Sheet** to upload a sample sheet\ Download a template sample sheet by selecting the Download Template link. The samples listed in the sample sheet do not need to be scanned or have data uploaded yet. More details are available in the [Sample Sheet section](https://help.dragenarray.illumina.com/product-guides/input-files#toc150786140). 8. Specify the **Maximum failed scanned samples** threshold\ This setting determines how many samples can fail during scanning before the planned analysis is aborted.

A sample is considered failed during scanning when the stripe registration is less than 0.75. Failed stripes are flagged as potentially misregistered and appear colored red in the Scan Progress Indicator window of the iScan Control Software.

9. Save the planned analysis ## How Planned Analyses Execute Once a planned analysis is saved, the system monitors for sample data availability: * **Auto-launch**: When all sample data listed in the sample sheet becomes available, the analysis will automatically launch. * **Abort on threshold exceeded**: If the number of samples that failed during scanning exceeds the **Maximum failed scanned samples** threshold, the planned analysis will be aborted. * **Launch with passing samples**: If some samples fail but the count is within the threshold, the analysis will launch automatically with only the passing samples. ## Viewing Planned Analyses The Planned Analyses tab displays a list of all planned analyses. Click on any analysis to view its details, including the configuration settings and sample information. ### Overview Card The overview card at the top of the Planned Analyses tab provides a quick summary of your analyses by status: * **Planned**: Total number of analyses with planned status (not yet launched) * **Planned & Launched**: Number of analyses that have been planned and subsequently launched * **Aborted Before Launch**: Number of analyses that were aborted before launching You can filter the analyses table by selecting a date range in the overview card. This will display only the planned analyses that have been modified within the selected date range. Clicking on any of the status count numbers will automatically filter the analyses table to show only analyses with that status. ## Viewing Single Analysis Details Click on any planned analysis in the table to view its complete details. The details page is organized into three sections: ### Configuration The Configuration section displays the core settings for your planned analysis: * **Analysis Name**: The name you assigned to the planned analysis * **Project**: The project where the analysis output files will be stored * **Analysis Type**: The type of analysis selected * **Detailed Configuration Settings**: The specific configuration parameters for your selected analysis type ### Samples The Samples section contains information about the samples included in your planned analysis: * **Samplesheet**: Download the samplesheet that was imported for this analysis * **Maximum Failed Samples Allowed**: The threshold you set for how many samples can fail during scanning before the planned analysis is aborted ### Status The Status section shows the current state and progress of your planned analysis: * **Status**: The current status of the analysis (e.g., planned, launched, aborted) * **Array Status Summary**: A summary of the scanning status for all samples in the analysis or the launch status * **Export Sample Status**: Download a detailed file containing the status of every individual sample in the analysis. This file provides information about each sample's processing status and any errors encountered. #### Sample Statuses and Reasons When you export the sample status, each sample will have a **Status** and a **Reason**. There may be multiple possible reasons for each status, depending on the sample's situation. The reason provides additional explanation for why the sample received its status. | Status | Reason | Explanation | | ------- | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- | | Ready | Sample will be included in analysis due to successful data upload. | The sample data is available and meets all requirements for inclusion in the analysis. | | Failed | Sample will be excluded from analysis due to scanning failure. | Sample failed during scanning because the stripe registration is less than 0.75 (flagged as potentially misregistered in iScan Control Software). | | Pending | Sample is pending data upload. | The sample is not ready for analysis because it has not yet been scanned or its data has not been uploaded. | ## Deleting Planned Analyses Only planned analyses with a **planned** status (analyses that have not yet launched) can be deleted from this page. Deleting a planned analysis removes it from the system and prevents it from auto-launching when sample data becomes available. To delete planned analyses: * Check boxes for individual analyses on the left-hand side, or * Use the top checkbox to select all analyses on the current page for bulk deletion. {% hint style="info" %} Deleting a planned analysis will permanently remove it and the action cannot be undone. Analyses that have already launched cannot be deleted from this page. {% endhint %} # Share Project Project sharing allows a user to share files with users outside the workgroup for collaboration or with Illumina Tech Support for troubleshooting. To share a project on BaseSpace Sequence Hub, first set the Workgroup type as 'Collaborative' during [Workgroup setup](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis/overview-1#prerequisites), and then use the following steps to obtain a link to your project. The project can then be accessed by anyone with the link. All files in the project are shared. ## Steps to Share a Project 1. Navigate to the Projects tab 2. Click the button next to the desired project 3. Select the Share button above to list 4. Select the Get Link Option to Activate a link for the project 5. Copy the link and send it to the desired recipient(s) ## Additional Notes * **Ownership**: The project owner maintains ownership and write access. If project owner deletes the data, the collaborators lose access to it. * **Collaborative workgroup requirement**: Either sending or receiving domain must be collaborative. See [Workgroup setup in BaseSpace](https://help.basespace.illumina.com/microarray/getting-started) for more information. * **Regional restrictions**: Must be in the same [AWS regional instance](https://help.basespace.illumina.com/manage-your-account/regions). Data cannot be transferred directly between instances, however you can download and share data separately. * **Enterprise domains**: Use this same [share-by-link method](https://help.basespace.illumina.com/collaborate/share-with-collaborators/share-by-link), not share-by-transfer. # Troubleshoot iScan Integration The firewall protects the iScan control computer by filtering incoming traffic to remove potential threats. The firewall is enabled by default to block all inbound connections. Keep the firewall enabled and allow outbound connections. For the instrument to connect to BaseSpace Sequence Hub, you will need to add regional platform endpoints and instrument specific endpoints to the allow list on your firewall. Regional endpoints and further detail can be found in [Security and Networking for Illumina instrument control computers](https://support-docs.illumina.com/SHARE/NetworkSecurity/Content/SHARE/FrontPages/NetworkingSecurity.htm). The following table shows the applicable endpoints for the iScan.
EndpointCategoryPurpose
ica.illumina.comRequiredSend IDAT files to ICA
o.ss2.usRequiredCertificate authorization
ocsp.digicert.comRequiredCertificate authorization
ocsp.pki.goog/gsr2RequiredCertificate authorization
ocsp.rootca1.amazontrust.comRequiredCertificate authorization
ocsp.rootg2.amazontrust.comRequiredCertificate authorization
ocsp.sca1b.amazontrust.comRequiredCertificate authorization
fonts.gstatic.comRequiredDisplay fonts
fonts.googleapis.comRecommendedDisplay fonts
cdn.walkme.comRecommendedTelemetry
cdn3.userzoom.comRecommendedTelemetry
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## IDAT Fail Status iScan will mark certain samples with a FAIL status if the registration quality is too poor for that particular section. Selected samples that are marked with FAIL status will be excluded from analysis and there would be no results for that sample, even though IDATs are generated. These samples will not be available for selection through beadChip table or samplesheet upload when launching analysis. The registration quality can be found in the `metrics.txt` file. More information on that file can be found in the [iScan documentation](https://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/ScanMetrics_fIS.htm). # DRAGEN Array Local Analysis ## DRAGEN Array Local Overview DRAGEN Array provides accurate, comprehensive, and efficient analysis of Infinium microarray data. The local command-line interface makes it easy for power users to have granular control and flexibility to support large scale microarray genomic studies. ## Getting Started DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. If new to using command-line interface, please review the [Command-line interface Basics](#toc150786129). ### Computing Requirements Before downloading and installing the software, ensure the following specifications are met for best performance: | Category | Recommendation | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | CPU | 8 cores | | Memory | 16 GB available or more | | Hard Drive | 30 GB or more of free disk space | | Operating System |

One of the following:

  • Windows 10 or later – win10-x64
  • CentOS 7 or later, Ubuntu 20.04 or later – linux-x64
| **Note on Cybersecurity:** DRAGEN Array is not required to run as adminstrative user. We recommend you do not run with elevated permissions. ### Quota Specifications The star-allele call command in DRAGEN Array Local requires quota to run. The quota is charged per sample analyzed and can be purchased on the [Illumina Product Page](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html). Quota is used for all samples analyzed including re-analysis or low-quality samples. Quota is checked before and after analysis but not after updating the usage. Users will need to re-run the command to re-check the current usage after a run. The credential provided in the activation email after purchasing should be used as an input to the star-allele call command through the "--license-server-url" option. During runtime, the [logs](/dragen-array-v1.3/product-guides/output-files#toc150786153) will record the remaining quota at the beginning and the end of the analysis. Internet is required to do a software license check and ensure paid quota is available for all samples in the analysis batch. For the software license check, the following endpoints are used: * In v1.0 and v1.1: `license.edicogenome.com` * In v1.2+: `license.dragen.illumina.com` **NOTES:** * Do not use `license.dragen.illumina.com` license server urls when running DRAGEN Array v1.0 and v1.1 as that domain only works with v1.2+ versions. This is described in the [1.0.0](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/reference/release-notes/dragen-array-v1.1.0-release-notes#known-issues) and [1.1.0](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/reference/release-notes/dragen-array-v1.1.0-release-notes#known-issues) known issues. * In v1.1+, during analysis, precomputed quota is no longer checked. This can result in a scenario where an analysis run can be over-quota, but will not fail until the end of the run. An example: if there is only quota for 6 samples, but the analysis run contains 8 samples, the analysis will proceed as normal until the end when usage is updated the software will produce the following error: `Error updating usage. HTTP error status code: 409` and will not write the results to disk. ## Installation Please follow the steps below to install the software on your compute infrastructure: 1. Click on the latest DRAGEN Array version installation package for the platform of your choice. Installers for Windows and Linux are available on the [Illumina Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html).\ \ Once download is completed, move the DRAGEN Array installation package to the desired folder. Administrative permissions may be required for system folders, for example `/usr/local/bin for Linux`, and `C:\Program Files` for Windows.\ \ **Note**: Throughout the remainder of the document, Linux will be assumed in the examples. 2. Unzip and extract the package. The executable can be found in the dragena subfolder of the software download after extraction. 3. To check that the DRAGEN Array installation was successful, follow these steps: * Open a command prompt (Windows) or terminal (Linux). * \[Optional] Add `/path/to/dragena/`, e.g. `/usr/local/bin/dragena-linux-x64-DAv1.1.0/dragena/`, to your PATH – to access the executable anywhere in the folder structure * Execute the following command: `/path/to/dragena/dragena version`, or if the environmental variable PATH is set: dragena version The version of the software will be displayed in the terminal window when the installation was successful. ## Run DRAGEN Array Local For genotyping or cytogenetic analysis, there is no sample minimum required to run analysis. For CNV PGx analysis, a minimum of 24 samples is required to run analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. With a standard hardware specification in section [Computing Requirements](#computing_requirements), up to 500 GDA-ePGx samples can be processed per analysis batch. To optimize performance of the targeted PGx CNV caller and minimize batch effect, it is recommended to: * Group samples in the same assay batch (e.g. whole genome amplication and targeted gene application assay batch) into the same analysis batch. * Avoid combining sample batches processed on different reagent lots. * Analyze batches of 96 samples or more. * Samples processed in a two-week period from multiple library preparation batches can be grouped together to meet size requirement of an analysis batch. In such cases, it is recommended to use the same lot of reagents and instruments used in the workflow. * Use the CN Model and PGx Database File provided as part of the standard product files ## Quick Start Review section [DRAGEN Array Applications](/dragen-array-v1.3/overview/our-features) for information on input files to use, sample minimums per analysis type and other best practices. Command examples show analysis for a Linux system using folders instead of sample sheets. For Windows users, make sure to substitute the file paths in the commands following windows conventions, e.g., using backslash (\\) instead of forward-slash (/). A sample sheet can be used to select specific samples out of a folder. **Note**: DRAGEN Array will overwrite older files if using the same `--output-folder` from a previous analysis. If this is not desired, use different `--output-folder` for re-analyses. ### PGx Use the following instructions to start the full PGx analysis, covering genotyping, PGx CNV and PGx star allele calling. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the pgx copy-number call command to call PGx CNVs from the GTC files and produce CNV VCF files. It is recommended to use the same output folder used for SNV VCF since the star-allele call command accepts one VCF folder with SNV and PGx CNV VCFs.\ `dragena pgx copy-number call --cn-model /user/productfiles/cnv_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` **Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. 5. Use the pgx star-allele call command to generate star allele calls using the CNV and SNV VCF files generated by the gtc-to-vcf and copy-number call commands.\ `dragena pgx star-allele call --vcf-folder /user/vcf --database /user/productfiles/DAv1.3.0-rc3.zip --output-folder /user/star-alleles --license-server-url https://username:password@license.dragen.illumina.com` **Note**: For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 6. Use the pgx star-allele annotate command to summarize the star alleles and add metabolizer statuses to the star alleles generated by the star-allele call command. Guidelines (CPIC or DPWG) can be specified.\ `dragena pgx star-allele annotate --star-alleles star_alleles.csv --guidelines CPIC --output-folder /user/metabolizer-statuses` 7. \[Optional] Use the pgx copy-number train command to retrain the copy number model.\ `dragena pgx copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --platform LCG --output-folder /user/productfiles/cnmodelnew` ### Cytogenetics Use the following instructions to start the full cytogenetics analysis, covering genotyping, CNV and LOH calling, and annotation. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the cyto call command to determine copy number variants and loss of heterozygosity given genotypes.\ `dragena cyto call --cn-model /user/productfiles/cyto_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the cyto annotate command to generate JSON annotation files with gene annotations, cytogenetic bands, various QC fields, and the variant information from the VCFs.\ `dragena cyto annotate --annotation-db /user/productfiles/CytoAnnotateData_DAv1.2.0.zip --vcf-folder user/vcf --output-folder /user/cyto-annotations` ## Command Index Use the following syntax when using the command-line interface: `dragena [module] [sub-module (not needed for cyto)] [command] [required parameters] [optional parameters]` | Module | Description | | -------- | --------------------------------------------------------------------------------- | | genotype | Call genotypes, single nucleotide variants, and various related file conversions. | | pgx | Pharmacogenomics CNV, star allele calling and metabolizer status annotation. | | cyto | Cytogenetics CNV/LOH/mosaic calling and annotation. | | help | Display more information on a specific command. | | version | Display version information. | ### **help** Displays the first-layer help information. ### **version** Displays current DRAGEN Array Local version. ### **genotype** The root command for genotype calling. | Command | Description | | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- | | genotype call | Determines genotype calls (GTC) from IDAT files. | | genotype gtc-to-bedgraph | Converts GTC to BedGraphs, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files. | | genotype gtc-to-vcf | Converts GTC to VCF. | | genotype help | Displays the help information for the genotype command. | | genotype version | Displays version information for the genotype command. | ### **genotype call** Determines genotype calls (GTC) from IDAT files. | Option | Description | | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --cluster-file | \[Required] Specifies the path to the EGT cluster file to use. | | --idat-folder |

Specifies the path to the directory where all intensity data IDATs (for the samples to be processed) are located. If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet.

This path also includes the contents of all subdirectories.

| | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --gencall-cutoff | GenCall score cutoff to label a NoCall. Default is 0.15. | | --help | Displays help information for the genotype call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --num-threads | Number of parallel threads to run. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --normalize-sample-name | Sample Name from IDATs will be ignored and instead Sample Name will be normalized to the SentrixBarcode\_Position in downstream outputs. Default is false | | --version | Displays version information. | **Note**: Either --idat-folder, --sample-sheet, or both are required inputs. ### **genotype gtc-to-bedgraph** Converts GTC to BedGraph files, producing BedGraph formatted visualization files from the Log R Ratio and B-allele frequency data contained in the GTC intermediate files. | Option | Description | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --debug | Include stack traces in logs. Default is false. | | --help | Displays help information for the genotype gtc-to-bedgraph command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | **Note**: Either --gtc-folder, --sample-sheet, or both are required inputs. ### **genotype gtc-to-vcf** Converts GTC (v5) to [SNV VCF Files](/dragen-array-v1.3/product-guides/output-files#snv_vcf_file). The command is only applicable for [Genotype Call Files](/dragen-array-v1.3/product-guides/output-files#genotype_call_file) produced by DRAGEN Array. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --csv-manifest | \[Required] Specifies the path to the CSV manifest with SourceSeq column. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --auxiliary-loci | Specifies the path to the VCF file with auxiliary definitions of loci, such as for multi-nucleotide variants. | | --debug | Include stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --filter-loci | Generates a text file containing a list of probe names to be filtered. | | --unsquash-duplicates | Generates unique VCF records for duplicate assays. Default is false. | | --help | Displays help information for the genotype gtc-to-vcf command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | #### Squashing duplicates In the manifest, there can be cases where the same variant is probed by multiple different assays. These assays may be the same design or alternate designs for the same locus. In the default mode of operation, these duplicates will be "squashed" into a single record in the VCF to reflect a true variant rather than probe genotype. The method used to incorporate information across multiple assays is defined further in the [VCF description](/dragen-array-v1.3/product-guides/output-files#snv_vcf_file). When the `--unsquash-duplicates` option is provided, this "squashing" behavior is disabled, and each duplicate assay will be reported in a separate entry in the VCF file. This option is helpful when you are interested in investigating or validating the performance of individual assays, rather than trying to generate genotypes for specific variants. Note that if a locus has more than two alleles and is also queried with duplicated designs, the duplicates will not be unsquashed (i.e., in the case of multi-allelic variants). **DO NOT** use `--unsquash-duplicates` option if doing star allele calling downstream as that command expects squashed variants. #### Genome cache By default, the entire reference genome will be read into memory. Generally, this will be more efficient than reading data from the indexed reference on disk at the expense of greater memory utilization. For situations in which the genome caching is not desirable (low memory availability or a small input manifest), it is possible to disable this default behavior with the `--disable-genome-cache` option. #### Auxiliary loci Certain classes of variant types (such as multi-nucleotide variants) are not currently supported in the upstream analysis software that produces GTC files. However, it is possible to query this type of variant by creating a SNP design that differentiates the specific multi-nucleotide alleles of interest. For example, if the true source sequence is ATGC\[AT/CG]GTAA This assay could be designed as a SNP assay with the following source sequence ATGC\[A/C]NNNN `gtc-to-vcf` provides an option (`--auxiliary-loci`) to supply a list of auxiliary records (in VCF format) to restore the true alleles for these cases in the output VCF. There are several restrictions around this function * The auxiliary definition must NOT be a multi-allelic variant. * The auxiliary definition must be a multi-nucleotide variant. * There must NOT be multiple array assays (e.g., duplicates) for the locus. **Notes:** * Either --gtc-folder, --sample-sheet, or both are required inputs. * The genome fasta files for human genomes are provided by Illumina on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ### **genotype help** Displays the help information for a genotype command. ### **genotype version** Displays current DRAGEN Array Local version. ### **pgx** The root command for pgx module | Command | Description | | ----------- | ---------------------------------------------- | | copy-number | Call and train copy number variants. | | star-allele | Star Allele Caller for Illumina Microarrays | | help | Display more information on a specific command | | version | Display version information. | ### **pgx copy-number** The root command for actions that act on pgx copy number variants. | Command | Description | | ----------------------- | --------------------------------------------------------------------- | | pgx copy-number call | Determines copy number variants given genotypes (GTC to CNV VCF). | | pgx copy-number help | Displays help information for a copy-number command. | | pgx copy-number train | Trains copy number model for a set of samples (GTC to CN Model File). | | pgx copy-number version | Displays version information for copy-number. | ### **pgx copy-number call** The command used to call copy number variants. A batch of 24 samples or more are required for analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. | Option | Description | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --cn-model | \[Required] Specifies the path to the copy number model parameters file (.dat). | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the copy-number call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. | | --version | Displays version information. | ### **pgx copy-number train** Trains pgx copy number (CN) model for a set of samples. Generate a new pgx CN model if using a customized cluster file (.egt) optimized for the specific data set. * Execute the train command using the data sets that were used to optimize the cluster file. * To use a pgx CN model generated by the train command, the mask file for the manifest must be saved in the same directory as the manifest. * A minimum of 96 samples is required to use the copy-number train command. For optimal performance, at least 150 is recommended. * For best performance, validate the pgx CN model using truth data before using in pgx CN calling. See [Optimizing cluster files and copy number models](#optimizing_cluster_files) for further details. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. Assumes mask file (.msk) is in the same directory. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --platform | \[Required] Specifies which microarray platform generated the data. Set this to 'LCG' for GDA-ePGx, 'EX' for GSAv4-ePGx or GCRA-ePGx | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --debug | Includes stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --help | Displays help information for the copy-number train command. | | --json-log | Outputs logs in JSON format. Default is false. | | --version | Displays version information. | | --output-folder | The location to output the CN model. By default, the output folder is the current working directory. | ### **pgx copy-number help** Displays help information for the copy-number command. ### **pgx copy-number version** Displays version information for pgx copy-number command. ### **pgx star-allele** The root command PGx star allele calling. | Command | Description | | ------------------------ | ---------------------------------------------------- | | pgx star-allele call | Determines PGx star allele and variant genotypes. | | pgx star-allele annotate | Annotate PGx gene functions and product JSON report. | | pgx star-allele help | Displays help information for a star allele command. | | pgx star-allele version | Displays version information for star allele. | ### **pgx star-allele call** Calls PGx star allele diplotypes. The SNV VCF files should be generated using the DRAGEN Array gtc-to-vcf command with unsquash-duplicates off (default) and without filter loci. | Option | Description | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | --database | \[Required] The PGx database file (.zip). | | --license-server-url | \[Required] The license server url with credentials. | | --vcf-folder | \[Required] The directory containing \*.snv.vcf.gz and \*.cnv.vcf.gz files. | | --query-license-quota | During beginning and end of analysis, the license server will be queried for the quotas on the valid license(s) and display the result. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Directory path to output files. Default is the current working directory. | | --version | Displays version information. | ### **pgx star-allele annotate** Annotates and summarizes the star-alleles, specifically for metabolizer statuses and outputs in a consolidated JSON report. Metabolizer status is determined through direct lookup into public PGx guidelines CPIC or DPWG as specified by the user. | Option | Description | | --------------- | -------------------------------------------------------------------------------------------- | | --star-alleles | \[Required] Path to star alleles file (.csv) generated by the call subcommand. | | --guidelines | PGx guidelines to use for annotation. Valid values are ‘CPIC’ and ‘DPWG’. Default is ‘CPIC’. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele annotate command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | Directory path to output files. Default is the current working directory. | | --version | Displays version | ### **pgx star-allele help** Displays help information for a star-allele command. ### **pgx star-allele version** Displays version information for star-allele. ### **cyto** The root command for Cytogenetics analysis and annotation. | Command | Description | | ------------- | --------------------------------------------------------------------------- | | cyto call | Determines copy number variants and loss of heterozygosity given genotypes. | | cyto annotate | Annotates samples and generates cytogenetics json reports. | | cyto help | Display more information on a specific command. | | cyto version | Displays version information. | ### **cyto call** Determines copy number variants (CNV) and loss/absence of heterozygosity (LOH/AOH) given genotypes. | Option | Description | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | --cn-model | \[Required] Path to cyto model parameters file (.dat). | | --gtc-folder | Folder containing genotype files (.gtc). If using the --sample-sheet option in conjunction, this value will be used to override the RootFolder in the samplesheet. | | --sample-sheet | Sample sheet that allows for filtering and providing sample metadata. | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-cnv-probes | CNV size limit (probes). Only CNV events with equal or more probes than the min-cnv-probes will be reported in the VCF file. Default is 10. | | --min-cnv-size | CNV size limit (kb). Only CNV events with effective size equal or larger than the min-cnv-size will be reported in the VCF file. Default is 20. | | --min-loh-probes | LOH size limit (probes). Only LOH events with equal or more probes than the min-loh-probes will be reported in the VCF file. Default is 500. | | --min-loh-size | LOH size limit (kb). Only LOH events with effective size equal or larger than the min-loh-size will be reported in the VCF file. Default is 3000. | | --max-mosaic-fraction | The maximum allowable mosaic fraction. Mosaic variants at or above this are promoted. Promoted variants are marked with a HIGHFRACTION INFO tag and retain their mosaic fraction value. Default is 1.0 | | --smoothing | Smoothing window size, specifying the number of probes on each side of the center probe used for smoothing LRR values. Default is 5. | **Notes:** * Greater than 10 events (DEL/DUP/AOH) per chromosome is an indication of need for visual inspection. * If mosaic fraction cannot be estimated due to insufficient informative probes, it will be set to NaN. * Mosaic events that surpass the `--max-mosaic-fraction` limit have the MOSAIC tag in the INFO field of the VCF replaced with an HIGHFRACTION tag. * Mosaic events with a fraction below 20% may be missed, and events under 5% mosaicism will not be called. * LogRDev > 0.2 is indicative of a low-quality sample. * For samples with LogRDev > 0.3, profiles are typically very noisy. In such cases, only whole-chromosome-level events are detected and reported to prevent excessive false positives. ### **cyto annotate** Annotates samples and generates cytogenetic json reports. | Option | Description | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --annotation-db | \[Required] Database for variant annotations. | | --vcf-folder | \[Required] The directory containing the \*.cnv.vcf.gz files. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-del-probes | Deletion CNV size limit (probes). Only deletions with equal or more probes than the min-del-probes will be reported in the json file. Default is 10. | | --min-del-size | Deletion CNV size limit (kb). Only deletions with size equal or larger than the min-del-size will be reported in the json file. Default is 0. | | --min-dup-probes | Duplication CNV size limit (probes). Only duplications with equal or more probes than the min-dup-probes will be reported in the json file. Default is 10. | | --min-dup-size | Duplication CNV size limit (kb). Only duplications with size equal or larger than the min-dup-size will be reported in the json file. Default is 0. | | --min-loh-probes | LOH size limit (probes). Only LOH events with equal or more probes than the min-loh-probes will be reported in the json file. Default is 500. | | --min-loh-size | LOH size limit (kb). Only LOH events with size equal or larger than the min-loh-size will be reported in the json file. Default is 3000. | | --min-qual | Min CNV qual and LOH qual scores. Default is 20. | **Notes:** * The metadata "cyto.cnv.dat" file that is generated during cyto call in the vcf-folder needs to be kept in the vcf-folder for cyto annotate. * The vcfs files need to be zipped and indexed for cyto annotate, which means "--no-bgzip" flag cannot be turned on for the cyto vcf file generation if those vcf files are going to be used for cyto annotate command. * The "cyto annotate" step needs at least 5GB free space on the hard drive. ### **cyto help** Display more information on a specific command. ### **cyto version** Displays version information. ## Troubleshooting and Additional Support ### Tips for using the Command-line interface DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. When using command-line consider the following tips: * Spaces cannot be part of a file name in a command. If the file name has spaces, use quotes around the file name * To correct a typing error in a previously entered command, use the up arrow to repeat the previous command, then correct the error before re-entering it. * Double check the command. Misspelling, extra, or missing dashes, etc. will cause the command to be unrecognizable by the software. * When entering paths or long names, copy and paste the values to help avoid errors. * If using Windows, use a File Explorer window to navigate to the product file or folder that is needed by the DRAGEN Array Local command. While holding down the shift button on the keyboard, right click the file and select the 'Copy as Path' option. Then paste the copied path into the command prompt to use the file or folder. * To cancel a command while it is running, press Control + C on the keyboard. ### Optimizing cluster files and copy number models A [Cluster File](/dragen-array-v1.3/product-guides/input-files#toc150786136) (.egt) contains the cluster positions of every probe used for genotyping analysis. Illumina provides a standard cluster file for all commercial Infinium BeadChips. It may be desirable to create a custom cluster file if the one provided does not fit the data well or if a semi-custom or custom BeadChip, that do not come with a cluster file, are used. [GenomeStudio 2.0](https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) is the software used to create custom cluster files. To facilitate the review and optimization of PGx variant GenTrain cluster positions, a GenomeStudio auxiliary file is provided for each PGx Array product through the [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) and array product files page, e.g. [Infinium Global Diversity Array with Enhanced PGx Product Files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html). The auxiliary file is a tab-delimited text file that can be imported into GenomeStudio through Column Import. The file contains the Infinium Assay to PGx star allele mapping, covering the variants involved in DRAGEN Array PGx star allele calling. When updating the cluster file for pharmacogenomic applications, understand the specifications for the copy number model file before beginning. Before creating a custom cluster file, review the [Infinium Genotyping Data Analysis Technical Note](https://www.illumina.com/Documents/products/technotes/technote_infinium_genotyping_data_analysis.pdf), the [Infinium Arrays Support Webinar Video](https://youtu.be/4JTrbMUbVN0?si=ZgRDLwN6umGBhv2G), and [Custom cluster file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). A [PGx Copy Number (CN) Model File](/dragen-array-v1.3/product-guides/input-files#cn_model_file) (.dat) contains the data needed to make accurate copy number calls for pharmacogenomics. This file is used in the creation CNV VCFs which are inputs to the star allele calling command. Illumina provides a standard CN model file for all commercial PGx Infinium BeadChips. If it is determined the cluster file needs to be customized, the CN Model File should also be updated using the copy-number train command available with DRAGEN Array Local only. i.e., 1. Use GenomeStudio 2.0 to generate a new cluster file. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/new_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/new_gtcs` 3. Use the copy-number train command to retrain the copy number model. **Note: The --platform option can be found in the `Assay Format` heading value from the CSV manifest.**\ `dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/new_gtcs --platform LCG --output-folder /user/productfiles/new_cnmodel` 4. Use the `new_cnmodel` for subsequent `copy-number call` commands. Note the difference in the cluster file requirement based upon the version of DRAGEN Array used: * **Version 1.1+**: If using a CN model with a different cluster file, the software will provide a warning but will proceed with copy number calling. As a result, a user can choose to keep using the commercial CN model from Illumina in combination with custom updated EGT file in the PGx analysis. * **Version 1.0**: The same cluster file used for copy number training must be used to generate GTC files for copy number calling. Otherwise, the software will produce an error and exit. For reference, see the [Command Index](#command_index_1) for details of `copy-number train` command. To retrain the CN model file, 96 samples must be used at minimum with 90 of those samples passing QC defined as Log R Dev less than or equal to 0.2. It is recommended to train with at least 150 samples. A greater number of samples can be advantageous, but diminishing returns and longer computation times are seen after 3,000 samples. It is recommended to manually QC the training samples and remove samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 so only the highest quality samples are used in the training. The same samples used to create the new cluster file should be used to retrain the CN Model. To minimize batch effect in the training sample set, the samples should be analyzed in as few batches as possible and come from the same reagent lots. The copy-number train algorithm is designed with the assumption that the copy number distribution resembles the standard population distributions. This ensures the updated CN model file is representative of the normal populations in which it will be used to calculate copy number for key pharmacogenomic targets. ### Pharmacogenomic analysis for semi-custom arrays Semi-custom arrays add additional content or other pre-designed [Infinium booster content](https://www.illumina.com/science/consortia/human-consortia.html) to enhance the commercial array content. This additional content can be analyzed for [genotyping applications](/dragen-array-v1.3/overview/our-features#toc150786108) to obtain information on SNV and indel calls. For [pharmacogenomic applications](/dragen-array-v1.3/overview/our-features#toc150786109), PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. When designing a semi-custom array using a commercial Infinium PGx array backbone, such as the Global Diversity Array with enhanced PGx, it is important to retain all backbone content in the design as removing content could decrease the quality of result. Pharmacogenomic analysis for semi-custom arrays should be run using [DRAGEN Array Local](/dragen-array-v1.3/product-guides/dragen-array-local-analysis). Because the PGx CNV calling and PGx star allele calling algorithms are only compatible with commercial product files (see [Applications](/dragen-array-v1.3/overview/our-features)), to fully analyze semi-custom PGx beadchips some steps of the pipeline can be run twice; once with the semi-custom product files (to get complete semi-custom SNV VCF files), and once with the commercial product files (to get the PGx CNV VCF files, PGx Star Allele output, and metabolizer report). The semi-custom product files can be used via the Command-line interface in `genotype call`, `genotype gtc-to-vcf`, and used in GenomeStudio, i.e., 1. Use GenomeStudio 2.0 to prepare a custom cluster file for the semi-custom array, following guidance outlined in [Custom cluster\ file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). 2. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 3. Use the genotype call command to call all semi-custom genotypes and generate custom content GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --cluster-file /user/productfiles/semi_custom_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/semi_custom_gtcs` 4. Use the genotype gtc-to-vcf command to create custom content SNV VCF files from the custom content GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --csv-manifest /user/productfiles/semi_custom_manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/semi_custom_gtcs --output-folder /user/semi_custom_vcfs` 5. Perform [Quick Start](#_toc150786126) steps 1-6 using the **commercial** Infinium PGx array product files to obtain PGx CNV VCFs, star allele calls, and metabolizer status annotations. Keep the GTC files and SNV VCF files generated using the semi-custom product files in clearly labelled folders to distinguish them from the GTC and SNV VCF files generated using the commercial product files. Note that the GTC and SNV VCFs generated using the commercial product files will not contain genotypes for the semi-custom/add-on content. The GTC and SNV VCFs generated using the semi-custom product files cannot be used for downstream PGx analysis commands. # Input Files The following section describes the input files required by DRAGEN Array.\ Product files (anything other than the IDATs) can be found on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ## IDAT Files For each sample a pair of raw intensity files (.idat) are generated from the iScan System or NextSeq550 (for select arrays). They provide intensities in the red and green channels for each probe on the Infinium array. More information on which arrays can be used with NextSeq550, can be found on the [Illumina Knowledge page on NextSeq550](https://knowledge.illumina.com/microarray/nextseq-500-550/microarray-nextseq-500-550-faq-list/000003871). An IDAT file is identified by the BeadChip Barcode (12-digit unique Sentrix ID, i.e. 123456789101), BeadChip Position (row and column of the sample, i.e. R01C01), and Grn (Green) or Red for the specific channel. ## Manifest Files The CSV and BPM manifest files can be found on the Illumina Support Site for all commercial Infinium BeadChips or on [MyIllumina](http://my.illumina.com/) for custom and semi-custom designs. DRAGEN Array only supports manifest files from the Illumina Support site. For instructions on obtaining manifest files from MyIllumina, see Illumina Knowledge article, [How to access custom array product files (manifest and product definition files) in MyIllumina](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001531). The CSV manifest file (.csv) provides complementary data to the BPM manifest file in a human readable format. It is a required input to the genotype gtc-to-vcf command to enable VCF generation for insertion/deletion variants. `gtc-to-vcf` depends on the presence of accurate mapping information within the manifest, and may produce inaccurate results if the mapping information is incorrect. Mapping information follows the implicit dbSNP standard, where * Positions are reported with 1-based indexing. * Positions in the PAR are reported with mapping position to the X chromosome. * For an insertion relative to the reference, the position of the base immediately 5' to the insertion (on the plus strand) is given. * For a deletion relative to the reference, the position of the most 5' deleted based (on the plus strand) is given. ## Cluster File The cluster file (.egt) is a standard product file provided by Illumina for commercial genotyping products and it is a required input for the genotype call command in DRAGEN Array. Custom cluster files may be required for optimal genotyping performance. See section [Optimizing cluster files and copy number models](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## PGx CN Model File The PGx CN (Copy Number) model file (.dat) is a required input to the pgx copy-number call command to enable accurate copy number calling for pharmacogenomics. Illumina provides a standard CN model file for each PGx array product. See section [Optimizing cluster files and copy number models](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## Cytogenetics Model File The cytogenetics CN (Copy Number) model file (.dat) is a required input to the cyto call command to enable accurate Cytogenetics analysis. Illumina provides a standard CN model file for each supported array product. For custom or other products, please contact Tech Support to request a CN model file and include the product BPM manifest. **Note:** The CN model file needs to be updated upon manifest revisions since probes can be added or removed during manifest revisions. A mismatch between the CN model file and the manifest will cause an error during `pgx copy-number call` and `cyto call`. ## Mask File The mask file (.msk) is a required input to the pgx copy-number train command to enable accurate pgx copy number training for pharmacogenomics. It does not need to be provided as an explicit input to the command line interface but should reside in the same folder as the BPM manifest. It should have the same base name as the manifest for the product. Illumina provides a mask file for each PGx array product and these can be found on the [product files support page.](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html) ## PGx Database File The PGx database file (.zip) contains the variant mapping information from Infinium PGx arrays to PGx variants. Each line in this file represents a single probe ID mapping to a variant's HGVS (Human Genome Variation Society) tag. This creates a map of many probes to one variant. DRAGEN Array cross references this map with SNV VCF IDs during runtime to do star allele calling. It works across all supported PGx products, even though the probes and variant coverage differ across them. ## Cytogenetics Database File The cytogenetics database file (.zip) contains information from Ensembl and RefSeq data sources used in the generation of Cytogenetics Annotation JSON File. This file can be used across products (beadchip/manifest types and versions). It is only necessary for input to local analysis (i.e., `cyto annotate`) as it is already stored in the cloud for cloud analysis. It may be updated in the future to accomodate changes in the underlying Ensembl and RefSeq datasources. ## Genome FASTA Files The genome FASTA file (.fa) is a text file with the reference genome sequences.The FASTA index file (.fai) contains metadata about chromosomal orchestration within the FASTA file for a particular species. DRAGEN Array PGx calling supports human genome build 37 and 38. The genome FASTA file and FASTA index file are both provided by Illumina for human species and should be stored together in the same input folder.\ For custom reference genomes, the contig identifiers in the provided genome FASTA file must match exactly the chromosome identifiers specified in the provided manifest. For a standard human product manifest, this means that the contig headers should read ">1" rather than ">chr1". Note: The Genome FASTA file is only required for the dragen-array-local-analysis workflow. If you're using dragen-array-cloud-analysis, you do not need to provide this file. ## Sample Sheet The sample sheet is a CSV formatted input file that utilizes a couple required fields for sample lookup (`SentrixBarcode_A, SentrixPosition_A` for local, `beadChipName, sampleSectionName` for cloud) to enable adding optional metadata and analyzing a filtered list of samples within a folder. It is intended to be flexible and the local version should be backwards compatible with most GenomeStudio samplesheets. The root folder which DRAGEN Array will search the files for can be set by either providing it via the `--idat-folder` or `--gtc-folder` options (where applicable). Or by setting the `RootFolder` field in the `[Header]` section. This `RootFolder` should be the full absolute path to the sample files. e.g., ``` [Header] RootFolder,/test/samples [Data] .... ``` **Note:** In the case of conflict between `RootFolder` and the CLI options (`--idat-folder` or `--gtc-folder`), the CLI options take precedence. The following are examples of all valid samplesheets: * Most basic (no sections, one sample) ``` SentrixBarcode_A,SentrixPosition_A 204753010023,R02C01 ``` * Medium complexity (no sections, multiple samples, optional data) ``` SentrixBarcode_A,SentrixPosition_A,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010024,R01C01,NA1233,Group2,M ``` * High complexity (sections, multiple samples, optional data) ``` [Header] RootFolder,/tests/samples Date,1/1/2025 [Data] SentrixBarcode_A,SentrixPosition_A,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010024,R01C01,NA1233,Group2,M ``` **Notes:** * The column names are case insensitive. For example, the columns `Sample_Name` and `sample_name`, would be considered the same and the software would produce an error like this: `Duplicate column sample_name found. Column names are case-insensitive. Please remove or rename the column from the samplesheet and re-process.` * Because user-provided fields get output in the [Genotype Summary File](/dragen-array-v1.3/product-guides/output-files#genotype-summary-files), the column names cannot conflict with those fields. For example, if the user provides a column named `Sex Estimate` in their samplesheet. DRAGEN Array will produce the following error: `Sex Estimate is a reserved keyword. Please remove or rename the column from the samplesheet and re-process.` * The optional fields (i.e. not `SentrixBarcode_A` and `SentrixBarcode_B`) will be output as-is in the [genotype summary files](/dragen-array-v1.3/product-guides/output-files#genotype-summary-files) for the `genotype call` command. * The `[Manifests]` section (used by GenomeStudio to delineate manifests in multi-manifest analyses) is currently ignored in DRAGEN Array. * There is a known issue regarding empty columns in the [v1.3 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes#known-issues). For cloud analyses (i.e., for use in sample selection in [running cloud analyses](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis#running-analysis)), the samplesheet does not currently support sections such as `[Header]` and `[Data]` and instead of using `SentrixBarcode_A` and `SentrixPosition_A` columns as the sample's keys, it uses `beadChipName` and `sampleSectionName`. i.e., a valid cloud samplesheet could look like this: ``` beadChipName,sampleSectionName 204753010023,R01C01 204753010023,R02C01 204753010024,R01C01 204753010024,R02C01 ``` There is also a template available on the sample selection interface on Basespace. ### Methylation QC sample sheet For DRAGEN Array Methylation QC on cloud, the additional optional sample sheet fields are used in analysis. Following Sample\_Group, any number of additional columns can be added to include meta data fields such as sex, sample type, plate and well information, etc. Additional columns added after the Sample\_Group column may have user-defined column header values. The Sample\_ID field and any additional metadata added will be replicated in the Sample QC Summary output files. The Sample\_Group field will be used to populate the PCA Control Plot within the Sample QC Summary Plots file and the Principal Component Summary file. For the PCA Control Plot, each sample group will be assigned a unique color. Samples assigned to the same Sample\_Group value will be the same color in the PCA Control Plot. e.g., ``` beadChipName,sampleSectionName,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010023,R02C01,NA1232,Group2,F 204753010024,R01C01,NA1233,Group2,M 204753010024,R02C01,NA1234,Group1,M ``` ### Cytogenetics analysis + Emedgene interpretation sample sheet For Cytogenetics analysis + Emedgene interpretation on cloud, an additional column: `demographicSex` will be used to compare against to the `Sex Estimate` output from DRAGEN Array genotyping module and be displayed in Emedgene. The allowed values for this field are `M` (Male), `F` (Female), or `U` (Unknown). Example: ``` beadChipName,sampleSectionName,demographicSex 204753010023,R01C01,F 204753010023,R02C01,F 204753010024,R01C01,M 204753010024,R02C01,M ``` ## Input File Summary Table In addition to the input files, there are set of intermediate files, including GTC, SNV VCF, CNV VCF and PGx CSV, which are outputs of some DRAGEN Array Local commands and inputs to other commands. The table below summarizes the input files or intermediate file, their sources, and the associated DRAGEN Array Local commands and options. | Input File | Source | Command | Option | | --------------------- | --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------- | | IDAT | User provided from scanning instrument | genotype call | --idat-folder | | CSV Manifest | Product file from Illumina | genotype gtc-to-vcf | --csv-manifest | | BPM Manifest | Product file from Illumina |

pgx copy-number train

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

| --bpm-manifest | | Cluster File | Product file from Illumina or user created using GenomeStudio | genotype call | --cluster-file | | PGx CN Model | Product file from Illumina or user created using DRAGEN Array Local | pgx copy-number call | --cn-model | | Cytogenetics CN Model | Product file from Illumina | cyto call | --cn-model | | PGx Database | Product file from Illumina | pgx star-allele call | --database | | Cytogenetics Database | Product file from Illumina | cyto annotate | --database | | Genome FASTA | Product file from Illumina |

genotype gtc-to-vcf

pgx copy-number train

| --genome-fasta-file | | Sample Sheet | User provided |

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

| --sample-sheet | | GTC | DRAGEN Array output from genotype call |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

| --gtc-folder | | SNV and PGx CNV VCF | DRAGEN Array output from genotype gtc-to-vcf and pgx copy-number call | pgx star-allele call | --vcf-folder | | PGx CSV | DRAGEN Array output from pgx star-allele call | pgx star-allele annotate | --star-alleles | | Cytogenetics CNV VCF | DRAGEN Array output from cyto call | cyto annotate | --vcf-folder | # Output Files The following section describes the outputs produced by DRAGEN Array. ## PGx CNV VCF File DRAGEN Array produces one PGx CNV variant call file (VCF) (\*.cnv.vcf) per sample to report the CN status on the gene and sub gene level, along with the CN events for PGx targets. The PGx CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV call quality. The CNV call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<7) are flagged by the Q7 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The PGx CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The PGx CNV VCF output file includes the following content. `##fileformat=VCFv4.1` `##source=dragena 1.3.0` `##genomeBuild=38` `##reference=file:///hg38_with_alt/hg38_nochr_MT.fa` `##FORMAT=` `##FORMAT=` `##ALT=` `##FILTER=` `##FILTER=` `##INFO=` `##INFO=` `##INFO=` `##INFO=` `##OverallPloidy=1.8` `##GCCorrect=True` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 204619760001_R01C01` `1 109687842 CNV:GSTM1:chr1:109687842:109693526 N 60 PASS CNVLEN=5685;PROBE=124;END=109693526;SVTYPE=CNV CN:NR 2:0.966631132771593` `4 68537222 CNV:UGT2B17:chr4:68537222:68568499 N 60 PASS CNVLEN=31278;PROBE=383;END=68568499;SVTYPE=CNV CN:NR 0:0.376696837881692` `10 133527374 CNV:CYP2E1:chr10:133527374:133539096 N 60 PASS CNVLEN=11723;PROBE=194;END=133539096;SVTYPE=CNV CN:NR 2:0.980059731860893` `16 28615068 CNV:SULT1A1:chr16:28603587:28613544 N 57 PASS CNVLEN=8315;PROBE=164;END=28623382;SVTYPE=CNV CN:NR 2:0.980552325552963` `19 40844791 CNV:CYP2A6.intron.7:chr19:40844791:40845293 N 60 PASS CNVLEN=503;PROBE=38;END=40845293;SVTYPE=CNV CN:NR 2:0.9663775484762` `19 40850267 CNV:CYP2A6.exon.1:chr19:40850267:40850414 N 60 PASS CNVLEN=148;PROBE=21;END=40850414;SVTYPE=CNV CN:NR 2:0.9663775484762` `22 42126498 CNV:CYP2D6.exon.9:chr22:42126498:42126752 N 48 PASS CNVLEN=255;PROBE=370;END=42126752;SVTYPE=CNV CN:NR 2:0.981703411438716` `22 42129188 CNV:CYP2D6.intron.2:chr22:42129188:42129734 N 10 PASS CNVLEN=547;PROBE=333;END=42129734;SVTYPE=CNV CN:NR 2:0.965498002434641` `22 42130886 CNV:CYP2D6.p5:chr22:42130886:42131379 N 60 PASS CNVLEN=494;PROBE=172;END=42131379;SVTYPE=CNV CN:NR 2:0.970341562236357` `22_KI270879v1_alt 270316 CNV:GSTT1:chr22_KI270879v1_alt:270316:278477 N 60 PASS CNVLEN=8162;PROBE=91;END=278477;SVTYPE=CNV CN:NR 2:1.01191145130511` ## Cytogenetics VCF File DRAGEN Array produces one cytogenetics Variant Call File (VCF) (\*.cnv.vcf) per sample to report the CN and LOH status of the detected variants. The cytogenetics CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV/LOH call quality. The CNV/LOH call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<10) are flagged by the Q10 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The cytogenetics CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as cyto annotate. One example file can be found below: `##fileformat=VCFv4.1` `##source=dragena 1.3.0 Cyto` `##genomeBuild=37` `##product=GDACyto-8v1-0_A` `##reference=file://genome.fa` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##platform=cytoplatform` `##ALT=` `##ALT=` `##ALT=` `##FILTER=` `##FILTER=` `##INFO=` `##INFO=` `##INFO=` `##INFO=` `##OverallPloidy=1.9` `##GCCorrect=True` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 208588190001_R02C01`\ `1 109687842 DEL:chr1:109687842:109693526 N 60 PASS SVLEN=5685;PROBE=99;END=109693526 GT:CN:NR:LRD 1/1:1:0.8860:0.21`\ `16 28603587 DUP:chr16:28603587:28613544 N 60 PASS SVLEN=9958;PROBE=197;END=28613544 GT:CN:NR:LRD 1/1:3:1.1666:0.11`\ `22 42129188 AOH:chr22:42129188:42129734 N 37 PASS SVLEN=547;PROBE=198;END=42129734;LOHTYPE=AOH GT:CN:NR:LRD 1/1:2:1.0208:0.25` ## SNV VCF File The software produces one genotyping variant call file (\*.snv.vcf) file per sample, covering single nucleotide variants (SNV) and indels for the sample. It reports GenCall score (GS), B Allele Frequency (BAF), and Log R Ratio (LRR) per variant. The VCF file output follows [VCF4.1 format](https://samtools.github.io/hts-specs/VCFv4.1.pdf). Some additional details: * The FILTER column is hardcoded to `PASS` and is not dependent on the `GT` value. It does not reflect the underlying quality of the call. Refer to the `GS` value for quality information. * Genotypes are adjusted to reflect the sample ploidy. Calls are haploid for loci on Y, MT, and non-PAR chromosome X for males. * Multiple SNPs in the input manifest which are mapped to the same chromosomal coordinate (e.g. tri-allelic loci or duplicated sites) are collapsed into one VCF entry and a combined genotype generated. To produce the combined genotype, the set of all possible genotypes is enumerated based on the queried alleles. Genotypes which are not possible based on called alleles and assay design limitations (e.g. Infinium II designs cannot distinguish between A/T and C/G calls) are filtered. If only one consistent genotype remains after the filtering process, then the site is assigned this genotype. Otherwise, the genotype is ambiguous (more than 1) or inconsistent (less than 1) and a no-call is returned. * Certain SNV and indel calls will be skipped when reported in the VCF. Skipped data can include unmapped loci (i.e., `Chr` is `0` in the manifest), intensity-only probes used for CNV identification, and indels that do not map back to the genome. See [Warning/Error Messages and Logs](#toc150786153) for messages that may be seen with DRAGEN Array Local related to the skipped data. * The BAF and LRR are oriented with Ref as A and Alt as B relative to the reference genome, while GS is agnostic to the reference genome. Users familiar with GenomeStudio may observe BAF and LRR reported in the VCF as 1 minus the value reported in GenomeStudio depending on the Ref Alt allele orientation with the reference genome. GenomeStudio reports these values based on the information in the manifest without knowledge of the reference genome. * The SNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the [TBI Index File](#toc150786155). To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The SNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The SNV VCF output file includes the following content. The last row shows an example of variant call. `##fileformat=VCFv4.1` `##source=dragena 1.3.0` `##genomeBuild=38` `##reference=file:///genomes/38/genome.fa` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 202937470021_R06C01` `1 2290399 rs878093 G A . PASS . GT:GS:BAF:LRR 0/1:0.7923:0.50724137:0.14730307` ### Note on Multi-Allelic Variants (MAV) calling limitations DRAGEN Array can combine multiple assays with different target bases but the same genomic position to make MAV calls. However, Illumina Microarrays are inherently bi-allelic assays made up of [Infinium I or Infinium II probe designs](https://www.illumina.com/Documents/products/technotes/technote_iselect_design.pdf) which require special design considerations and have some inherent limitations. The MAV calling algorithm currently filters across all overlapping assays, retaining only genotypes whose alleles are present in the intersection of all assays. If multiple genotypes remain after filtering, the result is considered ambiguous and reported as a NoCall to avoid false positives. This ambiguity often arises when one assay is a NoCall due to presumed probe failure rather than missing signal. In such cases, its potential genotypes are not excluded, contributing to ambiguity. When DRAGEN Array outputs a NoCall because of the described behaviors, they are logged as [warnings](#warningerror-messages-and-logs) (e.g., `Failed to combine genotypes due to ambiguity...`). Overall, the current algorithm errs on the side of caution to ensure quality calls, but produces some idiosyncratic behavior and potential false NoCalls when genotypes are biologically consistent but differ due to probe designs. We hope to improve this behavior in future versions of DRAGEN Array. Some illustrative examples are below to help understand the current limitations: | Scenario | Expected MAV Call | Actual MAV Call | Explanation | | ---------------------------------------- | ----------------- | --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Inf II \[A/G] -> AA + Inf I \[T/G] -> TT | AT | NoCall | The Inf II assay cannot differentiate A versus T alleles, hence AA call for the Inf II assay is consistent with AA, AT, or TT genotypes. The Inf I assay TT call, however, is consistent with AT or TT genotypes. Combining the two assays results in a NoCall due to the AT or TT ambiguity. | | Inf I \[T/A] -> AA + Inf II \[A/G] -> NC | AA | NoCall | NoCall for Inf II probe leaves possibility of AG. Ambiguity between AG or AA leads to NoCall. | ### Note on delimiters in the "ID" field By default, when multiple probes are present for a given variant, all probe names are included in the "ID" field of the resulting VCF file. * For SNP entries, probe names are separated by commas (,). * For Indel entries, probe names are separated by semicolons (;). ### Note on REF/ALT "flipping" for INDELs Expected REF and ALTs for INDELS may not match the dbSNP annotations in rare cases. E.g., an expected "Deletion" with the REF = "ATCG" and the ALT = "A" may be "flipped" to an "Insertion" variant with REF="A" and ALT="ACTG". The corresponding genotype output will take this into account so the actual VCF is still correct. This is simply a notation issue in some of the manifest files. ### Note on PLINK compatibility It is possible to make DRAGEN Array genotype VCF files compatible for conversion to PED/MAP format with preprocessing using tabix (v1.19.1), BCFtools (v1.21) and PLINK (v1.9). The following three commands demonstrate the basic process. * `bcftools merge -l vcf_list.txt -Oz -o merged.vcf.gz` creates a single compressed VCFs from individual sample VCFs listed in the `vcf_list.txt` text file. * `tabix -p vcf merged.vcf.gz` creates a binary index for the merged file. * `plink --vcf merged.vcf.gz --recode --out merged` creates .ped and .map files with the prefix provided to `--out`. Some optional arguments may be provided to PLINK depending on the content of the VCFs to be converted and the downstream analysis. * For VCFs containing non-standard human chromosomes (e.g. haplotype chromosomes or unplaced contigs), the `--allow-extra-chr` flag can be used. * If using non-human data, refer to the [PLINK documentation](https://www.cog-genomics.org/plink/1.9/input#chr_set) for the `--chr-set` argument and supported options. * By default, PLINK will only consider the most common ALT allele for multi-allelic variants. The `--biallelic-only` argument can be provided to exclude multi-allelic variants altogether. As an alternative, using `bcftools norm -m - in.vcf.gz -Oz -o out.vcf.gz`, can be used upstream of PLINK to split multi-allelic variants into bi-allelic records to retain them for downstream processing. For more info on the options described and others, refer the [PLINK VCF conversion documentation](https://www.cog-genomics.org/plink/1.9/input#vcf). ## Genotype Call (GTC) File The genotype call algorithm produces one genotype call file (.gtc) per sample analyzed. The Genotype Call (GTC) file contains the small variant (SNV and indel) genotype for each marker specified by the product and sample quality metrics. The sample marker location is not included and must be extracted from the manifest file. Binary proprietary format can be parsed using the Illumina open-source tool [BeadArray Library File Parser](https://github.com/Illumina/BeadArrayFiles). **Note on lack of i18n:** GTCs are binary/fixed format files built designed before modern internationalization and localization tools. There is a related [known issue](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.0-release-notes#known-issues) that makes the GTCs unable to be used in downstream analyses. Refer to the same issue to see a workaround. **Note on legacy GTCs:** Other Illumina software (such as AutoConvert and Beeline) also product GTC files.\ These "legacy GTC" files will work in DRAGEN Array genotyping commands such as `genotype gtc-to-vcf` but they will not work with all other downstream analyses such as [Cytogenetics analysis](/dragen-array-v1.3/overview/our-features#dragen-array--cytogenetics-calling) and [PGx](/dragen-array-v1.3/overview/our-features#dragen-array--pgx--star-allele-annotation). We recommend using DRAGEN Array end-to-end starting from IDATs for these analyses. ## BedGraph Files The BedGraph files contains the Log R Ratios (LRR.bedgraph) and B-Allele Frequencies (BAF.bedgraph) from the genotyping algorithm for use in visual tools. ## Star Allele CSV File The Star Allele CSV file is an intermediate file generated by the pgx star-allele call command and serves as the input to the pgx star-allele annotate command. It contains all the star allele calls for all samples in a run. Each row in the file provides either a star allele diplotype or simple variant call for a PGx-related gene. Star allele diplotype calls for a sample and a gene may span multiple lines where alternative solutions can be listed. The Star Allele CSV file also contains meta information marked by # at the top of the file for the genome build and PGx database used for the star allele calling. The star\_allele.csv file contains the following details per sample: | Field | Description | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Sentrix barcode and position of the sample. | | Rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | Gene or Variant | The gene symbol, or gene symbol plus rsID for variants. | | Type | ‘Haplotype’ (star allele) or ‘Variant’ PGx calling type. | | Solution | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | Solution Long |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | Supporting Variants |

All variants present in the array that support the star allele solution. The field has the following format: Long Solution Star Allele: (Supporting Variants).

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome, Location, Reference allele, Alternative allele, Genotype, GenCall score (GS), and B-allele frequency (BAF).

| | Missing/Masked Core Variants | All variants not present in the array or not called in the SNV VCF file for the star allele. The field has the following format: Long Solution Star-Allele: (Missing Variants). | | All Missing Variants in Array | All core definition variants that are not on the array or are not called in the SNV VCF along with the associated star alleles that are impacted. The field has the following format: Missing Variant: (List of impacted star alleles). | | Collapsed Star-Alleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | Score | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Raw Score: | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Copy Number Solution | Estimated copy number for each gene region. The field has the following format: Gene Region: Copy Number. | Below is an example of the first 4 columns from a star allele CSV file: `Sample,Rank,Gene or Variant,Type,Solution` `204650490282_R02C01,1,CYP2C9,Haplotype,*9/*11` `204650490282_R02C01,1,CYP2C19,Haplotype,*2/*10` ## Genotype Summary Files The software produces genotype summary files (gt\_sample\_summary.csv and gt\_sample\_summary.json) that contains the following details per sample: * Sample ID * Sample Name * Sample Folder * Autosomal Call Rate * Call Rate * Log R Ratio Std Dev * Sex Estimate * TGA\_Ctrl\_5716 Norm R * (Optional) User defined fields from the [samplesheet](/dragen-array-v1.3/product-guides/input-files#sample-sheet) The TGA\_Ctrl\_5716 Norm R field is specific to PGx products (e.g., Global Diversity Array with enhanced PGx). The field value is the Normalized R value of one probe and is meant as an assay control where < 1 indicates the sample failed in the TGA (Targeted Gene Amplification) process. If the product does not have this probe, it is not included in the gt\_sample\_summary. The user defined fields from the samplesheet will appear as-is in the gt\_sample\_summary files. e.g. for the given samplesheet: ``` SentrixBarcode_A,SentrixPosition_A,Sample_ID,Sample_Group,MetaData1 204753010023,R01C01,NA1231,Group1,F 204753010024,R01C01,NA1233,Group2,M ``` It would produce something like the following `gt_sample_summary.csv`: ``` Sample ID,Sample Name,Sample Folder,Autosomal Call Rate,Call Rate,Log R Ratio Std Dev,Sex Estimate,SentrixBarcode_A,SentrixPosition_A,Sample_Group,MetaData1 204753010023_R01C01,204753010023_R01C01,/sample/folder,0.99414575,0.98843694,0.14829777,F,204753010023,R01C01,Group1,F 204753010024_R01C01,204753010024_R01C01,/sample/folder,0.99415575,0.98943694,0.14929777,M,204753010024,R01C01,Group2,M ``` And something like the following `gt_sample_summary.json`: ``` [ { "Sample ID": "204753010023_R01C01", "Sample Name": "204753010023_R01C01", "Sample Folder": "/sample/folder", "Autosomal Call Rate": 0.99414575, "Call Rate": 0.98843694, "Log R Ratio Std Dev": 0.14829777, "Sex Estimate": "F", "SentrixBarcode_A": "204753010023", "SentrixPosition_A": "R01C01", "Sample_Group": "Group1", "MetaData1": "F" }, { "Sample ID": "204753010024_R01C01", "Sample Name": "204753010024_R01C01", "Sample Folder": "/sample/folder", "Autosomal Call Rate": 0.99415575, "Call Rate": 0.98943694, "Log R Ratio Std Dev": 0.14929777, "Sex Estimate": "F", "SentrixBarcode_A": "2083757900024", "SentrixPosition_A": "R01C01", "Sample_Group": "Group2", "MetaData1": "M" } ] ``` **Note:** As of v1.3, samples that fail during genotyping will still be present in this file. See the details in the [release notes](https://github.com/illumina-swi/dragen-array-docs/blob/DAv1.3/docs/reference/release-notes/dragen-array-v1.3.0-release-notes#new-features-in-detail). ## Final Report DRAGEN Array Cloud produces a Final Report (gtc\_final\_report.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus per sample: | Field | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | SNP Name | SNP identifier. | | SNP | SNP alleles as reported by assay probes. Alleles on the Design strand (the ILMN strand) are listed in order of Allele A/B. | | Sample ID | Sample identifier. | | Allele 1 – Top | Allele 1 corresponds to Allele A and are reported on the Top strand. | | Allele 2 – Top | Allele 2 corresponds to Allele B and are reported on the Top strand. | | Allele 1 – Forward | Allele 1 corresponds to Allele A and are reported on the Forward strand. | | Allele 2 – Forward | Allele 2 corresponds to Allele B and are reported on the Forward strand. | | Allele 1 – Plus | Allele 1 corresponds to Allele A and are reported on the Plus strand. | | Allele 2 – Plus | Allele 2 corresponds to Allele B and are reported on the Plus strand. | | GC Score | Quality metric calculated for each genotype (data point), and ranges from 0 to 1. | | GT Score | The SNP cluster quality. Score for a SNP from the GenTrain clustering algorithm. | | Log R Ratio | Base-2 log of the normalized R value over the expected R value for the theta value (interpolated from the R-values of the clusters). For loci categorized as intensity only; the value is adjusted so that the expected R value is the mean of the cluster. | | B Allele Freq | B allele frequency for this sample as interpolated from known B allele frequencies of 3 canonical clusters: 0, 0.5 and 1 if it is equal to or greater than the theta mean of the BB cluster. B Allele Freq is between 0 and 1, or set to NaN for loci categorized as intensity only. | | Chr | Chromosome containing the SNP. | | Position | SNP chromosomal position. | *Note: Analyses on products with large numbers of loci (>1 Million) and large numbers of samples (>100) yield a large (50+ Gigabyte) Final Report that are difficult to download and review. It’s recommended to create analysis configurations that do not produce this report if large batches are desired.* For more information on interpreting DNA strand and allele information, see Illumina Knowledge article [How to interpret DNA strand and allele information for Infinium genotyping array data](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001489). ## Locus Summary DRAGEN Array Cloud produces a Locus Summary (locus\_summary.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus: | Field | Description | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Locus\_Name | Locus name from the manifest file. | | Illumicode\_Name | Locus ID from the manifest file. | | #No\_Calls | Number of loci with GenCall scores below the call region threshold. | | #Calls | Number of loci with GenCall scores above the call region threshold. | | Call\_Freq | Call frequency or call rate calculated as follows: #Calls/(#No\_Calls + #Calls) | | A/A\_Freq | Frequency of homozygote allele A calls. | | A/B\_Freq | Frequency of heterozygote calls. | | B/B\_Freq | Frequency of homozygote allele B calls. | | Minor\_Freq | Frequency of the minor allele. | | Gentrain\_Score | Quality score for samples clustered for this locus. | | 50%\_GC\_Score | 50th percentile GenCall score for all samples. | | 10%\_GC\_Score | 10th percentile GenCall score for all samples. | | Het\_Excess\_Freq | Heterozygote excess frequency, calculated as (Observed -Expected)/Expected for the heterozygote class. If $f\_{ab}$ is the heterozygote frequency observed at a locus, and p and q are the major and minor allele frequencies, then het excess calculation is the following: $(f\_{ab} - 2pq)/(2pq + \varepsilon)$ | | ChiTest\_P100 | Hardy-Weinberg p-value estimate calculated using genotype frequency. The value is calculated with 1 degree of freedom and is normalized to 100 individuals. | | Cluster\_Sep | Cluster separation score. | | AA\_T\_Mean | Normalized theta angles mean for the AA genotype. | | AA\_T\_Std | Normalized theta angles standard deviation for the AA genotype. | | AB\_T\_Mean | Normalized theta angles mean for the AB genotype. | | AB\_T\_Std | Standard deviation of the normalized theta angles for the AB genotype. | | BB\_T\_Mean | Normalized theta angles mean for the BB genotypes. | | BB\_T\_Std | Standard deviation of the normalized theta angles for the BB genotypes. | | AA\_R\_Mean | Normalized R value mean for the AA genotypes. | | AA\_R\_Std | Standard deviation of the normalized R value for the AA genotypes. | | AB\_R\_Mean | Normalized R value mean for the AB genotypes. | | AB\_R\_Std | Standard deviation of the normalized R value for the AB genotypes. | | BB\_R\_Mean | Normalized R value mean for the BB genotypes. | | BB\_R\_Std | Standard deviation of the normalized R value for the BB genotypes. | | Plus/Minus Strand | Designated "+" or "-" with respect to the reference genome strand. "U" designates unknown. | ## CN Summary File The sample summary contains per sample key stats for each sample in a batch that contains the following details per sample: * Sample ID * Sample Name * Sample Folder ## Copy Number Batch File The copy number batch summary file (cn\_batch\_summary.csv) shows the total copy number gain, loss, and neutral (CN=2) values for each target region across all the samples in the analysis. Example copy number batch summary file content: `Target Region,Total CN gain,Total CN loss,Total CN neutral` `CYP2A6.exon.1,0,1,47` `CYP2A6.intron.7,0,1,47` `CYP2D6.exon.9,2,4,42` `CYP2D6.intron.2,7,2,39` `CYP2D6.p5,13,2,33` `CYP2E1,2,0,46` `GSTM1,0,42,6` `GSTT1,0,33,15` `SULT1A1,0,0,48` `UGT2B17,0,34,14` `All Target Regions,24,119,337` ## Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Manifest file used to generate GTC is not the same as the manifest file used to generate the CN model. * FASTA files and FASTA index files do not match. For the following scenarios, the software reports messages to the terminal output (as either a warning or an error): * Indel processing for GTC to VCF conversion failed. * The input folder does not contain the required input files. * An input file is corrupt. Examples of such notifications can include the following: | Error | Type | Cause | | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Failed to normalize and gencall sample: {sample\_id}, it will be skipped. Error: The given key '{loci\_id}' was not present in the dictionary. | Warning | This generally occurs because of a mismatch between the manifest (bpm) and cluster file (egt) (i.e., the cluster file was generated via a different manifest). To remedy the issue, use the manifest and cluster files intended for use together. | | Reference allele is not queried for locus: {identifier} | Warning | True reference allele does not match any alleles in the manifest. The error is common for MNVs and will be addressed in future versions of the software. | | Skipping non-mapped locus: {identifier} | Warning | Locus has no chromosome position (usually 0) These loci may be used for quality purposes or CNV calling only. | | Skipping intensity only locus: {identifier} | Warning | Similar to non-mapped loci, intensity only probes have applications outside creating variants for SNV VCFs such as CNV calling. | | Skipping indel: {identifier} | Warning | Indel context (deletion/insertion) could not be determined. | | Failed to process entry for record: {identifier} | Warning | Unable to determine reference allele for indel. | | Incomplete match of source sequence to genome for indel: {identifier} | Warning | Indel not properly mapped to the reference genome. | | Failed to combine genotypes due to ambiguity - exm1068284 (InfiniumII): TT, ilmnseq\_rs1131690890\_mnv (InfiniumII): AA, rs1131690890\_mnv (InfiniumII): AA | Warning | Detailed information about a NoCall ("./.”) in the VCF as a result of combining multiple probes that assay the same variant with conflicting results. The example here is two probes with homozygous REF genotypes (AA) and one probe with homozygous ALT probe (TT) | | Cluster file ({GTC.egt}) is not the same as CN Model Cluster file ({CN\_Model.egt}). | Warning | Cluster file used to generated GTCs used for copy number calling is not the same as was used for the GTCs used during copy number training that created the input CN model. Though CNV model is robust to minor cluster file updates, CNV training should be considered when there are significant updates in the cluster file. To remove the warning, copy number training needs to be re-run with the new GTCs generated via the new cluster file during genotyping, a different CN model with the expected cluster file needs to be used, or different GTCs should be used for copy number calling that were generated using the same cluster file as was used during the generation of the input CN model. | |

{numPassingSamples} sample(s) passed QC.

Requires at least {minPassingSamples} samples to proceed.

| Error | CNV calling is batch dependent and requires a certain number of samples with high-quality to make accurate calls. More high-quality samples need to be added to analysis batch to resolve error. | | Invalid manifest file path {manifestPath} | Error | Application could not find manifest file provided or user error. | | Failed to load cluster file: {e.Message} | Error | Corrupted file or unsupported version. | | System.IO.EndOfStreamException: Unable to read beyond the end of the stream. | Error | Likely failure to read a GTC file, see this [known issue](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.0-release-notes#known-issues) for more details on root cause and a workaround | ## Star allele JSON File The star allele JSON file is produced per sample. It contains the fields present in the [star allele CSV file](#star_allele_csv) as well as additional meta data and annotations. Fields included in the star allele JSON header are described below. | Field | Description | | ------------------------- | ------------------------------------------------------------------------------------------------------------- | | softwareVersion | DRAGEN Array software version, e.g. dragena 1.0.0. | | genomeBuild | Genome build, e.g hg38. | | starAlleleDatabaseSources | Public databases with versions used as the sources of the star allele definitions and population frequencies. | | phenotypeDatabaseSources | Public databases with versions used as the sources of the star allele phenotypes. | | mappingFile | The PGx database file used for the star allele calling. | | pgxGuideline | The PGx guidelines used for metabolizer status/phenotype annotations, e.g. CPIC or DPWG | | sampleId | Sentrix barcode and position of the sample. | | locusAnnotations | The star allele call information. | Fields included in the star allele call (locusAnnotations) information are described below. | Field | Description | | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | gene | The gene symbol. | | callType | ‘Star Allele’ or ‘Variant’ PGx calling type. | | genotype | Most likely star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. More than one solution meeting threshold requirements can be reported. Multiple top solutions are separated by a semi-colon. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotypeDatabaseAnnotation | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), the variant ID (id), and the associated star allele IDs (alleleIds).

| | candidateSolutions | The set of alternative star allele calling solutions, this is only relevant for genes of the ‘Star Allele’ call type. | | missingVariantSites | All core variants that are not available (e.g. not on the array, or no calls in the SNV VCF) for star allele calling for this gene. For star alleles, the field provides an array (list) of variant "id" and impacted "alleleIds" pairs | | allelesTested | Alleles that are covered by the star allele caller. The capability to call star alleles is also dependent on array content coverage and data quality. This field is defined by the array's content and will be the same across all samples. | Fields included in the candidateSolution section, only available for star allele call type, are described below. | Field | Description | | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | genotype | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotype | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | alleles | The composite alleles of the candidate genotype solution. | | solutionLong |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), and the variant ID (id).

| | missingVariantSites | All variants not present in the array or not called in the SNV VCF file for the star allele solution. The field provides an array (list) of missing variants. | | collapsedAlleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | copyNumberRegions | Gene regions for the copy numbers listed in CopyNumberSolution. | | copyNumberSolution | Estimated copy number for each gene region listed in CopyNumberRegions | Example of JSON file content: ```json { "softwareVersion": "dragena 1.3.0", "genomeBuild": "38", "starAlleleDatabaseSources": [ "PharmVar Version: 6.1", "PharmGKB Database Version: Snapshot-2024.05.16", "UGT Alleles Nomenclature: 2010.12.21", "The Human Cytochrome P450 (CYP) Allele Nomenclature Database, July 2024" ], "phenotypeDatabaseSources": [ "CPIC Database Version: 1.38.0", "DPWG Database Version: June 2023" ], "mappingFile": "DRAGENA-549-fix-annotate-sha.e56e884ed1f2d118e796cdab578ab895456bb94e.zip", "pgxGuideline": "CPIC", "sampleId": "207883050020_R08C03", "locusAnnotations": [ { "gene": "CYP2C9", "callType": "Star Allele", "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9999", "supportingVariants": [], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9999, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [], "missingVariantSites": [], "collapsedAlleles": "" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94938719T>G", "alleleIds": "*80" }, { "id": "NC_000010.11:g.94938788C>T", "alleleIds": "*83" }, { "id": "NC_000010.11:g.94938800G>A", "alleleIds": "*76" }, { "id": "NC_000010.11:g.94941975G>A", "alleleIds": "*77" }, { "id": "NC_000010.11:g.94942243T>G", "alleleIds": "*78" }, { "id": "NC_000010.11:g.94942306C>T", "alleleIds": "*72" }, { "id": "NC_000010.11:g.94942308C>T", "alleleIds": "*73" }, { "id": "NC_000010.11:g.94942309G>T", "alleleIds": "*27" }, { "id": "NC_000010.11:g.94947939G>T", "alleleIds": "*74" }, { "id": "NC_000010.11:g.94949145C>T", "alleleIds": "*82" }, { "id": "NC_000010.11:g.94949163del", "alleleIds": "*85" }, { "id": "NC_000010.11:g.94972183A>T", "alleleIds": "*81" }, { "id": "NC_000010.11:g.94981258C>T", "alleleIds": "*79" }, { "id": "NC_000010.11:g.94986136A>C", "alleleIds": "*75" }, { "id": "NC_000010.11:g.94986174G>C", "alleleIds": "*84" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*20,*21,*22,*23,*24,*25,*26,*27,*28,*29,*30,*31,*32,*33,*34,*35,*36,*37,*38,*39,*40,*41,*42,*43,*44,*45,*46,*47,*48,*49,*50,*51,*52,*53,*54,*55,*56,*57,*58,*59,*60,*61,*62,*63,*64,*65,*66,*67,*68,*69,*70,*71,*72,*73,*74,*75,*76,*77,*78,*79,*80,*81,*82,*83,*84,*85" }, { "gene": "CYP2C19", "callType": "Star Allele", "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9958", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G", "alleleIds": "*1" }, { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G", "alleleIds": "*2" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A", "alleleIds": "*2" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G", "alleleIds": "*2" } ], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9958, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "" }, { "solutionLong": "Complete: *2", "supportingVariants": [ { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "*2.001" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94762715T>C", "alleleIds": "*34" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*22,*23,*24,*25,*26,*28,*29,*30,*31,*32,*33,*34,*35,*38,*39" } ``` ### Guidance on alternative star-allele results Typically, the star allele solution with highest quality score is accepted as the final genotype (i.e. star allele diplotype) for the PGx locus. In rare cases, there are lower ranked star allele solutions with quality scores no less than 50% of the highest quality score, these lower ranked solutions are considered feasible and they are all listed in the genotype field of the locus annotation of the PGx gene in the PGx JSON file. Alternative solutions should also be considered if there are supporting variants for those solutions with low (less than 0.15) GS scores. The clustering of low GS scoring supporting variants should also be evaluated for cluster quality and any potential cluster shift. ## Cytogenetics Annotation JSON File DRAGEN Array produces one cytogenetics annotation JSON (\*.json) per sample to report more sample-level, chromosome-level, and event-level metrics and annotations. Example of JSON file content: ```json { "annotateDb": "CytoAnnotateData_DAv1.2.0.zip", "softwareVersion": "dragena 1.3.0 Cyto", "referenceGenome": "file://genome.fa", "annotationType": "Constitutional", "genomeBuild": "hg19", "databaseSources": "RefSeq (Version: GCF_000001405.40-RS_2023_10; Release Date: 2023-10-07),Ensembl (Version: 112; Release Date: 2024-05-14)", "iscnVersion": "ISCN 2020", "sampleId": "208662410005_R01C01", "gcCorrect": true, "minDelProbes": 10, "minDupProbes": 10, "minLOHProbes": 500, "minDelSize": "20kb", "minDupSize": "20kb", "minLOHSize": "3000kb", "minQual": 20, "overallPloidy": 2.012, "callRate": 0.9847303032875061, "logRDev": 0.19977793097496033, "medianLogRDev": 0.1329595363075463, "bafDev": { "AA": 0.015344353947021572, "AB": 0.05078388443393606, "BB": 0.026002263878862304 }, "numLOHOver1M": 4, "numLOHOver8M": 3, "totalSizeLOHOver1M": 49319297, "copyNumberMedian": 2.0, "percentLOH": "1.59%", "sexEstimate": "Female", "traditionalNomenclature": "dup(2)(q32.3q33.1),dup(2)(q33.1q37.1),dup(2)(q37.1q37.3),del(2)(q37.3q37.3),del(2)(q37.3q37.3),dup(3)(p24.3p24.3),del(13)(q34q34)", "microarrayNomenclature": "1p12q21.1(120311442_144549929)x2 hmz,2q32.3q33.1(197045077_201353083)x3,2q33.1q37.1(201356309_234652155)x3,2q37.1q37.3(234653107_238195820)x3,2q37.3(238204076_238283050)x1,2q37.3(238283403_243062047)x1,3p24.3(23235392_23403815)x3,5p12q11.1(44708357_49847659)x2 hmz,11p11.2q12.1(47912150_56507812)x2 hmz,13q34(111358236_111423865)x1,Xp11.22q12(53907828_65253670)x2 hmz", "chromosomeAnnotations": [ { "id": "chr1", "size": 249250621, "percentHet": "10.9587%", "hasMosaicism": false, "lrrMedian": -0.009397780522704124, "lrrDev": 0.16622741086471732, "numLOHOver1M": 0, "numLOHOver8M": 0, "totalSizeLOHOver1M": 0, "percentLOH": "0%", "copyNumberMedian": 2.0, "copyNumberMean": 2.0, "minLogRRatio": -2.739042392000556, "maxLogRRatio": 1.631125334650278, "medianMosaicFraction": ".", "numberDel": 0, "numberDup": 0, "numberLOH": 0, "numberMosaic": 0 }, ... ], "locusAnnotations": [ { "id": "AOH:1:120311442:144549929", "chrom": "chr1", "start": 120311441, "end": 144549929, "callType": "LOH", "mosaicState": false, "mosaicFraction": ".", "copyNumber": 2, "qualityScore": 35.0, "size": 24238488, "effectiveSize": 151008838, "probeCount": 701, "percentHet": "1.01%", "lrrMedian": 0.05862508801510572, "lrrDev": 0.08330211160585141, "bafDev": 0.47622189059186604, "startCytoBand": "1p12", "endCytoBand": "1q21.1", "traditionalNomenclature": "N/A", "microarrayNomenclature": "1p12q21.1(120311442_144549929)x2 hmz", "geneCount": 96, "genes": [ "HMGCS2", "REG4", "NBPF7P", "PFN1P9", "NOTCH2P1", "ADAM30", "RP5-1042I8.7", "NOTCH2", "RP11-114O18.1", ... ] }, ... ] } ``` The fields in the annotation JSON for each sample are described as follows. | Field | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | annotateDb | File name of the database annotation file. | | softwareVersion | Version of DRAGEN Array used for the analysis. | | referenceGenome | File name of the reference genome. | | annotationType | Integer representing annotation methodology where 0=Constitutional and 1=Oncology. | | genomeBuild | Genome build e.g. hg19, hg38. | | databaseSources | Release versions of annotation data. | | iscnVersion | Release date of ISCN formatting used. | | sampleId | ID string assigned to the sample. | | gcCorrect | Boolean indicating whether GC correction was enabled. | | minDelProbes | Deletions must contain this many probes to be reported. | | minDupProbes | Duplications must contain this many probes to be reported. | | minLOHProbes | LOH variants must contain this many probes to be reported. | | minDelSize | Minimum length filter for reporting deletions in kilobases (kb). | | minDupSize | Minimum length filter for reporting a duplication in kb. | | minLOHSize | Minimum length filter for reporting a loss-of-heterzygozity (LOH) variant in kb. | | minQual | Minimum quality score filter for reporting a variant. | | overallPloidy | Arithmetic mean of the ploidy across the genome. This value accounts for the length of all variant calls. The baseline ploidy value without any variants will differ by sex. | | callRate | Frequency of expected calls i.e. #Calls/(#No\_Calls + #Calls). | | logRDev | Standard deviation of the Log R ratio values for all probes. | | bafDev | Standard deviation of the B allele frequency values for each assigned genotype (AA/AB/BB). | | numLOHOver1M | Count of LOH variants detected > 1 Mbp in length. | | numLOHOver8M | Count of LOH variants detected > 8 Mbp in length. | | totalSizeLOHOver1M | Cumulative length of all detected LOH variants > 1 Mbp in length. | | copyNumberMedian | Length-normalized genome-wide median copy number value. Copy number values are assigned to contiguous segements of variable size in the genome by the algorithm. The length-weighted copy numbers of each variant are aggregated to calculate the overall median. | | percentLOH | Percent of the genome comprised of LOH variants. | | sexEstimate | Detected sex of the sample. | | traditionalNomenclature | Simplified ISCN format designation for all detected variants in the sample. | | microarrayNomenclature | ISCN format designation for all detected variants in the sample. | | chromosomeAnnotations | Counts of each type of variant detected per chromosome, including mosaic calls. | | locusAnnotations | Locus level statistics (see additional table for locus-level statistics). | The fields for each chromosome under the chromosomeAnnotations field of the Cyto annotation JSON are described below. | Field | Description | | -------------------- | --------------------------------------------------------------------------------- | | id | Chromosome name. | | size | Chromosome size. | | percentHet | Percent of probes in the chromosome called as heterzygous i.e. AB. | | hasMosaicism | Boolean indicating presence of any mosaic variant on the chromosome. | | lrrMedian | Median log R ratio value of the probes within the chromosome. | | lrrDev | Standard deviation of the log R ratio values of the probes within the chromosome. | | numLOHOver1M | Count of LOH variants detected > 1 Mbp in length. | | numLOHOver8M | Count of LOH variants detected > 8 Mbp in length. | | totalSizeLOHOver1M | Cumulative length of all detected LOH variants > 1 Mbp in length. | | percentLOH | Percent of the genome comprised of LOH variants. | | copyNumberMedian | Copy number median of the chromosome. | | copyNumberMean | Copy number mean of the chromosome. | | minLogRRatio | Minimum Log R ratio of the chromosome. | | maxLogRRatio | Maximum Log R ratio of the chromosome. | | medianMosaicFraction | Median mosaic fraction of mosaic events on the chromosome. | | numberDel | Number of deletion events on the chromosome. | | numberDup | Number of duplication events on the chromosome. | | numberLOH | Number of LOH events on the chromosome. | | numberMosaic | Number of mosaic events on the chromosome. | The fields within each variant (CNV/LOH event) under the locusAnnotations field of the Cyto annotation JSON are described below. | Field | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | id | Unique variant ID containing variant type, chromosome and the start and end positions. | | chrom | Chromosome of the variant. | | start | Variant start position. | | end | Variant end position. | | callType | Variant class (DEL, DUP, LOH). | | mosaicState | Boolean indicating whether the locus is a mosaic variant. | | copyNumber | Copy number of the locus. | | qualityScore | Phred-scaled score of the variant call quality. | | size | Length of the variant. | | effectiveSize | Gap-excluded length of the variant. A gap is defined when probe spacing is more than 150 times the median probe spacing. In that case, the gap is replaced with the median probe spacing. | | probeCount | Number of probes contained in the called variant region. | | percentHet | Percent of probes in the region call as heterzygous i.e. AB. | | lrrMedian | Median log R ratio value of the probes within the variant. | | lrrDev | Standard deviation of the log R ratio values of the probes within the variant. | | bafDev | Standard deviation of the B allele frequency values of the probes within the variant. | | startCytoBand | Cytoband in which the variant starts. | | endCytoBand | Cytoband in which the variant ends. | | traditionalNomenclature | Simplified ISCN format designation for the detected variant. | | microarrayNomenclature | ISCN format designation for the detected variant. | | geneCount | Count of annotated genes within the variant region. | | genes | List of names of all annotated genes within the variant region. | The traditionalNomenclature field is used to describe individual and cumulative copy number variants at a coarse resolution. They show gains (dup) and losses (del) according to their chromosome number, arm (p or q), and band (e.g., p36.13). The microarrayNomenclature field follows the Comparative Genomic Hybridization or SNP array conventions. Values are prefixed with arr\[] to indicate array data as the source, along with the genome build e.g. GRCh38. These data can more precisely describe the location (start\_end in bp) and copy number (x1 for loss, x3 for gain, etc). The genes list field for each variant include those with transcript coordinates that intersect with the described variant. The values included are a combination of HGNC gene symbols taken from NCBI RefSeq database (e.g. [TBC1D3I](https://www.ncbi.nlm.nih.gov/gene/102724862)), as well as the subset of Ensembl gene accessions unmatched to a gene symbol ([ENSG00000278395](https://useast.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000278395;r=17:36241701-36241806;t=ENST00000612652)). ## TBI Index File The TBI (TABIX) index file is associated with the bgzipped VCF files. It allows for data line lookup in VCF files for quick data retrieval. The format is a tab-delimited genome index file developed by Samtools as part of the HTSlib utilities. For more information, visit the [Samtools](http://www.htslib.org/doc/tabix.html) website. ## Methylation Control Probe Output File The software produces a control probe output file ({BeadChipBarcode}\_{Position}\_ctrl.tsv.gz) per sample that includes the raw methylated and unmethylated values for each control probe. Each control probe has an address, type, color channel, name, and probe ID. It also provides the raw signal for methylated green (MG), methylated red (MR), unmethylated green (UG) and unmethylated red (UR). The file can help identify which probes are available on a given BeadChip. ## Methylation CG Output File The software produces a CG output file ({BeadChipBarcode}\_{Position}\_cgs.tsv.gz) per sample that includes beta values, m-values and detection p-values for each CG site. Beta values measure methylation levels in a linear fashion for easy interpretation. Unmethylated probes are close to zero and methylated probes are close to 1. M-values are a log transformed beta value which provides a more representative measure of methylation. Detection p-values measure the likelihood that the signal is background noise. It is recommended that p-value >0.05 are excluded from analysis as they are likely background noise. see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note for further detail on calculation of these metrics. ## Methylation Sample QC Summary Files The software produces methylation sample QC summary in .xlsx and .tsv file formats (sample\_qc\_summary.xlsx and sample\_qc\_summary.tsv) per analysis batch, which provides per sample QC data for all samples in the batch. The QC summary provides details on 21 controls metrics (see tables below), which are computed in same way as in the BeadArray Controls Reporter software from Illumina. In addition, it provides average red and green raw and normalized signals, time of scanning, proportion of probes passing, overall sample pass/fail status, and the failure codes for control metrics that did not pass. The sample pass status is defined as the passing of all 21 control metrics. The QC summary .xlsx file further highlights failing parameters for easy viewing. The QC summary files contain the following fields: | Field | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Sentrix\_ID | 12-digit BeadChip Barcode associated with the sample. | | Sentrix\_Position | Row and column on the BeadChip ie R01C01 | | Sample\_ID | Optional field that can be indicated using IDAT Sample Sheet | | User Defined Meta Data | Optional field(s) that can be indicated using IDAT Sample Sheet. Any number of fields indicated will appear in this output file. | | restoration |
  • The default threshold is 0.
  • If using the FFPE DNA Restore Kit, the restoration control identifies success of the FFPE restoration chemistry. Change the threshold from 0 to 1 if the FFPE DNA Restore Kit was used.
  • The green channel intensity is higher than Background. Therefore, the metric provided is the Green Channel Intensity/Background.
| |

staining\_green

staining\_red

|
  • Staining controls are used to examine the efficiency of the staining step in both the red and green channels. These controls are independent of the hybridization and extension step.
  • The green channel shows a higher signal for biotin staining when compared to biotin background, whereas the red channel shows higher signal for DNP staining when compared to DNP background.
  • The metric provided for green is the (Biotin High value)/ (Biotin Bkg) and the metric provided for red is (DNP High value)/(DNP Bkg value)
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

extension\_green

extension\_red

|
  • Extension controls test the extension efficiency of A, T, C, and G nucleotides from a hairpin probe, and are therefore sample independent.
  • In the green channel, the lowest intensity for C or G is always greater than the highest intensity for A or T.
  • The metric provided is the (lowest of the C or G intensity)/ (highest of A or T extension) for a single sample.
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

hybridization\_high\_medium

hybridization\_medium\_low

|
  • Hybridization controls test the overall performance of the Infinium Assay using synthetic targets instead of amplified DNA. These synthetic targets complement the sequence on the array, allowing the probe to extend on the synthetic target as a template. Synthetic targets are present in the Hybridization Buffer at 3 levels, monitoring the response from high-concentration (5 pM), medium concentration (1 pM), and low concentration (0.2 pM) targets. All bead type IDs result in signals with various intensities, corresponding to the concentrations of the initial synthetic targets.
  • The value for high concentration is always higher than medium and the value for medium concentration is always higher than low.
  • The metric provided is the value of high/medium and the value of medium/low.
  • The default thresholds are 1. Do not change the default threshold.
| |

target\_removal1

target\_removal2

|
  • Target removal controls test the efficiency of the stripping step after the extension reaction. In contrast to allele-specific extension, the control oligos are extended using the probe sequence as a template. This process generates labeled targets. The probe sequences are designed such that extension from the probe does not occur. All target removal controls result in low signal compared to the hybridization controls, indicating that the targets were removed efficiently after extension. Target removal controls are present in the Hybridization Buffer.
  • The Background for the same sample is close to or larger than either control.
  • The metric provided is Background/Control Intensity.
  • The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion1\_green

bisulfite\_conversion1\_background\_green

bisulfite\_conversion1\_red

bisulfite\_conversion1\_background\_red

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium I probe design and allele-specific single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "C" (Converted) probes matches the converted sequence and get extended. If the sample has unconverted DNA, the "U" (Unconverted) probes get extended. There are no underlying C bases in the primer landing sites, except for the query site itself.

  • The calculation is done in both the green and red channels separately to provide 2 unique sets of values:

    • Green Channel

      • Lowest value of C1 or C2 / Highest value of U1 or U2. The default threshold is 1. This value can be increased for some scanners.
      • Background/(U1, or U2). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.

    • Red Channel

      • Lowest value of C3, 4, or 5 / Highest value of U3, 4, or 5. The default threshold is 1. This value can be increased for some scanners.
      • Background /(Highest value of U4, U5, or U6). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion2

bisulfite\_conversion2\_background

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium II probe design and single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "A" base gets incorporated and the probe has intensity in the red channel. If the sample has unconverted DNA, the "G" base gets incorporated across the unconverted cytosine, and the probe has elevated signal in the green channel.
  • The calculation is done using both channels for 1 set of numbers returned.

  • The following metrics are provided:

    • (Lowest of red C 1, 2, 3, or 4) / (Highest of green C 1, 2, 3, or 4). The default threshold is 1. This value can be increased for some scanners.
    • Background/(Highest C1, C2, C3, or C4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

specificity1\_green

specificity1\_red

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor allele-specific extension for Infinium I probes. The methylation status of a particular cytosine is carried out following bisulfite treatment of DNA by using query probes for unmethylated and methylated state of each CpG locus. In assay oligo design, the A/T match corresponds to the unmethylated status of the interrogated C, and G/C match corresponds to the methylated status of C. G/T mismatch controls check for nonspecific detection of methylation signal over unmethylated background. PM controls correspond to A/T perfect match and give high signal. MM controls correspond to G/T mismatch and give low signal.
  • The metrics provided are the ratio of the lowest PM/highest MM in each channel.
  • The default threshold is 1. Do not change the default threshold.
| |

specificity2

specificity2\_background

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor extension specificity for Infinium II probes and check for potential nonspecific detection of methylation signal over unmethylated background. Specificity II probes incorporate the "A" base across the nonpolymorphic T and have intensity in the Red channel. If there was nonspecific incorporation of the "G" base, the probe has elevated signal in the Green channel.

  • The following metrics are provided:

    • (Lowest intensity of S1, S2, or S3 red) / (Highest intensity of S1, S2, or S3 green). The default threshold is 1. Do not change the default threshold.
    • Background/(Highest intensity S1, S2, S3, or S4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

nonpolymorphic\_green

nonpolymorphic\_red

|
  • Nonpolymorphic controls test the overall performance of the assay, from amplification to detection, by querying a particular base in a nonpolymorphic region of the genome. They let you compare assay performance across different samples. One nonpolymorphic control has been designed for each of the 4 nucleotides (A, T, C, and G).
  • In the green channel, the lowest intensity of C or G is always greater than the highest intensity of A or T.
  • The metric provided is the (lowest intensity for C or G) /(highest intensity for A or T) for a single sample.
  • The default threshold is 5. This value can be increased for some scanners.
| |

avg\_green\_raw

avg\_red\_raw

|
  • Average green and red raw signal for the given sample.
| |

avg\_green\_norm

avg\_red\_norm

|
  • Average green and red signal after dye bias correction and noob normalization for the given sample.
| | ScanTime |
  • The date (MM/DD/YY) and time (HH:MM) that the sample was scanned by the iScan system.
| | NProbes |
  • Number of probes on the BeadChip, including SNP and CG probes
| | NPassDetection |
  • Number of probes on the BeadChip that passed detection p-value at the threshold defined.
| | prop\_probes\_passing |
  • The proportion of probes passing defined as the number of probes passing detection p-value divided by the total number of probes on the BeadChip.
| | passQC |
  • 1 = sample passed all QC metrics for the thresholds defined
  • 0 = sample did not pass all QC metrics for the thresholds defined
| | failCodes |
  • The list of parameters that failed QC for the thresholds defined.
| The control metrics in the QC summary files are calculated as following. The default value for background correction offset (x) of 3,000 can be modified and applies to all background calculations indicated with (bkg + x). Note that the table uses default thresholds for EPIC arrays as example, the default thresholds changes with the methylation arrays. See section [Threshold Adjustment](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis#methylation-qc-threshold-adjustment) for additional details.
ControlCalculationAdditional Information
Restoration Green > bkg(Green/(bkg+x))> 0
  • If using the FFPE Restore kit, change the default threshold from 0 to 1.
  • bkg = Extension Green highest A or T intensity

Staining Green

Biotin High > Biotin Bkg

(High/Biotin Bkg) > 5

Staining Red

DNP High > DNP Bkg

(High/DNP Bkg) > 5
Extension Green Lowest CG/Highest AT(C or G/A or T) > 5Green channel—Lowest C or G intensity is used; highest A or T intensity is used.

Extension Red

Lowest AT/Highest CG

(A or T/C or G) > 5Red channel—Lowest A or T intensity is used; highest C or G intensity is used.
Hybridization Green High > Medium > Low(High/Med) > 1
(Med/Low) > 1
Target Removal Green ctrl 1 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity
Target Removal Green ctrl 2 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity

Bisulfite Conversion I Green

C1, 2 > U1, 2

(C/U) > 1
  • Lowest C intensity is used. Highest U intensity is used.

Bisulfite Conversion I Green

U ≤ bkg

((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Green channel—bkg = Extension Green highest AT
Bisulfite Conversion I Red C3, 4, 5 > U3, 4, 5(C/U) >1
  • Lowest C intensity is used. Highest U intensity is used.
Bisulfite Conversion I Red U ≤ bkg((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Red Channel—bkg = Extension Red highest CG
Bisulfite Conversion II C Red > C Green(C Red/ C Green) > 1
  • For MSA arrays, the default is 0.5
  • Lowest C Red intensity is used. Highest C Green intensity is used.
Bisulfite Conversion II C green ≤ bkg((bkg + x)/C Green) > 1
  • For MSA arrays, the default is 0.5
  • Highest C Green intensity is used.
  • Green channel—bkg = Extension Green highest AT
Specificity I Green PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used
Specificity I Red PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used

Specificity II

S Red > S Green

(S Red/ S Green) > 1
  • Lowest S Red intensity is used. Highest S Green intensity is used.

Specificity II

S Green ≤ bkg

((bkg + x)/ S green) > 1
  • bkg = Extension Green highest A or T intensity
  • Highest S Green intensity is used.
Nonpolymorphic Green Lowest CG/ Highest AT(C or G/ A or T) > 5
  • Lowest C or G intensity is used; highest A or T intensity is used
  • For MSA arrays, the default threshold is 2.5
Nonpolymorphic Red Lowest AT/ Highest CG(A or T/ C or G) >5
  • Lowest A or T intensity is used; highest C or G intensity is used
  • For MSA arrays, the default threshold is 3
## Methylation Sample QC Summary Plots The software produces methylation sample QC summary plots (sample\_qc\_summary.pdf) per analysis batch which provides visual depictions of two QC summary plots for quick visual review. The file contains the following control plots: | Control Plot | Description | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Proportion of Probes Passing Threshold | Histogram of the proportion of probes passing the p-value detection threshold. Samples passing QC are shown in one color, and samples failing QC are shown in another color. | | Principal Component Analysis (PCA) | Uses beta values for all analytical probes to compare samples. Principal component analysis (PCA) is applied to the beta values to reduce the dimensionality of the data to two “principal components” that reflect the most variation across samples. If more than 100 samples are used in the analysis, a random subset of 10,000 probes are used for the PCA analysis to reduce computational burden. PCA control plot assigns unique colors to each sample group defined by the IDAT Sample Sheet. If no groups were assigned, all samples will appear the same color. Sample groups may cluster together and can be used to explain some of the variation. Coordinates used to plot each sample in the PCA control plot are provided in the pcs.tsv.gz output file (see below). | ## Methylation Principal Component Summary The software produces a methylation principal component summary file (pcs.tsv.gz) per analysis batch which provides principal component data for each sample within the batch. This can be used to identify the specific samples associated with points on the PCA control plot within the Methylation Sample QC Control Plots output file. The files contain the following fields: | Field | Description | | --------------------- | -------------------------------------------------------------------------------------------------------------------- | | blank | BeadChip Barcode and Position ie 123456789101\_R01C01 | | principal component 1 | The variable of the first axis for the Principal Component Analysis | | principal component 2 | The variable of the second axis for the Principal Component Analysis | | Sample\_Group | Sample group defined by the user in the IDAT Sample Sheet. If no sample group was defined, all samples will show NA. | ## Methylation Manifest Files The software produces two methylation manifest files 1. Manifest in Sesame format (probes.csv) 2. Additional information for control probes (controls.csv) The probes.csv file has the following columns: | Field | Description | | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | | U | This is corresponds to the AddressA\_ID column in the standard Illumina manifest format. | | M | This corresponds to the AddressB\_ID column in the standard Illumina manifest format. | | col | This is the color channel for Infinium I probes (R/G). For Infinium I probes, this column will be NA. | The controls.csv file has the following columns: | Field | Description | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Address | The address of the probe | | Type | The control probe type | | Color\_Channel | A color used to denote certain control probes in legacy software | | Name | A human readable identifier for certain control probes | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | ## Methylation Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Missing IDATs or manifest * Incorrect sample sheet formatting * Duplicate BeadChip Barcode and Position within the sample sheet * Missing control or assay probes * Missing required columns in the manifest * Unable to compute certain metrics Examples of such notifications can include the following: | **Log** | **Error** | **Type** | **Cause** | | ----------------------------------- | ---------------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | write\_samplesheet.log | No IDATs found | Error | No IDATs provided for analysis | | format\_samplesheet.log | No samples in sample sheet | Error | No samples in user’s sample sheet input | | format\_samplesheet.log | Sample sheet not correctly formatted | Error | Sample sheet is not in CSV format or header lines do not start with “<” | | format\_samplesheet.log | beadChipName and sampleSectionName columns are required for the sample sheet. | Error | Sample sheet does not contain required columns: beadChipName and sampleSectionName. | | format\_samplesheet.log | Warning: \ samples have duplicate Sample\_ID | Warning | X lines in the sample sheet have duplicate \\_\. Duplicates are dropped from analysis. | | convert\_manifest\_ilmn\_sesame.log | Missing control probes in manifest | Error | Missing “\[Controls]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Probe section not found | Error | Missing “\[Assay]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Missing required columns: IlmnID, AddressA\_ID, AddressB\_ID, Color\_Channel | Error | Missing one of required columns in Assay section of manifest | | convert\_manifest\_ilmn\_sesame.log | Controls not formatted correctly. Must have 4 columns (Address,Type,Color\_Channel,Name) | Error | Missing one of required columns in Control section of manifest | | run\_sesame\_gs.log | Missing sample: \ | Error | Missing idats for a particular sample | | run\_sesame\_gs.log | No scan time available | Warning | No scan time in idat | | run\_sesame\_gs.log | Prep failed | Error | Dye bias correction or noob failure for sample | | run\_sesame\_gs.log |

Warning: missing control probe types

\

| Warning | Missing control probe types to compute a BACR metric. Metric will be set to NA. | | run\_sesame\_gs.log |

Warning: missing control probe names

\

| Warning | Missing control probes to compute a BACR metric. Metric will be set to NA. | | qc.log | No features, skipping PCA plot | Warning | No common betas found in all samples. This may occur if a sample has no signal intensity in the IDAT files. | [^1]: If using the FFPE Restore kit, change the default threshold from 0 to 1. [^2]: For MSA arrays, the default is 0.5. [^3]: For MSA arrays, the default is 2.5. [^4]: For MSA arrays, the default is 3. # Support and Additional Resources ## Technical Support For support, questions, and feedback on DRAGEN Array, please contact Illumina Tech Support at . ## Additional Resources | Resource | Description | | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | [DRAGEN Array Webpage](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html) | Product features and benefits and allows product ordering. | | [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) | Support site for DRAGEN Array which includes installers and product documentation. | | [DRAGEN Array Methylation QC analysis](https://developer.illumina.com/news-updates/dragen-array-1-0-now-supporting-methylation-qc-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 Methylation QC. | | [DRAGEN Array PGx Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 PGx analysis. | | [Infinium Lab Setup and Best Practices](http://support-docs.illumina.com/ARR/infinium-labsetup.htm) | Lab setup and maintenance information for Infinium assays. | | [Evaluation of Infinium Genotyping Assay Controls using GenomeStudio](https://support.illumina.com/content/dam/illumina-support/courses/eval-inf-controls/story_content/external_files/Infinium_Controls_Training_Guide.pdf) | Instructions for evaluating assay controls using GenomeStudio | | [GenomeStudio Genotyping: Evaluating Infinium Assay Controls](https://www.youtube.com/embed/MuDBayIegkg?autoplay=1\&rel=0) | Video instructions for evaluating assay controls using GenomeStudio | | [Infinium Assay Consumables & Equipment List](http://support-docs.illumina.com/ARR/infinium-consumables.htm) | List of consumables and equipment used in Infinium assays. | | [iScan System Product Documentation](http://support-docs.illumina.com/ARR/iscan.htm) | Instructions for operating and maintaining the iScan System. | | [Polygenic Risk Score – Predict](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) | Instructions for using the Polygenic Risk Score – Predict Module. | | [Illumina Connected Analytics](https://help.connected.illumina.com/illumina-connected-analytics) | Instructions for using the hosted environment Illumina Connected Analytics. | | [BaseSpace Sequence Hub](https://help.basespace.illumina.com/) | Instructions for using the hosted environment BaseSpace Sequence Hub. | | [Emedgene](https://help.connected.illumina.com/emedgene) | Instructions for using Emedgene software | ### # Frequently Asked Questions 1. **Is DRAGEN Array analysis a local (on-premises) or cloud solution?**\ DRAGEN Array analysis is available locally (on-premises) and cloud. DRAGEN Array Local Analysis utilizes a command-line interface for power users to have granular control and flexibility to support large scale microarray genomic studies. Deployed on Windows or Linux operating systems, the local package is CPU-based and does not require a specialized server or hardware. DRAGEN Array Cloud Analysis utilizes the user-friendly, graphical interface of BaseSpace Sequence Hub to simplify analysis setup and kickoff. 2. **Which Infinium arrays is DRAGEN Array compatible with?**\ Refer to the Product and Analysis Compatibility table in the [Applications](/dragen-array-v1.3/overview/our-features) section. 3. **How many samples are needed per analysis?**\ **Cytogenetics:** As few as one sample can be used for cytogenetics. Multiple analysis batches can be kicked off and run in parallel. **Genotyping:** As few as one sample can be used for genotyping. Multiple analysis batches can be kicked off and run in parallel. **Pharmacogenomics:** A minimum of 24 samples is required for PGx CNV calling with 22 passing QC. Passing QC is defined as Log R Dev < 0.2. 96 samples are recommended for the most accurate CNV results. Multiple analysis batches can be kicked off and run in parallel. 4. **Which PGx CNVs and star alleles are available?**\ Please refer to the DRAGEN Array [release notes](/dragen-array-v1.3/reference/release-notes). 5. **Where can I find demo data?**\ Demo data is available in BaseSpace under the “Demo Data” section. All array data starts with “iScan:” and includes the name of the type of analysis. Supported types of analysis can be found in the [Applications](/dragen-array-v1.3/overview/our-features) section. 6. **Where can I find assay QC metrics?** Dragen Array currently provides functional QC metrics such as call rate and Log R Dev. See [Support and Additional Resources](/dragen-array-v1.3/reference/support-and-additional-resources) for instructions on how to evaluate assay QC using GenomeStudio. # Release Notes The following versions of DRAGEN Array have been released: * [DRAGEN Array v1.3.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes) * [DRAGEN Array v1.3.0 + Emedgene V100.39.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes/dragen-array-v1.3.0-emg-release-notes) * [DRAGEN Array v1.2.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.2.0-release-notes) * [DRAGEN Array v1.2.0 EMGv38 Automatic Case Creation Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.2.0-release-notes/dragen-array-v1.2.0-cyto-emg-release-notes) * [DRAGEN Array v1.1.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.1.0-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.1 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.1-cloud-methylqc-release-notes) * [DRAGEN Array v1.0.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.0-release-notes) * [DRAGEN Array Genotyping Cloud v1.0.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-genotype-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-methylqc-release-notes) # DRAGEN Array v1.3.0 Release Notes ## **RELEASE DATE** August 2025 ## **RELEASE HIGHLIGHTS** * Provides mosaic fraction estimation for mosaic events. * Improved accuracy of sex chromosome calling, including pseudo-autosomal regions (PAR). * New QC metrics available in cytogenetics JSON output. ## **NEW FEATURES IN DETAIL** * Genotyping & Core * GenomeStudio backwards compatible samplesheet support and related deprecation of separate IDAT and GTC samplesheets. * User-defined data from the samplesheet will get passed to gt\_sample\_summary files during genotyping. * Samples that fail IDAT->GTC conversion during `genotype call` will be added to the gt\_sample\_summary instead of skipped. For these samples, the `Autosomal Call Rate` and `Call Rate` will be set to 0 while the `Log R Ratio Std Dev` and `TGA_Ctrl_5716 Norm R` (when applicable for PGx products) are set to `NaN`. * Cytogenetics * Fixed an issue causing cyto calling to crash due to overflow errors for noisy samples in v1.2. * Fixed a memory issue in v1.2 that limited the number of samples able to run to about 200. * Improved accuracy of length normalized median copy number calculation by removing lower limit on included variant size (1 Kbp). * Added reporting of mosaic fraction for mosaic events. * Added a method for promoting mosaic events above user-defined mosaic fraction. * Reduced verbosity in STDOUT messages produced by annotate command. * Added sample-level Median Log R Dev statistic to annotate JSON output. * Added chromosome-level QC metrics to the annotation JSON output. * Added an event-level QC metric `effective size` to the JSON output. * Variants are filtered by their effective size in the `cyto call` command. In the `cyto annotate` command, they are filtered by the raw size. * Fixed a bug whereby the minimum deletion/LOH/duplication thresholds were shown in the wrong units in the annotation JSON, when set higher than the calling thresholds. * Fixed a bug that prevented cyto CNV variants with quality scores of 0 from appearing in the output json files. * The cytogenetic caller now attempts to resolve sample sex if previously classified as unknown by the upstream genotyping module, enabling more accurate results. A log message is generated when sex is resolved, e.g., "Sample XXX sex updated from Unknown to Male." * Pharmacogenomics * Fixed a bug in the `pgx star-allele annotate` command, sample with a reference allele for ABCG2 genes will now be annotated properly using default annotation "Normal" for reference alleles. * Corrected the CYP2A6 \*1 definition. Removed NC\_000019.10:g.40848264\_40848265delinsT variant that was incorrectly added to the CYP2A6 \*1 definition ## **KNOWN ISSUES** * The [samplesheet](/dragen-array-v1.3/product-guides/input-files#sample-sheet) does not handle empty columns. For example this samplesheet: ``` SentrixBarcode_A,SentrixPosition_A,, 204753010023,R02C01,, ``` Will throw the following error: `System.ArgumentException : Duplicate column found. Column names are case-insensitive. Please remove or rename the column from the samplesheet and re-process.` And this example: ``` SentrixBarcode_A,SentrixPosition_A, 204753010023,R02C01, ``` Will produce an empty column/field in the [Genotype Sample Summary files](/dragen-array-v1.3/product-guides/output-files#genotype-summary-files), e.g., ``` { "SentrixBarcode_A": "204753010023", "SentrixPosition_A": "R02C01", "Sample ID": "204753010023_R02C01", "Sample Name": "204753010023_R02C01", "Sample Folder": "/tmp", "Autosomal Call Rate": 0.99, "Call Rate": 0.99, "Log R Ratio Std Dev": 0.15, "Sex Estimate": "F", "": "" } ``` ## **KNOWN LIMITATIONS** * If the genotyping module reports an unknown sex and the cytogenetic caller cannot resolve it, the caller assumes the sample is male. As a result, sex chromosome detection may be inaccurate if the sample is actually female. This behavior is not currently output in the log. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 Kb in length. This is often cited as a minimum size limit used to define copy number variants. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions are likely to be false positives. * ISCN annotations are not provided for LOH variants in the cytogenetic annotation JSON output file. * DRAGEN Array Cytogenetics analysis is intended for constitutional samples only, oncology samples not supported at this time. * DRAGEN Array Cytogenetics analysis is validated only for specific array platforms: Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, and Infinium CytoSNP-850K BeadChip (iScan System). * **Note:** DRAGEN Array can process IDAT files from the NextSeq550 for cytogenetic analysis, but this setup hasn’t been formally validated. If you're interested in trying it, check out the demo data in the ‘Demo Data’ section on BaseSpace, which was generated using the iScan system. * DRAGEN Array Cytogenetics analysis may call large events that are broken into smaller pieces and require visual confirmation. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * Tabix indexing from DRAGEN Array is not exactly the same as [bcftools index --tbi](https://samtools.github.io/bcftools/bcftools.html#index). For instance, if you run `bcftools index --stats in.vcf.gz` or `bcftools index --nrecords in.vcf.gz`, with certain versions of bcftools, you may get the following error: `index of in.snv.vcf.gz does not contain any count metadata. Please re-index with a newer version of bcftools or tabix.`. If these tools are critical to user's bioinformatics pipelines a workaround would be to unzip and re-index DRAGEN Array VCFs using bcftool's tabix. But please note, these index files may not work in downstream VCF-based DRAGEN Array commands like `pgx star-allele call`. Please use DRAGEN Array end-to-end for analysis flows like the ones detailed in the [Quick Start](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#quick-start) guide. * There can be some minor differences when running `pgx star-allele call` on Windows vs. Linux. During verification testing, out of 1576 samples, we noticed the following discordance: | Field name | Number of differences | | ---------------------------- | --------------------- | | Collapsed Star-Alleles | 2 | | Missing/Masked Core Variants | 1 | | Solution Long | 1 | | Supporting Variants | 2 | * **Note:** All overall solutions tested for comparison were found to be concordant. * DRAGEN Array v1.3 is not compatible with Emedgene (EMG) v38. I.e., it does not support automatic case creation and you can't manually upload [Cytogenetics VCF Files](/dragen-array-v1.3/product-guides/output-files#cytogenetics-vcf-file) from v1.3 into EMG. Users should continue to use `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation 1.2.0` for DRAGEN Array + EMG cyto analyses. # DRAGEN Array v1.3.0 + Emedgene V100.39.0 Release Notes ## **RELEASE DATE** October 2025 ## **RELEASE HIGHLIGHTS** * Release of `DRAGEN Array – Cytogenetics analysis + Emedgene interpretation 1.3.0` * VCFs generated on Windows now compatible with Emedgene ## **NEW FEATURES IN DETAIL** * See new features for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes#new-features-in-detail). * See Emedgene V100.39.0 new features in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#support-for-dragen-array-v1.3). ## **KNOWN ISSUES** * See known issues for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes#known-issues). * See known issues for Emedgene in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#known-issues). ## **KNOWN LIMITATIONS** * See known limitations for DRAGEN Array – Cytogenetics analysis in the [1.3.0 release notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.3.0-release-notes#known-limitations). * See Emedgene V100.39.0 limitations in the [V100.39.0 release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v100.39.0-october-16th-2025#limitations). * See the [Prerequisites](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis#prerequisites) section in the cloud setup guide for detailed setup instructions. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all users regardless of Emedgene (EMG) subscription status or SNS notification settings. The software does not enforce an EMG subscription in the workgroup. Without an EMG subscription and SNS configuration, the analysis will start and run as the "DRAGEN Array – Cytogenetics analysis" type; however, "Automatic Case Creation on EMG" will not occur. # DRAGEN Array v1.2.0 Release Notes ## **RELEASE DATE** February 2025 ## **RELEASE HIGHLIGHTS** * Whole-genome copy number and loss of heterozygosity (LOH) calling, with VCF output format, for any human genotyping array. * B-allele frequency bedgraph output file to power informative CNV visualizations. * Additional outputs including ISCN and cytoband nomenclature to support cytogenetics applications. ## **NEW FEATURES IN DETAIL** * Cytogenetics Calling and VCF Output * Ability to obtain output files for any human genotyping array. Detection abilities vary by array probe density and spacing. * Detects copy number up to 4+. * Provides Phred scaled quality score to assess the event quality. * Addition of mosaic tagging to detect mosaic deletions and duplications. * Three arrays tested for performance including: * Infinium Global Diversity Array with Cytogenetics-8 * Infinium Global Screening Array with Cytogenetics-24 * Infinium CytoSNP-850K BeadChip using the iScan System * Ability to adjust minimum size and probe number for copy number and LOH event calling * BAF and LRR Bedgraph files * Additional bedgraph file output for B-allele frequency (BAF) for use in visualization. Updated file extensions to differentiate BAF.bedgraph and LRR.bedgraph files. * Added a smoothing parameter to the genotype gtc-to-bedgraph command for LRR.bedgraph (log R ratio bedgraph file) generation for improved visualization. * Bedgraph files are compatible with IGV (Integrative Genomics Viewer) for visualization purposes. * Cytogenetic annotation and JSON Output * Provides summary statistics per sample and per CNV/LOH event. Includes gene count and gene names within each event based on the RefSeq database. * Annotates each event using International System for Human Cytogenomic Nomenclature (ISCN) 2020 and cytoband nomenclature based on Ensembl database. * Pharmacogenomics * Added root command *pgx* for grouping PGx copy number and star allele calling. * Fixed issue causing pgx star-allele annotate command to fail mid-analysis from version 1.1. ## **KNOWN ISSUES** * If a sample's sex estimate is called as unknown in the genotyping module, the cytogenetic caller will assume the sample is male. Consequently, detection results on sex chromosomes could be inaccurate if the sample is actually female. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 Kb in length. This is often cited as a minimum size limit used to define copy number variants. * ISCN annotations are not provided for LOH variants in the cytogenetic annotation JSON output file. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions are likely to be false positives. * The `cyto annotate` subcommand produces extraneous logs (e.g., `No credential is provided`) that can be safely ignored. * During `cyto call`, there is a log for the `CytoPlatform` currently hardcoded to `LCG` regardless of the product used. This has no bearing on the underlying algorithm and is just what is reported in the log. It can be safely ignored. * A non-default value of the `--smoothing` parameter for the [genotype gtc-to-bedgraph](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#genotype-gtc-to-bedgraph) command triggers a bug causing wrong values in the LogR Ratios (LRR) bedgraph. It is advised users use the default (0), which produces a valid LRR bedgraph with raw signal for visualization purposes. The --smoothing parameter will be disabled in next release of DRAGEN Array. * The `cyto call` command may throw an overflow error in very rare cases when no variants are detected in noisy or low-quality samples. Contact if you encounter this issue. * The minimum deletion/LOH/duplication thresholds shown in the cyto annotation JSON may be shown in the wrong units when set higher than the cyto calling thresholds. * Cyto CNV/LOH variants with quality scores of 0 seen in the cyto call VCF files cannot be passed into the annotation output json files. * CYP2A6 \*1 definition incorrectly includes NC\_000019.10:g.40848264\_40848265delinsT. * `DRAGEN Array – Cytogenetics Calling` and `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` cloud analyses may fail around 200 samples in one batch due to high memory usage. Recommended workaround is to run smaller batches. * Sample with a reference allele for ABCG2 genes will have missing phenotype annotations when running the command `pgx star-allele annotate`. ## **KNOWN LIMITATIONS** * DRAGEN Array Cytogenetics analysis is intended for constitutional samples only, oncology samples not supported at this time. * DRAGEN Array Cytogenetics analysis was only validated for specific array platforms (Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, Infinium CytoSNP-850K BeadChip using the iScan System). * DRAGEN Array Cytogenetics analysis may call large events that are broken into smaller pieces and require visual confirmation. * DRAGEN Array Cytogenetics analysis does not produce mosaic fraction estimation or mosaic ISCN notation at this time. * When using CytoSNP-850Kv1-4\_iScan\_B, GSACyto-24v1\_20044998\_C, or GDACyto-8v1-0\_20047166\_E manifests, DRAGEN Array Cytogenetics analysis will be unable to call events or visualize probes in the PAR (pseudo-autosomal regions). Please reach out to for additional details. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * IDATs originating from NextSeq550 not tested. # DRAGEN Array v1.2.0 Emedgene V38.0 Automatic Case Creation Release Notes ## **RELEASE DATE** June 2025 ## **RELEASE HIGHLIGHTS** * Automatic case creation in Emedgene (EMG) following the successful completion of a `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` analysis from Basespace (powered by ICA). ## **NEW FEATURES IN DETAIL** * See existing features for DRAGEN Array Cytogenetics analysis in the [1.2.0 release notes](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.2.0-release-notes) * For more details on the EMGv38 features, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN ISSUES** * See existing issues for DRAGEN Array Cytogenetics analysis in the [1.2.0 release notes](/dragen-array-v1.3/reference/release-notes) * For more details on the EMGv38 known issues, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN LIMITATIONS** * Please see the [Prerequisites](/dragen-array-v1.3/product-guides/dragen-array-cloud-analysis#prerequisites) section in the cloud setup page for detailed guidance on how to setup this analysis. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all users regardless of Emedgene (EMG) subscription status or SNS notification settings. The software does not enforce an EMG subscription in the workgroup. Without an EMG subscription and SNS configuration, the analysis will start and run as the "DRAGEN Array – Cytogenetics analysis" type; however, "Automatic Case Creation on EMG" will not occur. # DRAGEN Array v1.1.0 Release Notes ## **RELEASE DATE** September 2024 ## **RELEASE HIGHLIGHTS** * New EX PGx beadchips enabled for PGx analysis * Increased coverage of high priority PGx genes * Custom optimized .egt files accepted in PGx analysis * Up-to-date database reflecting latest versions of public PGx resources * DPWG guidelines now available for metabolizer status calling on cloud analysis ## **NEW FEATURES IN DETAIL** * DRAGEN Array supports multiple PGx products * Two new EX PGx beadchips enabled through genotyping, PGx CNV calling, and star allele annotation * Infinium Global Screening Array with Enhanced PGx-48 v4.0 Kit * Infinium Global Clinical Research Array with Enhanced PGx-24 v1.0 Kit * In total 3 PGx products supported: GDA-ePGx, GSAv4-ePGx, GCRA-ePGx. See the [Product & Analysis Compatibility table](/dragen-array-v1.3/overview/our-features#product--analysis-compatibility) for more details. * Increased coverage of high priority PGx genes * Star allele annotation now covers CYP2E1, CYP1A2, ABCG2, CYP2C8, HMGCR, UGT1A4, UGT2B15, F13A1, and HLA-B\*15:02 * CNV calling now covers SULT1A1 * Extended bi-allelic PGx variants from source databases to multi-allelic variants based on the designs in the supported PGx products. * See [PGx Star Allele Coverage](/dragen-array-v1.3/reference/pgx-star-allele-coverage) and [PGx CNV Coverage](/dragen-array-v1.3/reference/pgx-cnv-coverage) for the full coverage lists. * Allows flexibility for GTCs generated with a custom cluster file (.egt) to be used with the commercial CN model file (.dat). This alleviates the burden to retrain the CN model file. * The cluster file is a required input for the genotype call command in DRAGEN Array. The CN (Copy Number) model file is a required input to the copy-number call command to enable accurate copy number calling for pharmacogenomics. Custom cluster files and CN model files may be required for optimal genotyping and PGx performance. See section Optimizing cluster files and copy number models for additional details. * Database revision reflecting [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.3/reference/pgx-allele-definitions-and-pgx-guidelines) updates. * Standardization of star allele JSON output file * Renamed databaseSources to phenotypeDatabaseSources and starAlleleDatabaseSources * Renamed Phenotype to PhenotypeDatabaseAnnotation * Combined missingVariants and allMissingVariants to missingVariantSites * JSONized supportingVariants and missingVariants at the gene and candidate solution allele levels * Removed redundant info in the Alleles fields * Updated VCF tabix indexing, improving performance and disk usage for SNV VCF. ## **KNOWN ISSUES** * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report * Occasional star-allele solution score discorcordance between Linux and Windows OS with concordant solution ranking. * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To workaround the issue, user should restart star allele calling or run it on a machine with more memory. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. * Star allele annotation can fail mid-analysis in rare circumstances when a particular allele is unknown (e.g. for CYP2E1). The observed cases have all been mis-calls for CYP2E1 due to cluster drift. See [Optimizing cluster files](/dragen-array-v1.3/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for more details. ## **KNOWN LIMITATIONS** * Star allele calling does not support novel alleles but those defined in the PharmVar and PharmGKB databases. * CYP2D6 non-\*36 star alleles with exon 9 conversion, such as \*83, are reported as \*36 with \*83 as an underlying allele. * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * DRAGEN Array were only validated and intended to be used for commercial PGx beadchips with specified manifests (see table above). PGx star allele annotation is not backwards compatable with v1.0 manifest version, e.g., GDA\_PGx-8v1-0\_20042614\_E2 is supported in DRAGEN Array v1.0, GDA\_PGx-8v1-0\_20042614\_G2 is supported in DRAGEN Array v1.1. * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. # DRAGEN Array Methylation QC Cloud v1.0.1 Release Notes ## RELEASE DATE February 2026 ## RELEASE HIGHLIGHTS Hot-fix release patching known issue from v1.0 where analysis could fail for the 80-100 sample size range when using large (>900K probes) arrays. Large arrays are now supported in 80-100 sample size range as well. ## KNOWN LIMITATIONS * Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550 * Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips # DRAGEN Array v1.0.0 Release Notes ## **RELEASE DATE** December 2023 ## **RELEASE HIGHLIGHTS** * Improved star allele calling accuracy for Global Diversity Array with enhanced PGx (GDA-ePGx) BeadChips. * Reports star allele calls with quality scores for greater transparency and confidence. * Provides missing variant reporting to improve data quality. ## **NEW FEATURES IN DETAIL** * Star Allele Calling * Star allele calling for genes listed in [PGx Star Allele Coverage](/dragen-array-v1.3/reference/pgx-star-allele-coverage) * For in-silico datasets, call rate ≥99%, diplotyping accuracy ≥ 90% * Includes reporting of the hybrid star alleles and allelic specific copy number * Provides quality score that estimates confidence in the star allele call as an additional quality metric * Star allele call rate increased through more robust error tolerance and missing data tolerance * Supporting variants and missing variants are listed and can be further reviewed * Quality score indicates confidence in result considering the missing data * Reports alternative ranked PGx star allele solutions * Allows an alternative to be investigated which may be desirable for samples with low confidence calls * Provides quality score (negative log likelihood) for alternative solutions * Function annotations for PGx genes listed in section [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.3/reference/pgx-allele-definitions-and-pgx-guidelines) * Metabolizer and function annotations are supported for two sets of guidelines from CPIC and DPWG respectively * Activity scores are provided for CYP2C9, CYP2D6, and DPYD * CNV VCF * CNV coverage for genes listed in PGx CNVs Coverage * Compressed and indexed files for size reduction and faster reading * Updated VCF header description to indicate copy number of 5 may be reported by the software * Revised filter field delimiter to comply with VCF 4.3 specification which allows VCF parsing software to parse the file successfully * Genotyping VCF * Compressed and indexed files for size reduction and faster reading ## **KNOWN ISSUES** * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * For gtc-to-vcf, multi-allelic variants designed with multiple assays might not always collapse into one variant correctly and be reported as two separate variants instead. Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * Manifest names greater than 80 characters will cause failure when converting IDATs to GTCs. * Symbolic links for VCFs are not supported as the inputs to the “star-allele call” subcommand. * The local Linux CLI and Cloud offering do not sort the star\_alleles.csv and various fields in the metabolizer\_status.json. The local Windows CLI does. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. * GTC files do not support non-ASCII characters. This is especially problematic when running DRAGEN Array local if operating system locale settings are not English-based (e.g., en-US) as internal datetime fields could write non-ASCII characters. This will result in the following error: ``` fail: ArrayAnalysis.CLI.App[0] [07:17:07 6620]: System.IO.EndOfStreamException: Unable to read beyond the end of the stream. at System.IO.BinaryReader.ReadString() at ArrayAnalysis.Core.GtcFileLoader..ctor(String filePath) in /src/ArrayAnalysis.Core/GtcFileLoader.cs:line 161 at ArrayAnalysis.Services.GtcFactory.CreateGtcFromSample(Sample sample, Boolean log) at ArrayAnalysis.Services.GtcToVcfService.Run(GtcToVcfInput input) at ArrayAnalysis.CLI.App.RunCliServiceAndReturnExitCode(BaseOptions opts) in /src/ArrayAnalysis.CLI/App.cs:line 110 ``` There is a workaround to disable globalization and produce valid GTC files: 1. Locate the `dragena.runtimeconfig.json` file inside the installation directory of DRAGEN Array (i.e., where the .zip or .tar.gz file was downloaded and unzipped). 2. Add the key `System.Globalization.Invariant` to that file and set it's value to `true`. (i.e., step #2 here: ) 3. Re-generate the GTC using the `genotype call` subcommand. ## **KNOWN LIMITATIONS** * PGx CNV calling and star allele calling and annotation were only validated and intended to be used with GDA\_PGx\_E2 product files. * Using subcommands “unsquash-duplicates” and “filter loci” during gtc-to-vcf conversion should not be used when star allele calling is desired. * Only CPIC guidelines are available for star allele annotation (metabolizer status calling) for the cloud offering. For local, CPIC and DPWG are available. # DRAGEN Array Genotyping Cloud v1.0.0 Release Notes ## **RELEASE DATE** March 2024 ## **RELEASE HIGHLIGHTS** * Ability to genotype and produce related reports for human and non-human arrays in the cloud. * Configureable interfaces in Basespace that allows for flexibility and easy kick off. ## **NEW FEATURES IN DETAIL** * [SNV VCF File](/dragen-array-v1.3/product-guides/output-files#snv_vcf_file) * [Final Report](/dragen-array-v1.3/product-guides/output-files#final_report) * [Locus Summary](/dragen-array-v1.3/product-guides/output-files#locus_summary) ## **KNOWN ISSUES** * Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report ## **KNOWN LIMITATIONS** * Genotyping only works on diploid organisms at this time. Polyploid genotyping is not currently supported. # DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes ## RELEASE DATE May 2024 ## RELEASE HIGHLIGHTS * Adjustable thresholds to determine pass/fail status * Data summary plots for a quick visual check of each analysis batch * Determining detection p-value, beta-values, and m-values from each methylation sample * Deployment on BaseSpace™ Sequence Hub user interface for easy analysis kickoff ## NEW FEATURES IN DETAIL * Adjustable thresholds for 21 built in controls, p-value detection, proportion probes passing, and offset correction within BaseSpace Sequence Hub to customize for user’s study needs * Thresholds are used to assign pass (1) or fail (0) status to each sample * Failed metrics can be highlighted for easy viewing * Pinpoint areas of failure including bisulfite conversion, staining, hybridization, etc. to identify assay steps in need of troubleshooting * Quantitative values for each control removing ambiguity with manual interpretation * Data summary plots with information on passing p-value detection and principal component analysis of beta values * Provides detection p-value, beta-values and m-values for each CG site per sample to use in downstream analysis ## KNOWN ISSUES * Analysis may fail for the 80-100 sample size range when using large (>900K probes) arrays. If encountering this issue users are recommended to increase sample size as a workaround. The issue does not affect sample sizes strictly greater than 100. ## KNOWN LIMITATIONS * Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550 * Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips # PGx CNV Coverage Copy number variation can be detected for genes and regions listed below. The chromosome locations are GRCh38 based. | Gene | Region Name | Chromosome | Start | End | | ------- | --------------- | ------------------- | --------- | --------- | | GSTM1 | GSTM1 | 1 | 109687842 | 109693526 | | UGT2B17 | UGT2B17 | 4 | 68537222 | 68568499 | | CYP2E1 | CYP2E1 | 10 | 133527374 | 133539096 | | SULT1A1 | SULT1A1 | 16 | 28603587 | 28613544 | | CYP2A6 | CYP2A6.intron.7 | 19 | 40844791 | 40845293 | | CYP2A6 | CYP2A6.exon.1 | 19 | 40850267 | 40850414 | | CYP2D6 | CYP2D6.exon.9 | 22 | 42126498 | 42126752 | | CYP2D6 | CYP2D6.intron.2 | 22 | 42129188 | 42129734 | | CYP2D6 | CYP2D6.p5 | 22 | 42130886 | 42131379 | | GSTT1 | GSTT1 | 22\_KI270879v1\_alt | 270316 | 278477 | # PGx Allele Definitions and PGx Guidelines ## PGx Allele Definitions and PGx Guidelines DRAGEN Array star allele calling leverages the star allele definitions provided by PharmVar and PharmGKB. DRAGEN Array star allele phenotype annotation, using the “star-allele annotate” command, is achieved through direct lookup into public PGx guidelines CPIC or DPWG, which is selected by the user when running DRAGEN Array. See table below for details of the data sources. | Data Source | Version | URL | | ----------------------------------------------------------------------- | ------------------- | -------------------------------------------------------------------------------------- | | PharmVar | 6.1 | | | PharmGKB | Snapshot-2024.05.16 | | | UGT Alleles Nomenclature | 2010.12.21 | | | Human Cytochrome P450 (CYP) Allele Nomenclature Database Legacy Content | July 2024 | | | CPIC guidelines | 1.38.0 |

| | DPWG guidelines | June 2023 | | DRAGEN Array “star-allele annotate” command provides both metabolizer status and activity score annotations for genes covered by the CPIC and DPWG guidelines. Specifically, CPIC metabolizer/phenotype annotations are supported for CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, MT-RNR1, NUDT15, RYR1, SLCO1B1, TPMT, UGT1A1, CFTR, IFNL3/IFNL4 and VKORC1, among them activity scores are supported for CYP2C9, CYP2D6, and DPYD. DPWG metabolizer/phenotype annotations are supported for CYP1A2, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1 and F5, among them activity scores are supported for CYP2D6 and DPYD. ## Extended Multi-allelic variants based on the designs in the supported PGx products * DRAGEN Array PGx extends any single allele variant definitions obtained from PharmVar or PharmGKB that have multiple alleles in Illumina's product files to include all alleles of the Multi Allelic Variant (MAV). The table below shows the MAVs that were extended in the DRAGEN Array Database to cover all alleles for that MAV that are in the product files. Allele Name describes the allele that was added to the database. | Gene Symbol | Allele Name | Hgvs | | ----------------- | --------------------------- | ---------------------------- | | CACNA1S.rs1800559 | rs1800559.C>A | NC\_000001.11:g.201060815C>A | | CFTR.rs113993958 | rs113993958.G>A | NC\_000007.14:g.117530953G>A | | CFTR.rs113993958 | rs113993958.G>T | NC\_000007.14:g.117530953G>T | | CFTR.rs11971167 | rs11971167.G>T | NC\_000007.14:g.117642528G>T | | CFTR.rs121908755 | rs121908755.G>T | NC\_000007.14:g.117587800G>T | | CFTR.rs121909005 | rs121909005.T>C | NC\_000007.14:g.117587801T>C | | CFTR.rs121909020 | rs121909020.G>C | NC\_000007.14:g.117611640G>C | | CFTR.rs150212784 | rs150212784.T>C | NC\_000007.14:g.117611595T>C | | CFTR.rs193922525 | rs193922525.G>C | NC\_000007.14:g.117664770G>C | | CFTR.rs267606723 | rs267606723.G>T | NC\_000007.14:g.117642451G>T | | CFTR.rs397508288 | rs397508288.A>C | NC\_000007.14:g.117590409A>C | | CFTR.rs397508759 | rs397508759.G>T | NC\_000007.14:g.117534363G>T | | CFTR.rs74551128 | rs74551128.C>T | NC\_000007.14:g.117548795C>T | | CFTR.rs75039782 | rs75039782.C>G | NC\_000007.14:g.117639961C>G | | CFTR.rs77834169 | rs77834169.C>A | NC\_000007.14:g.117530974C>A | | CFTR.rs77834169 | rs77834169.C>G | NC\_000007.14:g.117530974C>G | | CFTR.rs77932196 | rs77932196.G>C | NC\_000007.14:g.117540270G>C | | CFTR.rs77932196 | rs77932196.G>T | NC\_000007.14:g.117540270G>T | | CFTR.rs78655421 | rs78655421.G>C | NC\_000007.14:g.117530975G>C | | CFTR.rs78655421 | rs78655421.G>T | NC\_000007.14:g.117530975G>T | | COMT.rs13306278 | rs13306278.C>G | NC\_000022.11:g.19941504C>G | | DPYD.rs114096998 | rs114096998.2.G>C | NC\_000001.11:g.97078987G>C | | DPYD.rs140602333 | rs140602333.G>T | NC\_000001.11:g.97573919G>T | | DPYD.rs142619737 | rs142619737.C>G | NC\_000001.11:g.97515851C>G | | DPYD.rs143154602 | rs143154602.G>T | NC\_000001.11:g.97593289G>T | | DPYD.rs145548112 | rs145548112.C>A | NC\_000001.11:g.97306195C>A | | DPYD.rs190951787 | rs190951787.G>T | NC\_000001.11:g.97515889G>T | | DPYD.rs200687447 | rs200687447.2.C>A | NC\_000001.11:g.97193209C>A | | DPYD.rs3918289 | rs3918289.G>A | NC\_000001.11:g.97450059G>A | | DPYD.rs3918290 | rs3918290.C>G | NC\_000001.11:g.97450058C>G | | DPYD.rs6670886 | rs6670886.C>A | NC\_000001.11:g.97699506C>A | | DPYD.rs72549304 | rs72549304.G>C | NC\_000001.11:g.97549609G>C | | DPYD.rs72549304 | rs72549304.G>T | NC\_000001.11:g.97549609G>T | | DPYD.rs748620513 | rs748620513.C>A | NC\_000001.11:g.97573799C>A | | DPYD.rs748639205 | rs748639205.A>G | NC\_000001.11:g.97082415A>G | | DPYD.rs760663364 | rs760663364.G>C | NC\_000001.11:g.97515928G>C | | DPYD.rs777425216 | rs777425216.C>A | NC\_000001.11:g.97515815C>A | | RYR1.38499667G>A | NC\_000019.10:g.38499667G>T | NC\_000019.10:g.38499667G>T | | RYR1.rs118192116 | rs118192116.C>T | NC\_000019.10:g.38451850C>T | | RYR1.rs118192151 | rs118192151.G>C | NC\_000019.10:g.38584974G>C | | RYR1.rs118204423 | rs118204423.G>A | NC\_000019.10:g.38457539G>A | | RYR1.rs142474192 | rs142474192.G>T | NC\_000019.10:g.38443790G>T | | RYR1.rs143988412 | rs143988412.A>G | NC\_000019.10:g.38580066A>G | | RYR1.rs1801086 | rs1801086.G>T | NC\_000019.10:g.38446710G>T | | RYR1.rs186983396 | rs186983396.C>G | NC\_000019.10:g.38442434C>G | | RYR1.rs193922762 | rs193922762.C>A | NC\_000019.10:g.38448673C>A | | RYR1.rs193922767 | rs193922767.G>A | NC\_000019.10:g.38452996G>A | | RYR1.rs193922772 | rs193922772.G>A | NC\_000019.10:g.38457546G>A | | RYR1.rs193922826 | rs193922826.C>G | NC\_000019.10:g.38504319C>G | | RYR1.rs193922838 | rs193922838.G>A | NC\_000019.10:g.38529036G>A | | RYR1.rs193922842 | rs193922842.C>T | NC\_000019.10:g.38543821C>T | | RYR1.rs370634440 | rs370634440.G>T | NC\_000019.10:g.38463499G>T | ### Exceptions to Star Allele Definitions #### G6PD With the changes of reference genomes, the definition for a star allele sometimes need to be updated accordingly. `Mediterranean Haplotype` and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` are defined by two variants rs5030868 and rs2230037. In genome build GRCh37, `Mediterranean Haplotype` is defined by rs2230037 G>A and rs5030868 G>A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs5030868 G>A, with rs2230037 reference allele G. In genome build GRCh38, `Mediterranean Haplotype` is defined by rs5030868 G>A, with rs2230037 reference allele A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs2230037 A>G and rs5030868 G>A. Variant rs2230037 is ignored in all other G6PD alleles except in the two Mediterranean alleles. #### \*0 Star Allele Definition A \*0 allele refers to a full gene deletion of the analyzed gene, if there is no existing star allele name for the deletion allele from source databases, such as PharmVar and PharmGKB. # PGx Star Allele Coverage ## Theoretical Coverage The PGx genes and star/variant alleles listed below can be detected by DRAGEN Array v1.1 if available on the microarray. PGx coverage for specific PGx microarrays can be found here: [PGx Star Allele Coverage for Specific PGx Products](https://help.dragenarray.illumina.com/reference/pgx-star-allele-coverage#pgx-star-allele-coverage-for-specific-pgx-products). Known and novel star alleles not in the below list will not be reported. Star allele definitions are sourced from PharmVar and PharmGKB. Among the PGx genes, HLA-A, HLA-B, and IFNL3/IFNL4 alleles are covered through tagging variants, specifically HLA-A,\*31:01 (rs1061235.A>T); HLA-B,\*15:02 (rs144012689.T>A); HLA-B,\*57:01 (rs2395029.T>G); HLA-B,\*58:01 (rs9263726.G>A); IFNL3/4, rs12979860 variant (T). Reliability of the tagging SNPs varies depending on the population. Additional information on PGx gene types, variant type versus star allele type, can be found here: [Introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Gene | PGx Alleles | | ------- | 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| | ABCG2 | Reference;rs2231142.G>T | | ADH1B | Reference;rs1229984.T>C;rs1229984.T>G;rs1229985.A>G;rs17033.T>C;rs1789891.C>A;rs2018417.C>A;rs2018417.C>T;rs2066702.G>A;rs75967634.C>T | | ALDH2 | Reference;rs671.G>A | | ANK3 | Reference;rs143414470.T>C | | ANKK1 | Reference;rs1800497.G>A;rs2587550.G>A;rs2734849.A>C;rs2734849.A>G;rs4938013.A>C;rs4938013.A>G;rs4938013.A>T;rs7118900.G>A;rs7118900.G>C | | APOE | E2;E3;E4 | | ATM | Reference;rs11212570.G>A;rs11212570.G>T;rs11212617.C>A;rs1801516.G>A;rs620815.T>A;rs620815.T>C | | BDNF | Reference;rs10835210.C>A;rs10835210.C>G;rs11030101.A>G;rs11030101.A>T;rs11030104.A>G;rs11030118.G>A;rs11030119.G>A;rs11030119.G>T;rs1491850.T>C;rs16917234.T>A;rs16917234.T>C;rs1967554.A>C;rs2030324.A>G;rs61888800.G>T;rs6265.C>T;rs7103411.C>T;rs7124442.C>G;rs7124442.C>T;rs7127507.T>C;rs7934165.G>A;rs962369.T>C;rs988748.C>G | | CACNA1C | Reference;rs1006737.G>A;rs1034936.C>A;rs1034936.C>G;rs1034936.C>T;rs1051375.G>A;rs1051375.G>C;rs10774053.A>C;rs10774053.A>G;rs10848635.T>A;rs10848635.T>C;rs11062040.C>T;rs12813888.A>C;rs12813888.A>T;rs2041135.T>C;rs215976.C>G;rs215976.C>T;rs215994.T>C;rs216008.C>T;rs216013.A>G;rs2238032.T>C;rs2238032.T>G;rs2238087.C>G;rs2238087.C>T;rs2239050.G>A;rs2239050.G>C;rs2239128.T>A;rs2239128.T>C;rs2283271.T>A;rs723672.C>A;rs723672.C>G;rs723672.C>T;rs7295250.T>C;rs7316246.G>A;rs7316246.G>C;rs758723.T>A;rs758723.T>C | | CACNA1S | Reference;rs1800559.C>A;rs1800559.C>T;rs772226819.G>A | | CFTR | Reference;rs113993958.G>A;rs113993958.G>C;rs113993958.G>T;rs115545701.C>T;rs11971167.G>A;rs11971167.G>T;rs121908752.T>G;rs121908753.G>A;rs121908755.G>A;rs121908755.G>T;rs121908757.A>C;rs121909005.T>C;rs121909005.T>G;rs121909013.G>A;rs121909020.G>A;rs121909020.G>C;rs121909041.T>C;rs141033578.C>T;rs150212784.T>C;rs150212784.T>G;rs186045772.T>A;rs193922525.G>A;rs193922525.G>C;rs200321110.G>A;rs202179988.C>T;rs267606723.G>A;rs267606723.G>T;rs368505753.C>T;rs397508256.G>A;rs397508288.A>C;rs397508288.A>G;rs397508387.G>T;rs397508442.C>T;rs397508513.A>C;rs397508537.C>A;rs397508759.G>A;rs397508759.G>T;rs397508761.A>G;rs74503330.G>A;rs74551128.C>A;rs74551128.C>T;rs75039782.C>G;rs75039782.C>T;rs75527207.G>A;rs75541969.G>C;rs76151804.A>G;rs77834169.C>A;rs77834169.C>G;rs77834169.C>T;rs77932196.G>A;rs77932196.G>C;rs77932196.G>T;rs78655421.G>A;rs78655421.G>C;rs78655421.G>T;rs78769542.G>A;rs80224560.G>A;rs80282562.G>A | | COMT | Reference;rs13306278.C>T;rs165599.G>A;rs165599.G>C;rs165722.C>T;rs165728.C>A;rs165728.C>G;rs165728.C>T;rs165774.G>A;rs174675.T>C;rs174696.C>A;rs174696.C>T;rs174699.C>T;rs2020917.C>T;rs2075507.G>A;rs2075507.G>C;rs2075507.G>T;rs2239393.A>G;rs4633.C>T;rs4646312.T>C;rs4646316.C>G;rs4646316.C>T;rs4680.G>A;rs4818.C>G;rs4818.C>T;rs5746849.A>G;rs5993882.T>C;rs5993882.T>G;rs5993883.T>G;rs6267.G>A;rs6267.G>T;rs6269.A>G;rs6269.A>T;rs7287550.T>C;rs7287550.T>G;rs737865.A>G;rs737866.T>A;rs737866.T>C;rs740603.A>G;rs9332377.C>A;rs9332377.C>T;rs933271.T>A;rs933271.T>C;rs9606186.C>A;rs9606186.C>G;rs9606186.C>T | | CYP1A2 | \*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1A;\*1B;\*1C;\*1D;\*1E;\*1F;\*1G;\*1J;\*1K;\*1L;\*1M;\*1N;\*1P;\*1Q;\*1R;\*1S;\*1T;\*1U;\*1V;\*2;\*20;\*21;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2A6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*31;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*6;\*7;\*8;\*9 | | CYP2B6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*3;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*6;\*7;\*8;\*9 | | CYP2C19 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*22;\*23;\*24;\*25;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*38;\*39;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C8 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C9 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*57;\*58;\*59;\*6;\*60;\*61;\*62;\*63;\*64;\*65;\*66;\*67;\*68;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*76;\*77;\*78;\*79;\*8;\*80;\*81;\*82;\*83;\*84;\*85;\*9 | | CYP2D6 | \*1;\*1-\*90;\*10;\*100;\*101;\*102;\*103;\*104;\*105;\*106;\*107;\*108;\*109;\*10x2;\*11;\*110;\*111;\*112;\*113;\*114;\*115;\*116;\*117;\*118;\*119;\*12;\*120;\*121;\*122;\*123;\*124;\*125;\*126;\*127;\*128;\*129;\*13;\*13-\*1;\*13-\*2;\*13-\*4-\*68;\*130;\*131;\*132;\*133;\*134;\*135;\*136;\*137;\*138;\*139;\*13x2-\*1;\*13x2-\*2;\*14;\*140;\*141;\*142;\*143;\*144;\*145;\*146;\*147;\*148;\*149;\*15;\*150;\*151;\*152;\*153;\*154;\*155;\*156;\*157;\*158;\*159;\*160;\*161;\*162;\*163;\*164;\*165;\*166;\*167;\*168;\*169;\*17;\*170;\*171;\*172;\*17x2;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*29x2;\*2x2;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*35x2;\*36;\*36;\*36-\*10;\*36-\*10x2;\*36x2-\*10;\*36x3-\*10;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*43x2;\*44;\*45;\*46;\*47;\*48;\*49;\*4M;\*4N-\*4;\*4x2;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*58;\*59;\*6;\*60;\*62;\*64;\*65;\*68;\*68-\*4;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*8;\*81;\*82;\*83;\*84;\*85;\*86;\*87;\*88;\*89;\*9;\*90;\*91;\*92;\*93;\*94;\*95;\*96;\*97;\*98;\*99;\*9x2 | | CYP2E1 | \*1A;\*1B;\*2;\*3;\*4;\*5A;\*5B;\*6;\*7A;\*7B;\*7C | | CYP3A4 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*5;\*6;\*7;\*8;\*9 | | CYP3A5 | \*1;\*3;\*6;\*7;\*8;\*9 | | CYP4F2 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | DPYD | Reference;rs111858276.T>C;rs112766203.1.G>A;rs112766203.2.G>C;rs114096998.1.G>T;rs114096998.2.G>A;rs114096998.2.G>C;rs115232898.T>C;rs116364703.T>A;rs1180771326.T>C;rs137878450.C>A;rs137999090.C>T;rs138391898.C>T;rs138545885.C>A;rs138616379.C>T;rs139459586.A>C;rs139834141.C>T;rs140039091.C>G;rs140114515.C>T;rs140602333.G>A;rs140602333.G>T;rs140989814.C>G;rs141044036.T>C;rs141439344.C>T;rs141462178.T>C;rs141726921.C>T;rs142512579.C>T;rs142619737.C>G;rs142619737.C>T;rs143154602.G>A;rs143154602.G>T;rs143815742.1.C>A;rs143815742.2.C>T;rs143879757.1.G>T;rs143879757.2.G>A;rs143986398.G>C;rs144395748.1.G>C;rs144395748.2.G>T;rs144935781.T>C;rs145112791.G>A;rs145529148.T>C;rs145548112.C>A;rs145548112.C>T;rs145773863.C>T;rs146356975.T>C;rs146529561.G>A;rs147545709.G>A;rs147601618.A>G;rs148799944.C>G;rs148994843.C>T;rs150036960.G>C;rs150385342.1.C>T;rs150385342.2.C>A;rs150437414.A>G;rs151074666.C>T;rs17376848.A>G;rs1801158.C>T;rs1801159.T>C;rs1801160.C>T;rs1801265.A>G;rs1801266.G>A;rs1801267.C>T;rs1801268.C>A;rs183105782.A>G;rs183385770.C>T;rs186169810.A>C;rs187713395.A>G;rs188052243.T>C;rs190577302.G>C;rs190951787.G>C;rs190951787.G>T;rs199549923.G>T;rs199634007.G>T;rs199646142.C>T;rs199777072.C>T;rs200064537.A>T;rs200296941.T>C;rs200562975.T>C;rs200643089.A>C;rs200687447.1.C>T;rs200687447.2.C>A;rs200687447.2.C>G;rs200693895.A>G;rs200709381.T>G;rs201018345.C>T;rs201035051.T>G;rs201268750.G>T;rs201433243.C>T;rs201615754.1.C>A;rs201615754.2.C>T;rs201648613.C>G;rs201785202.G>A;rs202144771.G>A;rs202212118.C>A;rs2297595.T>C;rs267598785.G>A;rs267598786.C>T;rs267598789.G>A;rs367619008.T>C;rs368146607.T>G;rs368152149.T>C;rs368327291.C>G;rs368519011.T>C;rs368970772.G>T;rs369103276.A>G;rs369575517.G>A;rs370569731.1.C>G;rs370569731.2.C>T;rs370615432.C>A;rs370707404.A>G;rs371258350.C>T;rs371313778.C>T;rs371587702.1.G>A;rs371587702.2.G>C;rs371792178.1.G>A;rs371792178.2.G>C;rs372058915.T>C;rs372307932.A>T;rs372909322.T>C;rs374527058.A>G;rs374531732.C>T;rs374825099.1.G>T;rs374825099.2.G>C;rs374827081.G>C;rs375436137.C>T;rs375990187.A>G;rs376073289.1.C>T;rs376073289.2.C>A;rs376128878.G>T;rs376273539.G>C;rs377143350.C>T;rs377169736.C>G;rs3918289.G>A;rs3918289.G>C;rs3918290.C>G;rs3918290.C>T;rs45589337.T>C;rs527580106.T>C;rs528152707.C>A;rs528430685.G>A;rs528768620.C>T;rs529019871.T>C;rs532341730.A>T;rs536577604.T>C;rs538336580.T>A;rs538703919.G>A;rs547099198.G>A;rs548783838.C>T;rs55674432.C>A;rs556933127.A>C;rs557220418.G>A;rs558354142.G>A;rs55886062.1.A>C;rs55886062.2.A>T;rs559427764.C>A;rs55971861.T>G;rs56005131.G>T;rs56038477.C>T;rs568169006.T>C;rs568367673.C>A;rs569661196.A>G;rs570122671.G>A;rs571114616.A>G;rs573299212.C>T;rs575763449.G>A;rs575853463.C>T;rs576409484.T>A;rs57918000.G>A;rs59086055.G>A;rs60139309.T>C;rs60511679.A>C;rs61622928.C>T;rs61757362.G>A;rs6670886.C>A;rs6670886.C>T;rs672601273.1.C>A;rs672601273.2.C>T;rs672601275.T>G;rs672601276.C>A;rs672601282.G>A;rs672601284.C>T;rs672601285.T>C;rs672601287.T>G;rs672601288.C>A;rs67376798.T>A;rs72547601.T>C;rs72547602.T>A;rs72549303.del;rs72549304.G>A;rs72549304.G>C;rs72549304.G>T;rs72549305.T>C;rs72549306.1.C>A;rs72549306.2.C>T;rs72549307.T>C;rs72549308.T>G;rs72549309.ATGA\[1];rs72549310.G>A;rs72975710.1.G>A;rs72975710.2.G>C;rs745512069.G>A;rs745704371.G>C;rs745833535.T>C;rs745911874.C>T;rs745982505.1.T>C;rs745982505.2.T>A;rs746115989.C>T;rs746329786.T>A;rs746777181.C>T;rs747132274.C>G;rs747161261.C>T;rs747627716.A>C;rs747633945.C>T;rs747858350.G>A;rs747872037.C>A;rs748214188.A>T;rs748235192.1.T>A;rs748235192.2.T>C;rs748266854.G>A;rs748320430.A>C;rs748620513.C>A;rs748620513.C>G;rs748639205.A>C;rs748639205.A>G;rs748853941.T>C;rs748958293.G>A;rs748974194.G>A;rs749157068.C>A;rs749269410.C>T;rs749354734.A>T;rs749586100.T>A;rs749699298.A>C;rs749982106.G>A;rs750147471.T>C;rs75017182.G>C;rs750224169.G>A;rs750423752.A>C;rs750687600.C>T;rs750721736.A>T;rs751049055.C>A;rs751104498.T>C;rs751113340.G>A;rs751190912.G>A;rs751340819.A>G;rs751374989.T>A;rs751399062.G>T;rs751841116.1.C>A;rs751841116.2.C>T;rs751848058.T>A;rs752020412.C>T;rs752228747.G>A;rs752388408.C>T;rs752518145.C>A;rs752985272.C>A;rs753166888.C>G;rs753217888.G>C;rs753296078.C>G;rs753419296.C>G;rs753527420.C>G;rs753707032.G>A;rs753710779.G>A;rs753820482.T>C;rs753950237.G>A;rs754028972.A>G;rs754125729.1.G>A;rs754125729.2.G>T;rs754467630.G>A;rs754786483.T>C;rs755155824.C>A;rs755407188.T>G;rs755416212.C>T;rs755428442.C>G;rs755645831.A>C;rs755692084.T>G;rs755729055.T>C;rs756020314.G>C;rs756372042.A>G;rs756613407.T>C;rs756684474.T>C;rs756890859.T>C;rs756992995.C>T;rs757155354.T>C;rs757227327.C>T;rs757342874.C>T;rs757376267.C>A;rs757695236.C>T;rs757954074.C>T;rs757958938.T>C;rs757994597.G>A;rs758154803.A>G;rs758489611.C>T;rs758514990.C>T;rs758649719.C>T;rs758699471.T>C;rs759082282.C>A;rs759249769.G>T;rs759424419.A>T;rs759479759.T>C;rs759562628.T>G;rs759766897.T>C;rs759967863.A>G;rs760038956.C>T;rs760222167.T>C;rs760235888.C>T;rs760485592.G>A;rs760553268.G>C;rs760570391.A>G;rs760663364.G>A;rs760663364.G>C;rs761302217.T>C;rs761351410.G>A;rs761479700.G>C;rs761555670.T>C;rs761609256.T>G;rs762083671.T>A;rs762102298.A>C;rs762198241.G>A;rs762430779.G>T;rs762446803.A>C;rs762468894.G>C;rs762523739.T>A;rs762533012.C>T;rs762598766.T>C;rs762779297.T>C;rs762858106.C>T;rs762911226.T>A;rs763008163.T>G;rs763061658.A>G;rs763449831.C>T;rs763506271.T>C;rs763557204.A>G;rs763572567.T>G;rs763623595.A>C;rs763784786.G>C;rs763862486.C>T;rs763893877.T>C;rs763984510.G>C;rs764111543.C>T;rs764270260.G>A;rs764555085.A>G;rs764635955.G>T;rs764666241.C>A;rs764679468.A>C;rs764945792.C>T;rs765001324.C>T;rs765034707.C>A;rs765075551.T>C;rs765131182.G>A;rs765247038.G>A;rs765309287.G>T;rs765465250.T>C;rs765640386.C>A;rs765990958.G>A;rs766411970.A>C;rs766438205.T>C;rs766635900.C>T;rs766700777.C>G;rs766761199.T>G;rs766833304.G>C;rs766885021.A>C;rs767200577.T>C;rs767376585.C>G;rs767437717.G>T;rs767464878.C>A;rs767468952.C>T;rs767482279.A>G;rs767547827.G>C;rs767818267.C>T;rs767836989.T>C;rs767986711.T>G;rs768117152.T>C;rs768157853.G>C;rs768200107.T>G;rs768288280.T>C;rs768501828.T>C;rs768507975.A>T;rs768680499.G>T;rs768915005.C>T;rs769190350.T>A;rs769306962.C>T;rs769466648.1.T>G;rs769466648.2.T>C;rs769514867.G>T;rs769696395.T>C;rs769709846.T>C;rs769820114.C>T;rs769847078.T>C;rs769932607.G>A;rs770229152.T>A;rs770566506.A>G;rs770958862.G>A;rs771194906.A>G;rs771534236.T>C;rs771536388.C>T;rs771573678.T>A;rs771646887.C>T;rs771648776.T>C;rs771885007.A>G;rs771930534.1.A>T;rs771930534.2.A>G;rs772097379.G>A;rs772264512.G>A;rs772320654.T>C;rs772358811.C>G;rs772544099.G>T;rs772826416.A>G;rs772906420.C>T;rs773159364.C>G;rs773407491.T>C;rs773584401.C>A;rs773652644.T>C;rs773815814.1.C>A;rs773815814.2.C>T;rs773868825.C>T;rs773983635.A>T;rs774134971.T>C;rs774500505.A>T;rs774579695.1.C>T;rs774799003.G>A;rs774883578.A>C;rs775494607.G>A;rs775526810.C>A;rs775570841.G>C;rs775601164.G>A;rs775926386.G>C;rs776082092.C>T;rs776236081.C>T;rs776289153.C>T;rs776321529.G>C;rs776662759.T>G;rs776973423.C>T;rs776984091.T>C;rs777220476.1.C>T;rs777220476.2.C>A;rs777238016.T>C;rs777347164.C>T;rs777368221.A>C;rs777425216.C>A;rs777425216.C>T;rs777560627.G>A;rs777673186.G>C;rs777902288.T>A;rs778022685.C>T;rs778054451.C>T;rs778141885.T>C;rs778298325.C>T;rs778601245.C>T;rs778754188.A>G;rs778760295.C>G;rs778776264.T>C;rs778867644.T>C;rs778911905.A>C;rs779465366.A>G;rs779557503.G>A;rs779573574.T>A;rs779728902.A>T;rs779925747.T>G;rs779967271.T>C;rs780025995.G>A;rs780047918.T>C;rs780120302.T>C;rs78060119.C>A;rs780813130.C>T;rs780873985.T>C;rs780885126.T>C;rs781184141.T>C;rs80081766.C>T;rs866110709.C>T;rs866869468.C>A;rs867143119.C>A;rs867226255.C>T;rs867232786.C>T;rs867600987.C>T;rs868047175.C>T;rs868235016.C>T | | DRD2 | Reference;rs1076560.C>A;rs1076560.C>G;rs1076563.A>C;rs1079596.C>A;rs1079596.C>T;rs1079597.C>T;rs1079598.A>G;rs1079598.A>T;rs1110976.T>G;rs11214607.T>G;rs1124491.G>A;rs1124491.G>C;rs1124493.T>G;rs1125394.T>C;rs12364283.A>G;rs12574471.C>G;rs12574471.C>T;rs17601612.G>C;rs1799732.\_113475530insG;rs1799732.dup;rs1799978.T>C;rs1800497.G>A;rs1800498.G>A;rs1801028.G>C;rs2075652.G>A;rs2234689.G>C;rs2283265.C>A;rs2440390.T>C;rs2514218.C>T;rs2587548.G>A;rs2587548.G>C;rs2734833.G>A;rs2734841.A>C;rs2734841.A>G;rs2734841.A>T;rs2734842.G>C;rs4274224.G>A;rs4274224.G>C;rs4436578.C>G;rs4436578.C>T;rs4460839.C>G;rs4460839.C>T;rs4648317.G>A;rs4648318.T>A;rs4648318.T>C;rs4648318.T>G;rs4936274.A>G;rs4936274.A>T;rs6275.A>G;rs6277.G>A;rs6279.G>C;rs7122246.G>A;rs7131056.A>C;rs7131056.A>G;rs7131440.C>T | | F13A1 | Reference;rs5985.C>A;rs5985.C>T | | F2 | Reference;rs1799963.G>A;rs3136516.G>A;rs5896.C>G;rs5896.C>T | | F5 | Reference;rs6025.C>T | | FKBP5 | Reference;rs1360780.T>A;rs1360780.T>C;rs17614642.T>C;rs3800373.C>A;rs3800373.C>G;rs4713916.A>C;rs4713916.A>G;rs4713916.A>T;rs73748206.C>T;rs9380524.C>A;rs9380524.C>T | | G6PD | 202G>A\_376A>G\_1264C>G;A;A- 202A\_376G;A- 680T\_376G;A- 968C\_376G;Aachen;Abeno;Acrokorinthos;Alhambra;Amazonia;Amiens;Amsterdam;Anadia;Ananindeua;Andalus;Arakawa;Asahi;Asahikawa;Aures;Aveiro;B (reference);Bajo Maumere;Bangkok;Bangkok Noi;Bao Loc;Bari;Belem;Beverly Hills, Genova, Iwate, Niigata, Yamaguchi;Brighton;Buenos Aires;Cairo;Calvo Mackenna;Campinas;Canton, Taiwan-Hakka, Gifu-like, Agrigento-like;Cassano;Chatham;Chikugo;Chinese-1;Chinese-5;Cincinnati;Cleveland Corum;Clinic;Coimbra Shunde;Cosenza;Costanzo;Covao do Lobo;Crispim;Dagua;Durham;Farroupilha;Figuera da Foz;Flores;Fukaya;Fushan;Gaohe;Georgia;Gidra;Gond;Guadalajara;Guangzhou;Haikou;Hammersmith;Harilaou;Harima;Hartford;Hechi;Hermoupolis;Honiara;Ierapetra;Ilesha;Insuli;Iowa, Walter Reed, Springfield;Iwatsuki;Japan, Shinagawa;Kaiping, Anant, Dhon, Sapporo-like, Wosera;Kalyan-Kerala, Jamnaga, Rohini;Kambos;Kamiube, Keelung;Kamogawa;Kawasaki;Kozukata;Krakow;La Jolla;Lages;Lagosanto;Laibin;Lille;Liuzhou;Loma Linda;Ludhiana;Lynwood;Madrid;Mahidol;Malaga;Manhattan;Mediterranean Haplotype;Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham;Metaponto;Mexico City;Miaoli;Minnesota, Marion, Gastonia, LeJeune;Mira d'Aire;Mizushima;Montalbano;Montpellier;Mt Sinai;Munich;Murcia Oristano;Musashino;Namouru;Nankang;Nanning;Naone;Nara;Nashville, Anaheim, Portici;Neapolis;Nice;Nilgiri;No name;North Dallas;Olomouc;Omiya;Orissa;Osaka;Palestrina;Papua;Partenope;Pawnee;Pedoplis-Ckaro;Piotrkow;Plymouth;Praha;Puerto Limon;Quing Yan;Radlowo;Rehevot;Rignano;Riley;Riverside;Roubaix;S. Antioco;Salerno Pyrgos;Santa Maria;Santiago;Santiago de Cuba, Morioka;Sao Borja;Seattle, Lodi, Modena, Ferrara II, Athens-like;Seoul;Serres;Shenzen;Shinshu;Sibari;Sierra Leone;Sinnai;Songklanagarind;Split;Stonybrook;Sugao;Sumare;Sunderland;Surabaya;Suwalki;Swansea;Taipei, Chinese-3;Telti, Kobe;Tenri;Tokyo, Fukushima;Toledo;Tomah;Tondela;Torun;Tsukui;Ube Konan;Union,Maewo, Chinese-2, Kalo;Urayasu;Utrecht;Valladolid;Vancouver;Vanua Lava;Viangchan, Jammu;Villeurbanne;Volendam;Wayne;West Virginia;Wexham;Wisconsin;Yunan | | GRIK1 | Reference;rs2832407.C>A;rs2832407.C>T | | GRIK4 | Reference;rs12800734.G>A;rs1954787.T>C | | GRIN2B | Reference;rs1019385.C>A;rs1072388.G>A;rs1072388.G>C;rs1806191.G>A;rs1806191.G>T;rs1806201.G>A;rs2058878.T>A;rs2058878.T>C;rs2160733.A>C;rs2160734.C>G;rs2160734.C>T;rs2284411.C>T;rs890.A>C;rs890.A>G | | HLA-A | \*31:01;Reference | | HLA-B | \*15:02;\*57:01;\*58:01;Reference | | HMGCR | Reference;rs10474433.T>C;rs10474433.T>G;rs12654264.A>T;rs17238540.T>G;rs17244841.A>T;rs17671591.C>T;rs3846662.A>G;rs3846662.A>T | | HTR2A | Reference;rs17288723.T>C;rs17289304.T>C;rs17289304.T>G;rs1928040.G>A;rs1928040.G>C;rs2274639.C>G;rs2274639.C>T;rs2770296.C>G;rs2770296.C>T;rs3742278.A>G;rs3803189.T>G;rs6305.G>A;rs6311.C>A;rs6311.C>T;rs6312.C>A;rs6312.C>G;rs6312.C>T;rs6313.G>A;rs6313.G>C;rs6314.G>A;rs659734.G>A;rs659734.G>C;rs659734.G>T;rs7997012.A>C;rs7997012.A>G;rs7997012.A>T;rs9316233.C>A;rs9316233.C>G;rs9316233.C>T;rs9567746.A>C;rs9567746.A>G | | HTR2C | Reference;rs1023574.C>G;rs1023574.C>T;rs12836771.A>G;rs1414334.C>G;rs2497538.A>C;rs3813928.G>A;rs3813929.C>G;rs3813929.C>T;rs498207.G>A;rs518147.C>A;rs518147.C>G;rs539748.C>T;rs6318.C>G;rs6318.C>T;rs9698290.T>A;rs9698290.T>C | | IFNL3/4 | Reference;rs12979860 variant (T) | | IL6 | Reference;rs10242595.G>A;rs10242595.G>C;rs10242595.G>T;rs10499563.T>C;rs1524107.C>G;rs1524107.C>T;rs1800795.C>G;rs1800795.C>T;rs1800796.G>A;rs1800796.G>C;rs1800797.A>C;rs1800797.A>G;rs1800797.A>T;rs2066992.G>A;rs2066992.G>C;rs2066992.G>T;rs2069835.T>C;rs2069837.A>C;rs2069837.A>G;rs2069840.C>G | | ITGB3 | Reference;rs11871251.G>A;rs11871251.G>C;rs2317676.A>G;rs3785873.G>A;rs3785873.G>T;rs58847127.G>A;rs58847127.G>C;rs58847127.G>T;rs5918.T>C;rs8069732.C>A;rs8069732.C>T | | KIF6 | Reference;rs20455.A>G;rs9462535.C>A;rs9462535.C>G;rs9462535.C>T;rs9471077.A>G | | LPA | Reference;rs10455872.A>G;rs3798220.T>C | | MT-RNR1 | NC\_012920.1:m.1520T>C;NC\_012920.1:m.1537C>T;NC\_012920.1:m.1556C>T;NC\_012920.1:m.669T>C;NC\_012920.1:m.747A>G;NC\_012920.1:m.786G>A;NC\_012920.1:m.807A>C;NC\_012920.1:m.807A>G;NC\_012920.1:m.839A>G;NC\_012920.1:m.896A>G;NC\_012920.1:m.930A>G;NC\_012920.1:m.960delC;NC\_012920.1:m.988G>A;Reference;rs1556422499.delT;rs200887992.G>A;rs267606617.A>G;rs267606618.T>C;rs267606619.C>T;rs28358569.A>G;rs28358571.T>C;rs28358572.T>C;rs3888511.T>G;rs56489998.A>G | | MTHFR | Reference;rs1476413.C>G;rs1476413.C>T;rs17367504.A>G;rs17421511.G>A;rs1801131.T>G;rs1801133.G>A;rs1801133.G>C;rs2274976.C>T;rs3737967.G>A;rs4846051.G>A;rs4846051.G>C;rs4846051.G>T | | NUDT15 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | OPRD1 | Reference;rs1042114.G>C;rs1042114.G>T;rs10753331.G>A;rs10753331.G>T;rs12749204.A>G;rs204047.G>C;rs204047.G>T;rs204055.T>A;rs204055.T>C;rs204069.A>G;rs204076.T>A;rs204076.T>C;rs204076.T>G;rs2234918.C>G;rs2234918.C>T;rs2236855.C>A;rs2236855.C>G;rs2236857.T>C;rs2236861.G>A;rs2298895.A>T;rs2298896.T>G;rs2298897.C>G;rs3766951.T>C;rs419335.A>G;rs421300.A>C;rs421300.A>G;rs4654327.G>A;rs4654327.G>T;rs482387.G>A;rs482387.G>C;rs508448.A>G;rs529520.A>C;rs529520.A>G;rs533123.G>A;rs533123.G>C;rs569356.A>G;rs581111.A>C;rs581111.A>G;rs581111.A>T;rs6669447.T>C;rs678849.C>G;rs678849.C>T;rs680090.G>A;rs760589.G>A;rs797397.G>A | | OPRK1 | Reference;rs10111937.C>T;rs1051660.C>A;rs1051660.C>G;rs1051660.C>T;rs16918842.C>A;rs16918842.C>T;rs16918875.G>A;rs16918909.A>G;rs16918941.A>G;rs3802279.C>T;rs3802281.T>C;rs3808627.C>G;rs3808627.C>T;rs6473797.T>C;rs6473799.A>G;rs6985606.T>A;rs6985606.T>C;rs7016778.A>T;rs702764.T>C;rs702764.T>G;rs7813478.T>C;rs963549.C>T;rs997917.T>C | | OPRM1 | Reference;rs10457090.A>G;rs10457090.A>T;rs10485057.A>G;rs10485058.A>G;rs10485060.C>A;rs1074287.A>G;rs11575856.G>A;rs12190259.A>C;rs12205732.G>A;rs12209447.C>T;rs12210856.T>G;rs1294092.A>G;rs1319339.T>A;rs1319339.T>C;rs13195018.A>C;rs13195018.A>T;rs13203628.A>G;rs1323040.A>G;rs1323042.G>C;rs1323042.G>T;rs1381376.C>A;rs1381376.C>G;rs1381376.C>T;rs1461773.G>A;rs17174629.A>G;rs17174794.C>G;rs17174794.C>T;rs17174801.A>G;rs17180982.dup;rs17181352.A>G;rs1799971.A>G;rs1799972.C>A;rs1799972.C>G;rs1799972.C>T;rs1852629.T>A;rs1852629.T>C;rs1852629.T>G;rs2010884.G>A;rs2075572.G>C;rs2236256.C>A;rs2236257.G>C;rs2236258.C>G;rs2236258.C>T;rs2236259.T>A;rs2236259.T>C;rs2236259.T>G;rs2281617.C>G;rs2281617.C>T;rs3778148.G>T;rs3778150.T>C;rs3778151.T>C;rs3778152.A>G;rs3778156.A>G;rs3798676.C>T;rs3798677.A>G;rs3798678.A>C;rs3798678.A>G;rs3798683.G>A;rs3798688.G>T;rs3823010.G>A;rs483481.G>A;rs483481.G>C;rs4870266.G>A;rs495491.A>G;rs497976.G>A;rs497976.G>T;rs499796.A>G;rs506247.A>C;rs510769.C>T;rs511435.C>G;rs511435.C>T;rs518596.G>A;rs524731.C>A;rs527434.T>A;rs527434.T>C;rs538174.T>C;rs540825.A>C;rs540825.A>G;rs540825.A>T;rs544093.G>A;rs544093.G>T;rs548646.T>A;rs548646.T>C;rs548646.T>G;rs553202.C>T;rs558025.A>G;rs558948.C>G;rs558948.C>T;rs562859.C>A;rs562859.C>G;rs562859.C>T;rs563649.C>T;rs569284.A>C;rs583664.T>C;rs589046.C>T;rs598160.G>A;rs598160.G>C;rs598682.A>C;rs598682.A>G;rs598682.A>T;rs599548.G>A;rs606545.G>A;rs606545.G>C;rs609148.G>A;rs609148.G>T;rs609623.T>A;rs609623.T>C;rs610231.G>A;rs610231.G>C;rs613355.C>A;rs613355.C>G;rs613355.C>T;rs618207.A>C;rs618207.A>G;rs618207.A>T;rs62436463.C>T;rs62638690.G>T;rs632499.A>C;rs632499.A>G;rs632499.A>T;rs639855.C>A;rs639855.C>G;rs642489.G>A;rs642489.G>T;rs644261.G>A;rs644261.G>C;rs644261.G>T;rs645027.A>G;rs647192.G>A;rs647192.G>C;rs648007.A>C;rs648007.A>G;rs648893.A>G;rs650825.G>A;rs6557337.C>A;rs6557337.C>T;rs658156.A>C;rs658156.A>G;rs658156.A>T;rs671531.A>G;rs671531.A>T;rs675026.A>C;rs675026.A>G;rs677830.C>A;rs677830.C>G;rs677830.C>T;rs681243.T>A;rs681243.T>C;rs6902403.T>C;rs6912029.G>T;rs73576470.A>G;rs7748401.T>G;rs7763748.C>A;rs7763748.C>T;rs7776341.A>C;rs79910351.C>T;rs9282815.C>A;rs9282815.C>T;rs9322446.G>A;rs9322447.A>C;rs9322447.A>G;rs9322447.A>T;rs9322453.G>C;rs9371773.G>A;rs9371776.G>A;rs9384174.C>G;rs9384174.C>T;rs9384179.G>A;rs9384179.G>T;rs9397685.A>G;rs9397685.A>T;rs9397687.C>T;rs9479757.G>A;rs9479779.A>G | | RYR1 | NC\_000019.10:g.38440818G>C;NC\_000019.10:g.38444179C>A;NC\_000019.10:g.38444252G>T;NC\_000019.10:g.38444257A>C;NC\_000019.10:g.38444257A>G;NC\_000019.10:g.38448680\_38448681insGGA;NC\_000019.10:g.38448715G>A;NC\_000019.10:g.38451785C>A;NC\_000019.10:g.38452985C>T;NC\_000019.10:g.38455253C>G;NC\_000019.10:g.38455254T>C;NC\_000019.10:g.38455347T>C;NC\_000019.10:g.38455504G>T;NC\_000019.10:g.38466392G>A;NC\_000019.10:g.38469404A>C;NC\_000019.10:g.38485679T>C;NC\_000019.10:g.38486095A>G;NC\_000019.10:g.38490642A>C;NC\_000019.10:g.38494454G>A;NC\_000019.10:g.38496455G>A;NC\_000019.10:g.38499234T>C;NC\_000019.10:g.38499642C>A;NC\_000019.10:g.38499667G>A;NC\_000019.10:g.38499667G>T;NC\_000019.10:g.38499680T>A;NC\_000019.10:g.38499683G>A;NC\_000019.10:g.38499696C>G;NC\_000019.10:g.38499719A>G;NC\_000019.10:g.38499730G>A;NC\_000019.10:g.38499985A>T;NC\_000019.10:g.38500000G>A;NC\_000019.10:g.38502669C>G;NC\_000019.10:g.38504298G>A;NC\_000019.10:g.38506508C>G;NC\_000019.10:g.38506865C>T;NC\_000019.10:g.38507821C>T;NC\_000019.10:g.38512279G>A;NC\_000019.10:g.38515052C>T;NC\_000019.10:g.38516181T>C;NC\_000019.10:g.38516208G>C;NC\_000019.10:g.38517470T>C;NC\_000019.10:g.38517523T>A;NC\_000019.10:g.38519424C>A;NC\_000019.10:g.38519432A>T;NC\_000019.10:g.38519447A>G;NC\_000019.10:g.38525432C>T;NC\_000019.10:g.38527710G>C;NC\_000019.10:g.38528372G>T;NC\_000019.10:g.38529002G>C;NC\_000019.10:g.38529042C>T;NC\_000019.10:g.38543380A>T;NC\_000019.10:g.38543566G>A;NC\_000019.10:g.38543810C>T;NC\_000019.10:g.38548253A>T;NC\_000019.10:g.38561140G>C;NC\_000019.10:g.38561213C>T;NC\_000019.10:g.38561362G>A;NC\_000019.10:g.38561363G>T;NC\_000019.10:g.38565023T>G;NC\_000019.10:g.38570649C>G;NC\_000019.10:g.38577931A>C;NC\_000019.10:g.38578205G>T;NC\_000019.10:g.38580039\_38580040delinsAA;NC\_000019.10:g.38580041C>A;NC\_000019.10:g.38580126C>G;NC\_000019.10:g.38580397G>C;NC\_000019.10:g.38580416C>T;NC\_000019.10:g.38585078A>G;NC\_000019.10:g.38585099G>A;NC\_000019.10:g.38586190A>G;NC\_000019.10:g.38587362G>C;NC\_000019.10:g.38587363G>C;Reference;rs111272095.C>T;rs111364296.G>A;rs111565359.G>A;rs111657878.T>C;rs111888148.G>A;rs112151058.G>A;rs112196644.A>G;rs112563513.G>A;rs112596687.T>A;rs112772310.G>A;rs113210953.A>G;rs113332073.G>A;rs113332073.G>T;rs117886618.C>G;rs118192113.C>A;rs118192116.C>G;rs118192116.C>T;rs118192121.A>C;rs118192122.G>A;rs118192123.T>C;rs118192124.C>T;rs118192126.A>G;rs118192130.G>A;rs118192135.G>A;rs118192140.C>T;rs118192151.G>A;rs118192151.G>C;rs118192158.G>A;rs118192159.C>G;rs118192160.G>A;rs118192160.G>T;rs118192161.C>T;rs118192162.A>C;rs118192162.A>G;rs118192163.G>A;rs118192163.G>C;rs118192163.G>T;rs118192167.A>G;rs118192168.G>A;rs118192170.T>C;rs118192172.C>T;rs118192175.C>T;rs118192176.G>A;rs118192177.C>G;rs118192177.C>T;rs118192178.C>G;rs118192178.C>T;rs118192181.C>T;rs118204421.C>T;rs118204422.T>C;rs118204423.G>A;rs118204423.G>C;rs121918592.G>A;rs121918592.G>C;rs121918593.G>A;rs121918594.G>A;rs121918594.G>T;rs121918595.C>T;rs121918596.\_38499648delGAG;rs137932199.G>A;rs137933390.A>G;rs138874610.G>A;rs139161723.G>A;rs139647387.A>G;rs140152019.G>A;rs140616359.G>A;rs141646642.C>G;rs141942845.G>A;rs142474192.G>A;rs142474192.G>T;rs143398211.G>A;rs143520367.C>T;rs143987857.G>A;rs143988412.A>G;rs143988412.A>T;rs144336148.G>A;rs144685735.C>T;rs145573319.A>G;rs145801146.C>T;rs146306934.G>A;rs146429605.A>G;rs146504767.G>A;rs146876145.C>T;rs147136339.A>G;rs147213895.A>G;rs147303895.G>A;rs147707463.C>T;rs147723844.A>G;rs148399313.G>A;rs148623597.G>A;rs150396398.G>C;rs151029675.C>T;rs151119428.G>A;rs1801086.G>A;rs1801086.G>C;rs1801086.G>T;rs180714609.G>A;rs186983396.C>G;rs186983396.C>T;rs192863857.C>T;rs193922744.T>G;rs193922745.\_38440752delTGA;rs193922746.A>G;rs193922747.T>C;rs193922748.C>T;rs193922749.C>A;rs193922750.C>A;rs193922751.G>A;rs193922752.A>G;rs193922753.G>A;rs193922753.G>T;rs193922754.G>A;rs193922755.G>A;rs193922756.A>G;rs193922757.C>T;rs193922759.G>A;rs193922760.A>T;rs193922761.G>T;rs193922762.C>A;rs193922762.C>T;rs193922764.C>A;rs193922764.C>G;rs193922764.C>T;rs193922766.G>A;rs193922766.G>T;rs193922767.G>A;rs193922767.G>T;rs193922768.C>A;rs193922768.C>T;rs193922769.T>C;rs193922769.T>G;rs193922770.C>T;rs193922772.G>A;rs193922772.G>T;rs193922775.C>T;rs193922776.C>T;rs193922777.C>T;rs193922781.C>T;rs193922782.T>G;rs193922783.T>A;rs193922788.G>C;rs193922789.G>A;rs193922790.A>T;rs193922791.C>T;rs193922792.G>T;rs193922793.T>A;rs193922795.G>A;rs193922797.G>A;rs193922798.G>C;rs193922799.G>A;rs193922801.A>G;rs193922802.G>A;rs193922803.C>T;rs193922804.A>G;rs193922805.T>G;rs193922806.C>G;rs193922807.G>C;rs193922809.G>A;rs193922810.G>A;rs193922810.G>T;rs193922812.C>T;rs193922813.G>C;rs193922815.G>A;rs193922815.G>C;rs193922816.C>T;rs193922817.C>T;rs193922818.G>A;rs193922819.T>C;rs193922822.C>G;rs193922822.C>T;rs193922824.C>T;rs193922826.C>G;rs193922826.C>T;rs193922827.G>C;rs193922828.G>A;rs193922829.G>A;rs193922830.C>T;rs193922831.T>A;rs193922832.G>A;rs193922833.G>A;rs193922834.G>A;rs193922838.G>A;rs193922838.G>T;rs193922839.G>A;rs193922840.T>G;rs193922842.C>G;rs193922842.C>T;rs193922843.G>T;rs193922844.C>A;rs193922848.A>T;rs193922849.C>A;rs193922850.T>C;rs193922852.G>C;rs193922852.G>T;rs193922853.A>T;rs193922855.C>T;rs193922860.G>A;rs193922862.\_38572267delinsCT;rs193922863.C>T;rs193922864.T>C;rs193922865.T>G;rs193922866.G>A;rs193922867.C>T;rs193922868.G>A;rs193922873.G>A;rs193922873.G>T;rs193922874.T>C;rs193922876.C>T;rs193922877.delA;rs193922878.C>G;rs193922879.G>A;rs193922880.C>G;rs193922883.T>C;rs193922888.G>A;rs193922895.C>A;rs193922896.G>T;rs193922898.T>A;rs199738299.A>G;rs199870223.C>T;rs200766617.G>A;rs201321695.A>G;rs2145447772.G>A;rs2145447772.G>C;rs28933396.G>A;rs28933396.G>T;rs28933397.C>T;rs34390345.A>G;rs34694816.A>G;rs34934920.C>T;rs35180584.C>G;rs35364374.G>T;rs370634440.G>A;rs370634440.G>T;rs372958050.T>C;rs373406011.C>T;rs375626634.T>C;rs375915752.C>T;rs376149732.C>T;rs4802584.C>G;rs537994744.G>A;rs549201486.C>T;rs551223467.C>T;rs553055844.G>A;rs55876273.G>C;rs587784372.C>T;rs63749869.G>A;rs727504129.C>T;rs746818096.T>A;rs747177274.G>C;rs748575133.T>A;rs749040743.G>A;rs751180702.G>A;rs752652072.C>T;rs754476250.C>T;rs754785770.A>G;rs755088027.G>A;rs756850145.A>G;rs757753317.G>A;rs759500310.T>C;rs761616815.G>A;rs762401851.G>A;rs763112609.C>T;rs763352221.C>T;rs767553612.A>G;rs768360593.G>A;rs768535909.T>C;rs769482889.C>T;rs770593660.G>C;rs771058055.G>A;rs771741606.C>T;rs773040531.A>G;rs778241277.G>A;rs781104539.A>G;rs781126470.C>T;rs901087791.G>A;rs914804033.G>A;rs914804033.G>C;rs917523269.C>T;rs936513262.G>A;rs959170123.G>A;rs976108591.A>G;rs995399438.T>C | | SLCO1B1 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*19;\*2;\*20;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*5;\*6;\*7;\*8;\*9 | | TNF | Reference;rs1799724.C>T;rs1799964.T>C;rs1800610.G>A;rs1800629.G>A;rs1800630.C>A;rs1800750.G>A;rs2736195.A>G;rs3093548.C>T;rs3093662.A>G;rs3093726.T>C;rs361525.G>A;rs4248158.C>T;rs4248159.C>A;rs4248160.G>A;rs4248163.C>A;rs4248163.C>G;rs4248163.C>T;rs4647198.C>T;rs4987086.G>A;rs55634887.G>A;rs55994001.C>A;rs55994001.C>T | | TPMT | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*3A;\*3B;\*3C;\*4;\*40;\*41;\*42;\*43;\*44;\*5;\*6;\*7;\*8;\*9 | | UGT1A1 | \*1;\*27;\*28;\*36;\*37;\*6;\*80;\*80+\*28;\*80+\*37 | | UGT1A4 | \*1a;\*1b;\*1c;\*2;\*3a;\*3b;\*4;\*7 | | UGT2B15 | \*1;\*2;\*3;\*4;\*5;\*6;\*7 | | VKORC1 | Reference;rs9923231 variant (T) | | YEATS4 | Reference;rs7297610.C>T | ## PGx Star Allele Coverage for Specific PGx Products PGx star alleles can only be called when the related variants in the star allele definition are present in a PGx product. An auxiliary file (\[Product]\_GS\_import.txt) is provided for each product with the PGx variants and associated star alleles. The product files pages that contain the auxiliary files are listed in the table below. The aux file covers SNPs and indels only, it does not contain SV defined star-alleles. Instructions on how to use the auxiliary file can be found here: [How to use the auxiliary file](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000008505). | Product | GS Import File Name | Product Files Link | | ---------- | ---------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | GDA-ePGx | GDAePGx\_G2\_GS\_import.txt | [GDA-ePGx G2 product files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html) | | GSAv4-ePGx | GSAePGx\_E2\_GS\_import.txt | [GSAv4-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | | GCRA-ePGx | GCRAePGx\_E2\_GS\_import.txt | [GCRA-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | ## Known Limitations of GDA-ePGx, GSAv4-ePGx, and GCRA-ePGx. * APOE: GSAv4-ePGx and GCRA-ePGx do not support calling E2 and E4 due to the lack of functional probes for rs7412 and rs429358. * CYP2A6: GDA-ePGx does not support \*5 due to lack of coverage for \*5 core variants. * CYP4F2: for all three products * \*1 and \*2 are not distinguishable due to the lack of probes for rs30193105. Samples with \*2 will be called as \*1. * \*3 and \*4 are not distinguishable due to the lack of probes for rs30193105, while \*3 core variant rs2108622 is covered by all three products. Samples with \*4 will be called as \*3. * UGT1A1: \*28 (rs8175347 \[TA]8) and \*37 (rs8175347 \[TA]9) are not covered in all three PGx products due to the lack of functional probes. * UGT2B15: GSAv4-ePGx and GCRA-ePGx do not support \*4 or \*5 due to the lack of probes for rs4148269 and rs1902023. * CYP2D6: Due to the design of the probes, \*40 (rs72549356\[AAAGGGGCG]3) and \*58 (rs72549356\[AAAGGGGCG]2) cannot be distinguished. As a result, both alleles are reported as \*40. * NUDT15: Due to the design of the probes, \*6 (rs746071566dupGAGTCG) and \*9 ((rs746071566delGAGTCG)) cannot be distingushed. As a result, both alleles are reported as \*6. ## PGx Variants Masked in DRAGEN Array During DRAGEN Array star allele calling, poorly performing PGx variants are masked and treated as "No Calls". Star alleles that are solely defined by the masked variants will NOT be called by DRAGEN Array. The tables below provide the variants that are masked per product with each row represents a single variant. The Variant\_ID matches the ID field of the corresponding SNV VCF entry of the PGx product. ### GDA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GDA\_PGx-8v1-0\_20042614\_G | CYP1A2 | ilmnseq\_rs35694136\_ilmnfwd;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup1;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GDA\_PGx-8v1-0\_20042614\_G | CYP2D6 | ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_IlmnRep;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup3;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup3 | | GDA\_PGx-8v1-0\_20042614\_G | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GDA\_PGx-8v1-0\_20042614\_G | UGT1A1 | ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel;ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev;ilmnseq\_rs8175347\_ilmnrev\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev\_ilmndup2;ilmnseq\_rs8175347\_ilmnrev\_ilmndup3 | ### GSAv4-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GSA-PGx-48v4-0\_20079540\_E | CYP1A2 | IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs367543002,ilmnseq\_rs367543002\_ilmnfwd,ilmnseq\_rs367543002\_ilmnfwd\_ilmndup1,ilmnseq\_rs367543002\_ilmnrev\_deg3a1b0\_ilmndup1,rs367543002 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1,ilmnseq\_rs17882687\_ilmnrev\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C9 | 10:96701973,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup1,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | ilmnseq\_rs1135836\_ilmnrev\_deg3a3b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev\_F2BT,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GSA-PGx-48v4-0\_20079540\_E | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | | GSA-PGx-48v4-0\_20079540\_E | UGT1A1 | ilmnseq\_rs8175347.2\_ilmnrev\_F2BTindel\_cei\_ilmndup31 | ### GCRA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | ---------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GCRA-PGx-24v1-0\_20084467\_C | COMT | ilmnseq\_rs7287550\_ilmnfwd\_F2BT | | GCRA-PGx-24v1-0\_20084467\_C | CYP1A2 | IlmnSeq\_rs35694136;IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;rs35694136 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs367543002\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C9 | ilmnseq\_rs774607211\_ilmnrev,ilmnseq\_rs774607211\_ilmnrev\_ilmndup2 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | ilmnseq\_rs2004511\_dup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0\_ilmndup1,ilmnseq\_rs769157652\_ilmnrev\_ilmndup1,seq-rs61737947 | | GCRA-PGx-24v1-0\_20084467\_C | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | # Document Revision History The version history for DRAGEN Array product documentation: | Version | Date | Description of Change | | ------- | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 01 | December 2023 | Initial release | | 02 | March 2024 | Added details for DRAGEN Array v1.0.0 cloud genotype pipeline release. | | 03 | May 2024 | Added details for DRAGEN Array methylation QC pipeline v1.0.0 release. Error correction in the CNV VCF example (CN=4 to CN=5). | | 04 | September 2024 | DRAGEN Array v1.1.0 release | | 05 | February 2025 | DRAGEN Array v1.2.0 release | | 06 | February 2025 | Updated DRAGEN Array v1.2.0 release notes | | 07 | June 2025 |

• Updated DRAGEN Array v1.2.0 release notes: added gtc-to-bedgraph LRR smoothing bug. "Bedgraph Smoothing window size" disabled in cloud interface.

• Added details for DRAGEN Array - Cytogenetics analysis + Emedgene interpretation pipeline 1.2.0 release and corresponding release notes

| | 08 | August 2025 | DRAGEN Array v1.3.0 release. Rename of "CNV and LOH Calling" to "Cytogenetics analysis" | | 09 | October 2025 | `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` cloud pipeline v1.3.0 release | | 10 | February 2026 | [DRAGEN Array methylation QC pipeline v1.0.1 release](/dragen-array-v1.3/reference/release-notes/dragen-array-v1.0.1-cloud-methylqc-release-notes) | | 11 | March 2026 |

• Added details for the BaseSpace Planned Analyses feature associated with BaseSpace 7.44.0 release

• Major restructuring of Cloud Analysis section

| # Welcome to DRAGEN Array DRAGEN (Dynamic Read Analysis for GENomics) Array secondary analysis is a powerful bioinformatics software for Illumina Infinium array-based assays. DRAGEN Array uses cutting-edge data analysis tools to provide accurate, comprehensive, and highly efficient secondary analysis to maximize genomic insights and meet your research needs across multiple applications. DRAGEN Array is offered as a local package with command-line interface (no specialized server or hardware required) and as a cloud-based package with an intuitive graphical user interface, as summerized in the table below.
DescriptionKey featuresLocal analysisCloud analysis
GenotypingProvides genotyping results for any human Infinium genotyping array.
  • Greater than 99.5% genotyping accuracy
  • Genotyping VCF in as little as 35 seconds per sample
PGx – CNV callingProvides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Greater than 95% PGx CNV accuracy
PGx – star allele annotationProvides PGx star allele and variant coverage across 2400+ targets for over 50 genes, plus PGx CNV and genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Assess hard to discern PGx genes, including the elusive CYP2D6 with greater than 97% call rate
  • Obtain all PGx analysis results in ~1 minute per sample
Methylation QCProvides high-throughput, quantitative methylation quality control for Infinium methylation arrays.
  • 21 algorithm-based quantitative control metrics with adjustable thresholds
  • Data summary plots
  • Proportion of CG probes passing with user defined p-value threshold
CNV and LOH CallingProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays.
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
Cytogenetics analysis + Emedgene interpretationProvides cytogenetic CNV calling and LOH (loss of heterozygosity) detection for human Infinium arrays with added visualization and case management in Emedgene
  • Multiple output formats including CNV/LOH VCFs, annotated QC JSONs, and bedgraph files for Log R Ratio and B-Allele Frequency visualization
  • Adjustable algorithm thresholds such as minimum deletion, duplication, and LOH sizes and smoothing parameters
This product documentation describes the installation and setup, analysis execution, and result outputs. For the latest updates and release details, see the [DRAGEN Array Release Notes](/dragen-array-v1.2/reference/release-notes). See [Introducing DRAGEN™ Array 1.0 for Infinium™ Array-Based Pharmacogenomics Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) for additional details on DRAGEN Array genotyping, PGx CNV calling and PGx star allele annotation. # DRAGEN Array Applications The following Types of Analysis are currently supported by DRAGEN Array: * DRAGEN Array – Genotyping * DRAGEN Array – PGx – CNV calling * DRAGEN Array – PGx – Star allele annotation * DRAGEN Array – Methylation QC * DRAGEN Array – CNV and LOH Calling * DRAGEN Array - Cytogenetics analysis + Emedgene interpretation ### Product & Analysis Compatibility These products/beadchips have been verified to be compatible with the following analyses and versions of DRAGEN Array: | Manifest Name | DRAGEN Array Cloud Version(s) | DRAGEN Array Local Version(s) | Analysis | Genome(s) | | ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------- | ----------------------------- | ------------------------------------------ | -------------- | | [BovineSNP50\_v3\_A](https://support.illumina.com/array/array_kits/bovinesnp50-beadchip-kit.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | UMD3 | | [GDA-8v1-0\_D](https://support.illumina.com/array/array_kits/infinium-global-diversity-array.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV calling | GRCh37, GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0 | v1.0 | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GDA\_PGx-8v1-0\_20042614\_G](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GSA-24v3-0\_A](https://www.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GSA-PGx-48v4-0\_20079540\_E](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – Genotyping | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.0, v1.1 | v1.0+ | DRAGEN Array – PGx – CNV Calling | GRCh38 | | [GCRA-PGx-24v1-0\_20084467\_C](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | v1.1+ | v1.1+ | DRAGEN Array – PGx – Star allele annotate | GRCh38 | | [PRSbooster\_20083382\_A](https://support.illumina.com/array/array_software/gda-prs.html) | v1.1+ | v1.0+ | DRAGEN Array – Genotyping | GRCh37 | | [EPIC-8v1-0\_B5](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [EPIC-8v2-0\_A2](https://support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [MSA-48v1-0\_20102838\_A1](https://support.illumina.com/array/array_kits/infinium-methylation-screening-array.html) | v1.0 | N/A | DRAGEN Array – Methylation – QC | GRCh38 | | [CytoSNP-850Kv1-4\_iScan\_B](https://support.illumina.com/array/array_kits/cytosnp-850k_beadchip_kit.html) | v1.2 | v1.2 | DRAGEN Array – CNV and LOH Calling – 1.2.0 | GRCh37, GRCh38 | | [GSACyto-24v1\_20044998\_C](https://support.illumina.com/array/array_kits/infinium-global-screening-array-cyto-24.html) | v1.2 | v1.2 | DRAGEN Array – CNV and LOH Calling – 1.2.0 | GRCh37, GRCh38 | | [GDACyto-8v1-0\_20047166\_E](https://support.illumina.com/array/array_kits/infinium-global-diversity-array-cyto-8.html) | v1.2 | v1.2 | DRAGEN Array – CNV and LOH Calling – 1.2.0 | GRCh37, GRCh38 | ## DRAGEN Array – Genotyping | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides genotyping results for any human Infinium genotyping array. | | Variant types detected |

SNV

Indel

| | Sample minimum | 1 sample | | Arrays supported | Any human Infinium genotyping array including custom and semi-custom to create a SNV VCF output. Illumina provides [Genome FASTA Files](/dragen-array-v1.2/product-guides/input-files#toc150786139) required to map to the reference genome for human, genome build 37 and 38. DRAGEN Array Cloud offers additional output formats including Locus Summary and Final Report which are applicable for Infinium arrays for human and non-human species. | | Related Local Commands |

genotype call

genotype gtc-to-vcf

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Genotyping** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on cloud and local]

• TBI Index File \[optional on cloud and local]

Per analysis batch:

• Genotype Summary Files

• Final Report \[cloud only]

• Locus Summary \[cloud only]

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array – PGx – CNV calling | Item | Description | | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

pgx copy-number call

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – CNV calling** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array – PGx – Star Allele Annotation | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides PGx annotation on over 50 genes, plus PGx CNV and genotyping outputs | | Variant types detected |

SNV

Indel

CNV

Star allele diplotype

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

genotype call

genotype gtc-to-vcf

pgx copy-number call

pgx star-allele call

pgx star-allele annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – PGx – Star Allele Annotation** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• PGx CN Model File \[pre-setup on cloud]

• PGx Database File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• PGx CNV VCF File

• BedGraph Files \[optional on local]

• Star Allele JSON File

Per analysis batch:

• Star Allele CSV File

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: Per sample analysis.

Cloud: Per sample analysis. iCredits to store data as needed.

Visit the Illumina Product Page to learn more.

| ## DRAGEN Array – Methylation QC | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides methylation QC for Infinium methylation arrays. | | Variant types detected | N/A | | Sample minimum | 1 sample | | Arrays supported | Recommended thresholds and all built-in control probes are available for Methylation Screening Array (MSA) and MethylationEPIC (v1 & v2) originating from iScan. In non-human and custom arrays, availability of built-in QC probes may vary, and failure thresholds must be defined by the user. | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Methylation – QC** from the dropdown. Adjust customizable thresholds as desired. Further detail can be found in Additional information for [DRAGEN Array Methylation QC](/dragen-array-v1.2/product-guides/dragen-array-cloud-analysis#dragen-array-methylation-qc). A maximum of 1152 samples are supported. | | Inputs |

• IDAT(s) \[from iScan instrument]

• Manifest Files \[may be pre-setup on cloud]

• IDAT Sample Sheet \[optional on cloud]

| | Outputs |

Per sample:

• Methylation Control Probe Output File

• Methylation CG Output File

Per analysis batch:

• Methylation Sample QC Summary Files

• Methylation Sample QC Summary Plots

• Methylation Principal Component Summary

• Methylation Manifest Files

• Methylation Logs and Error Files

| | Cost | Cloud: [iCredits](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. | ## DRAGEN Array – CNV and LOH Calling | Item | Description | | ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatability) | | Related Local Commands |

genotype call

genotype gtc-to-vcf \[optional]

genotype gtc-to-bedgraph

cyto call

cyto annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – CNV and LOH Calling** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Cytogenetics Model File \[pre-setup on cloud]

• Cytogenetics Database File \[only necessary for local]

• IDAT Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional on cloud]

• SNV VCF File \[optional on local and cloud]

• TBI Index File \[optional on local and cloud for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array - Cytogenetics analysis + Emedgene interpretation | Item | Description | | ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides cytogenetic genome-wide copy number and loss of heterozygosity calling. This analysis type integrates with Emedgene via [Automatic Case Creation from ICA](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) on cloud only. | | Variant types detected |

CNV

LOH

| | Sample minimum | Minimum of 1 sample. | | Arrays supported | Check Product & Analysis Compatibility here [Product & Analysis Compatibility](#product_compatability) | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Cytogenetics Model File \[pre-setup on cloud]

• Cytogenetics Database File \[only necessary for local]

• IDAT Sample Sheet \[optional]

| | Outputs |

Per sample:

• Genotype Call (GTC) File \[optional on cloud]

• SNV VCF File \[optional on local and cloud]

• TBI Index File \[optional on local and cloud for snv vcf]

• Cytogenetics CNV VCF File

• Cytogenetics Annotation JSON File

• BedGraph Files \[optional on local]

Per analysis batch:

• Genotype Summary Files

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed. As well as additional sample-based costs if uploaded into the Emedgene interface.

| # DRAGEN Array Cloud Analysis ## DRAGEN Array Cloud Analysis Overview DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. ## Getting Started The following prerequisites are needed to get started with DRAGEN Array Cloud: * **Illumina Connected Analytics subscription**: An ICA Basic, Professional or Enterprise subscription can be used which include access to BaseSpace Sequence Hub. Follow the [Illumina Software Registration Guide](https://help.connected.illumina.com/account-management/rg-registration) to register the software. * **Workgroup setup**: Workgroups must be created before login. Using a workgroup allows all members of the workgroup to share access to resources, analyses, and data. Learn more about [managing a Workgroup](https://help.basespace.illumina.com/collaborate/manage-workgroups). * Designating a workgroup as ‘Collaborative’ allows projects to be shared with collaborators or Illumina Tech Support to assist with troubleshooting. To create a collaborative workgroup, select the Enable collaborators outside of this domain checkbox during workgroup creation. * **Software consumables**: iCredits can be purchased for storage on the cloud platform and analysis pipelines with a compute charge. Per sample analysis can be purchased for relevant pipelines as listed in section [Applications](/dragen-array-v1.2/overview/our-features). Follow the [Example 3: Configuring Software Consumables (iCredits or Sample Analyses)](https://help.connected.illumina.com/account-management/rg-registration#example-3-configuring-software-consumables-icredits-or-sample-analyses) in the Illumina Software Registration Guide to register the software consumables. * **\[Optional] iScan integration**: The iScan System is integrated with Illumina Connected Platform and can send IDATs for further analysis. The iScan System must be running iScan Control Software version 4.2.1 or later. * [Instructions to Use Illumina Connect Analytics (ICA) with the iScan System](http://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/UseICA_fIS.htm) * [Troubleshooting iScan integration](#troubleshooting_iscan_integration_1) * **EULA acceptance**: Accept all necessary End User License Agreements in BaseSpace Sequence Hub before scanning begins. * **Internet connection**: For uploading product files or IDATs, a network connection 1 GbE or faster is recommended. ## Running Analysis Before beginning analysis, ensure workgroup context is being used so analysis can be viewed by all members of your workgroup. The name of your workgroup should appear in the top right corner. Use the following steps to run the Microarray Analysis Setup on BaseSpace Sequence Hub: 1. Select the **Runs** tab 2. Select **New Run** 3. Select **Microarray Analysis Setup** 4. Enter the Analysis Name (Figure 1) ![Figure 1. Configuration step of Microarray Analysis Setup](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-f37dcbd018c7953f272f9168dbc113db2bedbc75%2F1.png?alt=media) 5. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 6. Select the Type of Analysis\ Further detail of each Type of Analysis is available in section [Applications](/dragen-array-v1.2/overview/our-features).**Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 7. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected (Figure 2). Custom configuration options vary by Type of Analysis including: * **DRAGEN Array – Genotyping** provides flexibility for turning off/on specific output files and adjusting GenCall score cutoff. Its recommended to turn off VCF output for non-human species and Final Report output for large sample numbers. * **DRAGEN Array – CNV and LOH Calling** provides options to adjust thresholds as detailed in section DRAGEN Array [Cytogenetic CNV and LOH Threshold Adjustment](#cytogenetic-cnv-and-loh-threshold-adjustment). * **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** shares the same options detailed in the DRAGEN Array [Cytogenetic CNV and LOH Threshold Adjustment](#cytogenetic-cnv-and-loh-threshold-adjustment) section. * **DRAGEN Array – Methylation – QC** provides options to adjust thresholds as detailed in section DRAGEN Array Methylation QC [Threshold Adjustment](#methylation-qc-threshold-adjustment). * **DRAGEN Array – PGx – Star allele annotation** provides an option to change the default metabolizer status database used from [CPIC](https://cpicpgx.org/) to [DPWG](https://www.pharmgkb.org/page/dpwg). ![Figure 2. Optional Custom Configuration step of Microarray Analysis Setup](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-38cae339e6f946e78b37b0d956d4d62bde623238%2F4.png?alt=media) 8. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [Applications](/dragen-array-v1.2/overview/our-features). 9. Select Next 10. Select either **Import Sample Sheet,** **Select BeadChips,** or **Import IDAT Files** (Figure 3) * **Import Sample Sheet** presents a link to upload sample sheet. Users may download a template sample sheet by selecting the Download Template link. * **Select BeadChips** allows users to select BeadChips from the displayed list of available BeadChips. If selecting specific samples within the BeadChip is desired the Import Sample Sheet option should be used. * **Import IDAT Files** allows users to upload the IDAT files from a local folder to the cloud platform for use with the current and future analyses by users within the same workgroup. 11. Select **Launch Analysis** ![Figure 3. Sample Selection step of Microarray Analysis Setup](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-c5adf73fdf33c60f74ba6583a146064156ce59c6%2F2.png?alt=media) ## View Outputs 1. On the Analyses tab, view the analysis status, e.g., initializing or complete. 2. After the analysis is complete, select the analysis and select the Files tab. 3. From the Files tab, select the Output folder. ## Manage Data The data management tab allows you to view and manage all your scanned IDAT files in the cloud. Before viewing, ensure workgroup context is being used so all data available your workgroup can be seen. The name of your workgroup should appear in the top right corner. For more information, see [BaseSpace Data Management](https://help.basespace.illumina.com/microarray/data-management) ## DRAGEN Array - CNV and LOH Calling ### Cytogenetic CNV and LOH Threshold Adjustment When using **DRAGEN Array – CNV and LOH Calling** or **DRAGEN Array - Cytogenetics analysis + Emedgene interpretation** cloud analysis types, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on results objectives. Adjusting thresholds will impact the number of events called and thus, the output in the VCF and JSON files. The recommended thresholds/settings are pre-set within the software for any new configurations: | Threshold | New Config | Min Value | Max Value | | ------------------------- | ---------- | --------- | --------- | | GTC Output | False | N/A | N/A | | SNV VCF Output | False | N/A | N/A | | CNV minimum size (kb) | 0 | 0 | 250000 | | CNV minimum probes | 10 | 0 | 250000 | | LOH minimum size (kb) | 3000 | 0 | 250000 | | LOH minimum probes | 500 | 0 | 250000 | | CNV Smoothing window size | 5 | 0 | 1000 | ## DRAGEN Array - Cytogenetics analysis + Emedgene interpretation This analysis type integrates with [Emedgene](https://help.connected.illumina.com/emedgene) to display results in a user-friendly interface. ### Prerequisites * You'll need an additional Emedgene subscription to be either "Array", "Professional", or "Enterprise" tier. You can also follow the [Illumina Software Registration Guide](https://help.connected.illumina.com/account-management/rg-registration) to obtain that subscription. * To ensure proper integration with Emedgene (EMG), [ICA notifications](https://help.ica.illumina.com/project/p-notifications) must be enabled for the specific ICA BSSH-managed project. EMG relies on these notifications to detect when an analysis has successfully completed. To configure SNS ([Amazon Web Services Simple Notification Service](https://aws.amazon.com/sns/)) events in your managed ICA BSSH-managed project, follow these steps: * In the [ICA portal](https://ica.illumina.com/ica/), in the ICA BSSH-managed project (e.g. "BSSH Your Workgroup Name") navigate to the **Notifications** section via the left-hand menu. ![ICA Notifications Menu](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-cee9edf57b49986ed37c2820f23ceb4a27cbef1c%2Fica_notif_1.png?alt=media) * Click **+ Create**, then select **ICA Event**. ![ICA Event](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-145a3f2740d6095d80c1623df8fe5b124656315a%2Fica_notif_2.png?alt=media) * Fill in the required fields as follows: * **Event:** Analysis Success * **Type:** SNS * **Address:** Provide the correct address based on your region (contact if unsure). * **Payload Version:** v4 * **AWS Region:** This will be auto-populated based on the provided address. * (Recommended) Click **Send Test Message** to verify the configuration. * Click **Save** to complete the setup. ![Create Subscription](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-21d6ac4a976ca1394729fb7cb6ff0e91582669cb%2Fica_notif_3.png?alt=media) For more details on the prerequisites for this analysis, see the [Automatic Case Creation from ICA](https://help.emg.illumina.com/emedgene-analyze-manual/integrations/automatic-case-creation-from-ica-cyto-array-analysis) section in the Emedgene User Guide.\ For more details on limitations for this analysis see the [release notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes/dragen-array-v1.2.0-cyto-emg-release-notes) ## DRAGEN Array Methylation QC ### Methylation QC Threshold Adjustment When using **DRAGEN Array – Methylation – QC** cloud analysis type, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on study objectives. Adjusting thresholds will impact the pass or fail status of samples in the output files. Illumina recommends thresholds for MethylationEPIC v1 & v2 and Methylation Screening Array (MSA). Users may use these thresholds as a starting point when defining thresholds for their custom or semi-custom BeadChip or other Infinium Methylation arrays. Further tuning may be required based on BeadChip used, laboratory conditions, iScan settings, bisulfite conversion methods, FPPE sample type, etc. A dataset deemed acceptable to the user based on proportion probes passing can be used for these additional threshold adjustments. To customize thresholds, use the toggle to allow additional thresholds to be displayed and adjust as desired by typing in a numeric value or using the arrows to adjust up or down. Further detail of these thresholds including calculation method can be found in the [Methylation Sample QC Summary Files](/dragen-array-v1.2/product-guides/output-files#methyl_qc_report) section. The recommended thresholds are pre-set within the software for MethylationEPIC and Methylation Screening Array with the following values: | Threshold | Methylation Screening Array | MethylationEPIC | | ----------------------------------- | --------------------------- | --------------- | | Restoration[^1] | 0 | 0 | | StainingGreen | 5 | 5 | | StainingRed | 5 | 5 | | ExtensionGreen | 5 | 5 | | ExtensionRed | 5 | 5 | | HybridizationHighMedium | 1 | 1 | | HybridizationMediumLow | 1 | 1 | | TargetRemoval1 | 1 | 1 | | TargetRemoval2 | 1 | 1 | | BisulfiteConversion1Green | 1 | 1 | | BisulfiteConversion1BackgroundGreen | 0.5 | 1 | | BisulfiteConversion1Red | 1 | 1 | | BisulfiteConversion1BackgroundRed | 0.5 | 1 | | BisulfiteConversion2 | 0.5 | 1 | | BisulfiteConversion2Background | 0.5 | 1 | | Specificity1Green | 1 | 1 | | Specificity1Red | 1 | 1 | | Specificity2 | 1 | 1 | | Specificity2Background | 1 | 1 | | NonpolymorphicGreen | 2.5 | 5 | | NonpolymorphicRed | 3 | 5 | | BgCorrectionOffset | 3000 | 3000 | | PvalThreshold | 0.05 | 0.05 | The first 21 rows in the tables correspond to the 21 control metrics used in the methylation sample QC. See section [Methylation Sample QC Summary Files](/dragen-array-v1.2/product-guides/output-files#methyl_qc_report) for details. ### DRAGEN Array Methylation QC and GenomeStudio Methylation Module Differences DRAGEN Array Methylation QC software provides automated methylation sample QC using assay control probes on the Infinium Methylation Arrays. Unlike the manual visual QC in GenomeStudio, DRAGEN Array ultilizes 21 numerical metrics defined based on the control probes and uses standard thresholds to determine pass/fail status of a sample. Unlike GenomeStuio, probe detection rate (proportion of probes passing at a given p-value threshold) is not utilized to determine sample pass/fail status in DRAGEN Array. For more information, see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note. DRAGEN Array Methylation QC performs background normalization, dye bias correction, and detection p-value calculation differently in comparison to the GenomeStudio Methylation module, leading to differences in probe detection p-values and detection rates. For the GenomeStudio Methylation Module, non-cancer samples at standard DNA input typically have detection rate > 96%. The detection rates from DRAGEN Array Methylation QC are typically lower compared to GenomeStudio, because the detection p-value from DRAGEN Array is more stringent than that from the GenomeStudio Methylation Module. The table below shows example detection rates from the DRAGEN Array Methylation QC software from MSA (Methylation Screening Array) datasets. | Dataset | Min detection rate | Mean detection rate | Sample Count | | ------- | ------------------ | ------------------- | ------------ | | A | 86% | 93% | 220 | | B | 61% | 83% | 951 | | C | 63% | 85% | 34 | | D | 77% | 85% | 22 | Note that only samples passing QC are included and all samples are at or above 50ng DNA input. Detection p-value threshold 0.05. ## Troubleshooting and Additional Support ### Troubleshooting iScan integration The firewall protects the iScan control computer by filtering incoming traffic to remove potential threats. The firewall is enabled by default to block all inbound connections. Keep the firewall enabled and allow outbound connections. For the instrument to connect to BaseSpace Sequence Hub, you will need to add regional platform endpoints and instrument specific endpoints to the allow list on your firewall. Regional endpoints and further detail can be found in [Security and Networking for Illumina instrument control computers](https://support-docs.illumina.com/SHARE/NetworkSecurity/Content/SHARE/FrontPages/NetworkingSecurity.htm). The following table shows the applicable endpoints for the iScan.
EndpointCategoryPurpose
ica.illumina.comRequiredSend IDAT files to ICA
o.ss2.usRequiredCertificate authorization
ocsp.digicert.comRequiredCertificate authorization
ocsp.pki.goog/gsr2RequiredCertificate authorization
ocsp.rootca1.amazontrust.comRequiredCertificate authorization
ocsp.rootg2.amazontrust.comRequiredCertificate authorization
ocsp.sca1b.amazontrust.comRequiredCertificate authorization
fonts.gstatic.comRequiredDisplay fonts
fonts.googleapis.comRecommendedDisplay fonts
cdn.walkme.comRecommendedTelemetry
cdn3.userzoom.comRecommendedTelemetry
dpm.demdex.netRecommendedTelemetry
illuminainc.demdex.netRecommendedTelemetry
illuminainc.tt.omtrdc.netRecommendedTelemetry
smetrics.illumina.comRecommendedTelemetry
google.comRecommendedTelemetry
google-analytics.comRecommendedTelemetry
stats.g.doubleclick.netRecommendedTelemetry
illumina.comOptionalAccess Illumina support material
**Some notes on IDAT fail status:**\ iScan will mark certain samples with a FAIL status if the registration quality is too poor for that particular section. Selected samples that are marked with FAIL status will be excluded from analysis and there would be no results for that sample, even though IDATs are generated. The registration quality can be found in the metrics.txt file. **Some notes on Infinium LIMS:**\ If using Infinium LIMS intregration with the iScan, its possible to set sample names that will be encoded in the IDATs and downstream will show up in analysis output files like VCFs instead of the sample ID (Sentrix Barcode + Position). This can cause issues with [Emedgene](https://help.connected.illumina.com/emedgene) integration for cytogenetic analysis as that sample name is used as a unique identifier. So it is highly recommended to not use that feature in Infinium LIMS to ensure the sample names remain unique throughout the various analyses. ### Sharing a project Project sharing allows a user to share files with users outside the workgroup for collaboration or with Illumina Tech Support for troubleshooting. To share a project on BaseSpace Sequence Hub, first set the Workgroup type as ‘Collaborative’ during [Workgroup setup](#getting_started), and then use the following steps to obtain a link to your project. The project can then be accessed by anyone with the link. All files in the project are shared. 1. Navigate to the Projects tab 2. Click the button next to the desired project 3. Select the Share button above to list (Figure 3) 4. Select the Get Link Option to Activate a link for the project 5. Copy the link and send it to the desired recipient(s) **Additional Notes:** * The project owner maintains ownership and write access. If project owner deletes the data, the collaborators lose access to it. * Either sending or receiving domain must be collaborative. See "Workgroup setup" [here.](https://help.basespace.illumina.com/microarray/getting-started) * Must be in the same [AWS regional instance](https://help.basespace.illumina.com/manage-your-account/regions) (i.e."Data cannot be transferred directly between instances, however you can download and share data separately." ) * For Enterprise domains, use this same [method](https://help.basespace.illumina.com/collaborate/share-with-collaborators/share-by-link) (share-by-link, not share-by-transfer) ![Figure 3. Share data on BaseSpace Sequence Hub](https://3810604700-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F8ecPi5RESkAyGQ0GwJte%2Fuploads%2Fgit-blob-a0ab1b3138aedc34855258f1fe17d816392d2a89%2F3.png?alt=media) [^1]: If FFPE restore kit is used, Restoration threshold should be increased from 0 to 1. # DRAGEN Array Local Analysis ## DRAGEN Array Local Overview DRAGEN Array provides accurate, comprehensive, and efficient analysis of Infinium microarray data. The local command-line interface makes it easy for power users to have granular control and flexibility to support large scale microarray genomic studies. ## Getting Started DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. If new to using command-line interface, please review the [Command-line interface Basics](#toc150786129). ### Computing Requirements Before downloading and installing the software, ensure the following specifications are met for best performance: | Category | Recommendation | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | CPU | 8 cores | | Memory | 16 GB available or more | | Hard Drive | 30 GB or more of free disk space | | Operating System |

One of the following:

  • Windows 10 or later – win10-x64
  • CentOS 7 or later, Ubuntu 20.04 or later – linux-x64
| ### Quota Specifications The star-allele call command in DRAGEN Array Local requires quota to run. The quota is charged per sample analyzed and can be purchased on the [Illumina Product Page](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html). Quota is used for all samples analyzed including re-analysis or low-quality samples. The credential provided in the activation email after purchasing should be used as an input to the star-allele call command through the "--license-server-url" option. During runtime, the [logs](/dragen-array-v1.2/product-guides/output-files#toc150786153) will record the remaining quota at the beginning and the end of the analysis. Internet is required to do a software license check and ensure paid quota is available for all samples in the analysis batch. For the software license check, the following endpoints are used: * In v1.0 and v1.1: `license.edicogenome.com` * In v1.2+: `license.dragen.illumina.com` **NOTE:** Do not use `license.dragen.illumina.com` license server urls when running DRAGEN Array v1.0 and v1.1 as that domain only works with v1.2+ versions.\ This is described in the [1.0.0](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.1.0-release-notes#known-issues) and [1.1.0](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.1.0-release-notes#known-issues) known issues. ## Installation Please follow the steps below to install the software on your compute infrastructure: 1. Click on the latest DRAGEN Array version installation package for the platform of your choice. Installers for Windows and Linux are available on the [Illumina Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html).\ \ Once download is completed, move the DRAGEN Array installation package to the desired folder. Administrative permissions may be required for system folders, for example `/usr/local/bin for Linux`, and `C:\Program Files` for Windows.\ \ **Note**: Throughout the remainder of the document, Linux will be assumed in the examples. 2. Unzip and extract the package. The executable can be found in the dragena subfolder of the software download after extraction. 3. To check that the DRAGEN Array installation was successful, follow these steps: * Open a command prompt (Windows) or terminal (Linux). * \[Optional] Add `/path/to/dragena/`, e.g. `/usr/local/bin/dragena-linux-x64-DAv1.1.0/dragena/`, to your PATH – to access the executable anywhere in the folder structure * Execute the following command: `/path/to/dragena/dragena version`, or if the environmental variable PATH is set: dragena version The version of the software will be displayed in the terminal window when the installation was successful. ## Run DRAGEN Array Local For genotyping or cytogenetic analysis, there is no sample minimum required to run analysis. For CNV PGx analysis, a minimum of 24 samples is required to run analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. With a standard hardware specification in section [Computing Requirements](#computing_requirements), up to 500 GDA-ePGx samples can be processed per analysis batch. To optimize performance of the targeted PGx CNV caller and minimize batch effect, it is recommended to: * Group samples in the same assay batch (e.g. whole genome amplication and targeted gene application assay batch) into the same analysis batch. * Avoid combining sample batches processed on different reagent lots. * Analyze batches of 96 samples or more. * Samples processed in a two-week period from multiple library preparation batches can be grouped together to meet size requirement of an analysis batch. In such cases, it is recommended to use the same lot of reagents and instruments used in the workflow. * Use the CN Model and PGx Database File provided as part of the standard product files ## Quick Start Review section [DRAGEN Array Applications](/dragen-array-v1.2/overview/our-features) for information on input files to use, sample minimums per analysis type and other best practices. Command examples show analysis for a Linux system using folders instead of sample sheets. For Windows users, make sure to substitute the file paths in the commands following windows conventions, e.g., using backslash (\\) instead of forward-slash (/). A sample sheet can be used to select specific samples out of a folder. **Note**: DRAGEN Array will overwrite older files if using the same `--output-folder` from a previous analysis. If this is not desired, use different `--output-folder` for re-analyses. ### PGx Use the following instructions to start the full PGx analysis, covering genotyping, PGx CNV and PGx star allele calling. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the pgx copy-number call command to call PGx CNVs from the GTC files and produce CNV VCF files. It is recommended to use the same output folder used for SNV VCF since the star-allele call command accepts one VCF folder with SNV and PGx CNV VCFs.\ `dragena pgx copy-number call --cn-model /user/productfiles/cnv_model.dat --gtc-folder /user/gtc --output-folder /user/vcf`**Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. 5. Use the pgx star-allele call command to generate star allele calls using the CNV and SNV VCF files generated by the gtc-to-vcf and copy-number call commands.\ `dragena pgx star-allele call --vcf-folder /user/vcf --database /user/productfiles/GDA_ePGx_E2_DAv1.0.0.zip --output-folder /user/star-alleles --license-server-url https://username:password@license.dragen.illumina.com`**Note**: For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 6. Use the pgx star-allele annotate command to summarize the star alleles and add metabolizer statuses to the star alleles generated by the star-allele call command. Guidelines (CPIC or DPWG) can be specified.\ `dragena pgx star-allele annotate --star-alleles star_alleles.csv --guidelines CPIC --output-folder /user/metabolizer-statuses` 7. \[Optional] Use the pgx copy-number train command to retrain the copy number model.\ `dragena pgx copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --platform LCG --output-folder /user/productfiles/cnmodelnew` ### Cytogenetics Use the following instructions to start the full cytogenetics analysis, covering genotyping, CNV and LOH calling, and annotation. Refer to [Command Index](#command_index_1) for parameters for all commands. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the cyto call command to determine copy number variants and loss of heterozygosity given genotypes.\ `dragena cyto call --cn-model /user/productfiles/cyto_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the cyto annotate command to generate JSON annotation files with gene annotations, cytogenetic bands, various QC fields, and the variant information from the VCFs.\ `dragena cyto annotate --annotation-db /user/productfiles/CytoAnnotateData_DAv1.2.0.zip --vcf-folder user/vcf --output-folder /user/cyto-annotations` ## Command Index Use the following syntax when using the command-line interface: `dragena [module] [sub-module (not needed for cyto)] [command] [required parameters] [optional parameters]` ### **pgx** The root command for pgx module | Command | Description | | ----------- | ---------------------------------------------- | | copy-number | Call and train copy number variants. | | star-allele | Star Allele Caller for Illumina Microarrays | | help | Display more information on a specific command | | version | Display version information. | ### **pgx copy-number** The root command for actions that act on pgx copy number variants. | Command | Description | | ----------------------- | --------------------------------------------------------------------- | | pgx copy-number call | Determines copy number variants given genotypes (GTC to CNV VCF). | | pgx copy-number help | Displays help information for a copy-number command. | | pgx copy-number train | Trains copy number model for a set of samples (GTC to CN Model File). | | pgx copy-number version | Displays version information for copy-number. | ### **pgx copy-number call** The command used to call copy number variants. A batch of 24 samples or more are required for analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. | Option | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --cn-model | \[Required] Specifies the path to the copy number model parameters file (.dat). | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. The command cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). The sample sheet can be in CSV or JSON format. The command cannot be used with --gtc-folder. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the copy-number call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if the GTC folder is provided. | | --version | Displays version information. | ### **pgx copy-number help** Displays help information for a copy-number command. ### **pgx copy-number train** Trains pgx copy number (CN) model for a set of samples. Generate a new pgx CN model if using a customized cluster file (.egt) optimized for the specific data set. * Execute the train command using the data sets that were used to optimize the cluster file. * To use a pgx CN model generated by the train command, the mask file for the manifest must be saved in the same directory as the manifest. * A minimum of 96 samples is required to use the copy-number train command. For optimal performance, at least 150 is recommended. * For best performance, validate the pgx CN model using truth data before using in pgx CN calling. See [Optimizing cluster files and copy number models](#optimizing_cluster_files) for further details. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. Assumes mask file (.msk) is in the same directory. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --platform | \[Required] Specifies which microarray platform generated the data. Set this to 'LCG' for GDA-ePGx, 'EX' for GSAv4-ePGx or GCRA-ePGx. | | --debug | Includes stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --help | Displays help information for the copy-number train command. | | --json-log | Outputs logs in JSON format. Default is false. | | --version | Displays version information. | | --output-folder | \[Optional] The location to output the CN model. By default, the output folder is the current working directory. | ### **pgx copy-number version** Displays version information for pgx copy-number command. ### **genotype** The root command for genotype calling. | Command | Description | | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- | | genotype call | Determines genotype calls (GTC) from IDAT files. | | genotype gtc-to-bedgraph | Converts GTC to BedGraphs, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files. | | genotype gtc-to-vcf | Converts GTC to VCF. | | genotype help | Displays the help information for the genotype command. | | genotype version | Displays version information for the genotype command. | ### **genotype call** Determines genotype calls (GTC) from IDAT files. | Option | Description | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --cluster-file | \[Required] Specifies the path to the EGT cluster file to use. | | --idat-folder |

\[Required] Specifies the path to the directory where all intensity data IDATs (for the samples to be processed) are located. Must be in IDAT format. Cannot be used with --idat-sample-sheet.

This path also includes the contents of all subdirectories.

| | --idat-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to intensity data IDATs. Can be in CSV or JSON format. Cannot be used with --idat-folder. | | --debug | Includes stack traces in logs. Default is false. | | --gencall-cutoff | GenCall score cutoff to label a NoCall. Default is 0.15. | | --help | Displays help information for the genotype call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --num-threads | Number of parallel threads to run. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the IDAT folder, if the IDAT folder is provided. | | --version | Displays version information. | ### **genotype gtc-to-bedgraph** Converts GTC to BedGraph files, producing BedGraph formatted visualization files from the Log R Ratio and B-allele frequency data contained in the GTC intermediate files. | Option | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype (.gtc) files are located. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --debug | Include stack traces in logs. Default is false. | | --help | Displays help information for the genotype gtc-to-bedgraph command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if the GTC folder is provided. | | --smoothing | \[Optional] Smoothing window size, specifying the number of probes on each side of the center probe used for smoothing LRR. Default is 0. | | --version | Displays version information. | **NOTE:** The `--smoothing` option is not functioning as intended due to a bug in v1.2.0. See the [Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes#known-issues) for more details. ### **genotype gtc-to-vcf** Converts GTC (v5) to [SNV VCF Files](/dragen-array-v1.2/product-guides/output-files#snv_vcf_file). The command is only applicable for [Genotype Call Files](/dragen-array-v1.2/product-guides/output-files#genotype_call_file) produced by DRAGEN Array. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --csv-manifest | \[Required] Specifies the path to the CSV manifest with SourceSeq column. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --auxiliary-loci | Specifies the path to the VCF file with auxiliary definitions of loci, such as for multi-nucleotide variants. | | --debug | Include stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --filter-loci | Generates a text file containing a list of probe names to be filtered. | | --unsquash-duplicates | Generates unique VCF records for duplicate assays. Default is false. | | --help | Displays help information for the genotype gtc-to-vcf command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if GTC folder is provided. | | --version | Displays version information. | #### Squashing duplicates In the manifest, there can be cases where the same variant is probed by multiple different assays. These assays may be the same design or alternate designs for the same locus. In the default mode of operation, these duplicates will be "squashed" into a single record in the VCF to reflect a true variant rather than probe genotype. The method used to incorporate information across multiple assays is defined further in the [VCF description](/dragen-array-v1.2/product-guides/output-files#snv_vcf_file). When the `--unsquash-duplicates` option is provided, this "squashing" behavior is disabled, and each duplicate assay will be reported in a separate entry in the VCF file. This option is helpful when you are interested in investigating or validating the performance of individual assays, rather than trying to generate genotypes for specific variants. Note that if a locus has more than two alleles and is also queried with duplicated designs, the duplicates will not be unsquashed (i.e., in the case of multi-allelic variants).**DO NOT** use `--unsquash-duplicates` option if doing star allele calling downstream as that command expects squashed variants. #### Genome cache By default, the entire reference genome will be read into memory. Generally, this will be more efficient than reading data from the indexed reference on disk at the expense of greater memory utilization. For situations in which the genome caching is not desirable (low memory availability or a small input manifest), it is possible to disable this default behavior with the `--disable-genome-cache` option. #### Auxiliary loci Certain classes of variant types (such as multi-nucleotide variants) are not currently supported in the upstream analysis software that produces GTC files. However, it is possible to query this type of variant by creating a SNP design that differentiates the specific multi-nucleotide alleles of interest. For example, if the true source sequence is ATGC\[AT/CG]GTAA This assay could be designed as a SNP assay with the following source sequence ATGC\[A/C]NNNN `gtc-to-vcf` provides an option (`--auxiliary-loci`) to supply a list of auxiliary records (in VCF format) to restore the true alleles for these cases in the output VCF. There are several restrictions around this function * The auxiliary definition must NOT be a multi-allelic variant. * The auxiliary definition must be a multi-nucleotide variant. * There must NOT be multiple array assays (e.g., duplicates) for the locus. **Note:** The genome fasta files for human genomes are provided by Illumina on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ### **genotype help** Displays the help information for a genotype command. ### **genotype version** Displays current DRAGEN Array Local version. ### **help** Displays the first-layer help information. ### **version** Displays current DRAGEN Array Local version. ### **pgx star-allele** The root command PGx star allele calling. | Command | Description | | ------------------------ | ---------------------------------------------------- | | pgx star-allele call | Determines PGx star allele and variant genotypes. | | pgx star-allele annotate | Annotate PGx gene functions and product JSON report. | | pgx star-allele help | Displays help information for a star allele command. | | pgx star-allele version | Displays version information for star allele. | ### **pgx star-allele help** Displays help information for a star-allele command. ### **pgx star-allele version** Displays version information for star-allele. ### **pgx star-allele call** Calls PGx star allele diplotypes. The SNV VCF files should be generated using the DRAGEN Array gtc-to-vcf command with unsquash-duplicates off (default) and without filter loci. | Option | Description | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | --database | \[Required] The PGx database file (.zip). | | --license-server-url | \[Required] The license server url with credentials. | | --vcf-folder | \[Required] The directory containing \*.snv.vcf.gz and \*.cnv.vcf.gz files. | | --query-license-quota | During beginning and end of analysis, the license server will be queried for the quotas on the valid license(s) and display the result. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | ### **pgx star-allele annotate** Annotates and summarizes the star-alleles, specifically for metabolizer statuses and outputs in a consolidated JSON report. Metabolizer status is determined through direct lookup into public PGx guidelines CPIC or DPWG as specified by the user. | Option | Description | | --------------- | -------------------------------------------------------------------------------------------- | | --star-alleles | \[Required] Path to star alleles file (.csv) generated by the call subcommand. | | --guidelines | PGx guidelines to use for annotation. Valid values are ‘CPIC’ and ‘DPWG’. Default is ‘CPIC’. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele annotate command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | ### **cyto** The root command for cytogenetics CNV/LOH calling and annotation. | Command | Description | | ------------- | --------------------------------------------------------------------------- | | cyto call | Determines copy number variants and loss of heterozygosity given genotypes. | | cyto annotate | Annotates samples and generates cytogenetics json reports. | | cyto help | Display more information on a specific command. | | cyto version | Displays version information. | ### **cyto help** Display more information on a specific command. ### **cyto version** Displays version information. ### **cyto call** Determines copy number variants (CNV) and loss/absence of heterozygosity (LOH/AOH) given genotypes. | Option | Description | | ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | | --cn-model | \[Required] Path to cyto model parameters file (.dat). | | --gtc-folder | \[Required] Folder containing genotype files (.gtc). Cannot be used in conjunction with --gtc-sample-sheet. | | --gtc-sample-sheet | \[Required] Sample sheet with paths to genotype files (.gtc), can be in CSV or JSON format. Cannot be used in conjunction with --gtc-folder. | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-cnv-probes | CNV size limit (probes). Default is 10. | | --min-cnv-size | CNV size limit (kb). Default is 0. | | --min-loh-probes | LOH size limit (probes). Default is 500. | | --min-loh-size | LOH size limit (kb). Default is 3000. | | --smoothing | Smoothing window size, specifying the number of probes on each side of the center probe used for smoothing LRR values. Default is 5. | #### Notes * Greater than 10 events (DEL/DUP/AOH) per chromosome is an indication of need for visual inspection. * LogRDev > 0.2 is indicative of a low-quality sample. * VCFs generated on Windows machines will not work when manually uploading to [Emedgene](https://help.connected.illumina.com/emedgene). Only Linux-based VCFs will work. ### **cyto annotate** Annotates samples and generates cytogenetic json reports. | Option | Description | | ---------------- | ------------------------------------------------------------------------------------- | | --debug | Logs will include stack traces. Default is false. | | --help | Display this help screen. | | --json-log | Logs will be output in JSON format. Default is false. | | --annotation-db | \[Required] Database for variant annotations. | | --vcf-folder | \[Required] The directory containing the \*.cnv.vcf.gz files. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | | --min-del-probes | Deletion CNV size limit (probes). Default is 10. | | --min-del-size | Deletion CNV size limit (kb). Default is 0. | | --min-dup-probes | Duplication CNV size limit (probes). Default is 10. | | --min-dup-size | Duplication CNV size limit (kb). Default is 0. | | --min-loh-probes | LOH size limit (probes). Default is 500. | | --min-loh-size | LOH size limit (kb). Default is 3000. | | --min-qual | Min CNV qual and LOH qual scores. Default is 20. | #### Notes * The metadata "cyto.cnv.dat" file that is generated during cyto call in the vcf-folder needs to be kept in the vcf-folder for cyto annotate. * The vcfs files need to be zipped and indexed for cyto annotate, which means "--no-bgzip" flag cannot be turned on for the cyto vcf file generation if those vcf files are going to be used for cyto annotate command. * The "cyto annotate" step needs at least 5GB free space on the hard drive. * You can safely ignore the logs that say `No credential is provided`. This is a known issue described in the [1.2.0 release notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes#known-issues) ## Troubleshooting and Additional Support ### Tips for using the Command-line interface DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. When using command-line consider the following tips: * Spaces cannot be part of a file name in a command. If the file name has spaces, use quotes around the file name * To correct a typing error in a previously entered command, use the up arrow to repeat the previous command, then correct the error before re-entering it. * Double check the command. Misspelling, extra, or missing dashes, etc. will cause the command to be unrecognizable by the software. * When entering paths or long names, copy and paste the values to help avoid errors. * If using Windows, use a File Explorer window to navigate to the product file or folder that is needed by the DRAGEN Array Local command. While holding down the shift button on the keyboard, right click the file and select the 'Copy as Path' option. Then paste the copied path into the command prompt to use the file or folder. * To cancel a command while it is running, press Control + C on the keyboard. ### Optimizing cluster files and copy number models A [Cluster File](/dragen-array-v1.2/product-guides/input-files#toc150786136) (.egt) contains the cluster positions of every probe used for genotyping analysis. Illumina provides a standard cluster file for all commercial Infinium BeadChips. It may be desirable to create a custom cluster file if the one provided does not fit the data well or if a semi-custom or custom BeadChip, that do not come with a cluster file, are used. [GenomeStudio 2.0](https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) is the software used to create custom cluster files. To facilitate the review and optimization of PGx variant GenTrain cluster positions, a GenomeStudio auxiliary file is provided for each PGx Array product through the [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) and array product files page, e.g. [Infinium Global Diversity Array with Enhanced PGx Product Files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html). The auxiliary file is a tab-delimited text file that can be imported into GenomeStudio through Column Import. The file contains the Infinium Assay to PGx star allele mapping, covering the variants involved in DRAGEN Array PGx star allele calling. When updating the cluster file for pharmacogenomic applications, understand the specifications for the copy number model file before beginning. Before creating a custom cluster file, review the [Infinium Genotyping Data Analysis Technical Note](https://www.illumina.com/Documents/products/technotes/technote_infinium_genotyping_data_analysis.pdf), the [Infinium Arrays Support Webinar Video](https://youtu.be/4JTrbMUbVN0?si=ZgRDLwN6umGBhv2G), and [Custom cluster file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). A [PGx Copy Number (CN) Model File](/dragen-array-v1.2/product-guides/input-files#cn_model_file) (.dat) contains the data needed to make accurate copy number calls for pharmacogenomics. This file is used in the creation CNV VCFs which are inputs to the star allele calling command. Illumina provides a standard CN model file for all commercial PGx Infinium BeadChips. If it is determined the cluster file needs to be customized, the CN Model File should also be updated using the copy-number train command available with DRAGEN Array Local only. i.e., 1. Use GenomeStudio 2.0 to generate a new cluster file. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/new_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/new_gtcs` 3. Use the copy-number train command to retrain the copy number model. **Note: The --platform option can be found in the `Assay Format` heading value from the CSV manifest.**\ `dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/new_gtcs --platform LCG --output-folder /user/productfiles/new_cnmodel` 4. Use the `new_cnmodel` for subsequent `copy-number call` commands. Note the difference in the cluster file requirement based upon the version of DRAGEN Array used: * **Version 1.1**: If using a CN model with a different cluster file, the software will provide a warning but will proceed with copy number calling. As a result, a user can choose to keep using the commercial CN model from Illumina in combination with custom updated EGT file in the PGx analysis. * **Version 1.0**: The same cluster file used for copy number training must be used to generate GTC files for copy number calling. Otherwise, the software will produce an error and exit. For reference, see the [Command Index](#command_index_1) for details of `copy-number train` command. To retrain the CN model file, 96 samples must be used at minimum with 90 of those samples passing QC defined as Log R Dev less than or equal to 0.2. It is recommended to train with at least 150 samples. A greater number of samples can be advantageous, but diminishing returns and longer computation times are seen after 3,000 samples. It is recommended to manually QC the training samples and remove samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 so only the highest quality samples are used in the training. The same samples used to create the new cluster file should be used to retrain the CN Model. To minimize batch effect in the training sample set, the samples should be analyzed in as few batches as possible and come from the same reagent lots. The copy-number train algorithm is designed with the assumption that the copy number distribution resembles the standard population distributions. This ensures the updated CN model file is representative of the normal populations in which it will be used to calculate copy number for key pharmacogenomic targets. ### Pharmacogenomic analysis for semi-custom arrays Semi-custom arrays add additional content or other pre-designed [Infinium booster content](https://www.illumina.com/science/consortia/human-consortia.html) to enhance the commercial array content. This additional content can be analyzed for [genotyping applications](/dragen-array-v1.2/overview/our-features#toc150786108) to obtain information on SNV and indel calls. For [pharmacogenomic applications](/dragen-array-v1.2/overview/our-features#toc150786109), PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. When designing a semi-custom array using a commercial Infinium PGx array backbone, such as the Global Diversity Array with enhanced PGx, it is important to retain all backbone content in the design as removing content could decrease the quality of result. Pharmacogenomic analysis for semi-custom arrays should be run using [DRAGEN Array Local](/dragen-array-v1.2/product-guides/dragen-array-local-analysis). Because the PGx CNV calling and PGx star allele calling algorithms are only compatible with commercial product files (see [Applications](/dragen-array-v1.2/overview/our-features)), to fully analyze semi-custom PGx beadchips some steps of the pipeline can be run twice; once with the semi-custom product files (to get complete semi-custom SNV VCF files), and once with the commercial product files (to get the PGx CNV VCF files, PGx Star Allele output, and metabolizer report). The semi-custom product files can be used via the Command-line interface in `genotype call`, `genotype gtc-to-vcf`, and used in GenomeStudio, i.e., 1. Use GenomeStudio 2.0 to prepare a custom cluster file for the semi-custom array, following guidance outlined in [Custom cluster\ file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). 2. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 3. Use the genotype call command to call all semi-custom genotypes and generate custom content GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --cluster-file /user/productfiles/semi_custom_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/semi_custom_gtcs` 4. Use the genotype gtc-to-vcf command to create custom content SNV VCF files from the custom content GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --csv-manifest /user/productfiles/semi_custom_manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/semi_custom_gtcs --output-folder /user/semi_custom_vcfs` 5. Perform [Quick Start](#_toc150786126) steps 1-6 using the **commercial** Infinium PGx array product files to obtain PGx CNV VCFs, star allele calls, and metabolizer status annotations. Keep the GTC files and SNV VCF files generated using the semi-custom product files in clearly labelled folders to distinguish them from the GTC and SNV VCF files generated using the commercial product files. Note that the GTC and SNV VCFs generated using the commercial product files will not contain genotypes for the semi-custom/add-on content. The GTC and SNV VCFs generated using the semi-custom product files cannot be used for downstream PGx analysis commands. # Input Files The following section describes the input files required by DRAGEN Array.\ Product files (anything other than the IDATs) can be found on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ## IDAT Files For each sample a pair of raw intensity files (.idat) are generated from the iScan System or NextSeq550 (for select arrays). They provide intensities in the red and green channels for each probe on the Infinium array. More information on which arrays can be used with NextSeq550, can be found on the [Illumina Knowledge page on NextSeq550](https://knowledge.illumina.com/microarray/nextseq-500-550/microarray-nextseq-500-550-faq-list/000003871). An IDAT file is identified by the BeadChip Barcode (12-digit unique Sentrix ID, i.e. 123456789101), BeadChip Position (row and column of the sample, i.e. R01C01), and Grn (Green) or Red for the specific channel. ## Manifest Files The CSV and BPM manifest files can be found on the Illumina Support Site for all commercial Infinium BeadChips or on [MyIllumina](http://my.illumina.com/) for custom and semi-custom designs. DRAGEN Array only supports manifest files from the Illumina Support site. For instructions on obtaining manifest files from MyIllumina, see Illumina Knowledge article, [How to access custom array product files (manifest and product definition files) in MyIllumina](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001531). The CSV manifest file (.csv) provides complementary data to the BPM manifest file in a human readable format. It is a required input to the genotype gtc-to-vcf command to enable VCF generation for insertion/deletion variants. `gtc-to-vcf` depends on the presence of accurate mapping information within the manifest, and may produce inaccurate results if the mapping information is incorrect. Mapping information follows the implicit dbSNP standard, where * Positions are reported with 1-based indexing. * Positions in the PAR are reported with mapping position to the X chromosome. * For an insertion relative to the reference, the position of the base immediately 5' to the insertion (on the plus strand) is given. * For a deletion relative to the reference, the position of the most 5' deleted based (on the plus strand) is given. ## Cluster File The cluster file (.egt) is a standard product file provided by Illumina for commercial genotyping products and it is a required input for the genotype call command in DRAGEN Array. Custom cluster files may be required for optimal genotyping performance. See section [Optimizing cluster files and copy number models](/dragen-array-v1.2/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## PGx CN Model File The PGx CN (Copy Number) model file (.dat) is a required input to the pgx copy-number call command to enable accurate copy number calling for pharmacogenomics. Illumina provides a standard CN model file for each PGx array product. See section [Optimizing cluster files and copy number models](/dragen-array-v1.2/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## Cytogenetics Model File The cytogenetics CN (Copy Number) model file (.dat) is a required input to the cyto call command to enable accurate cytogenetics calling. Illumina provides a standard CN model file for each supported array product. For custom or other products, please contact Tech Support to request a CN model file and include the product BPM manifest. **Note:** The CN model file needs to be updated upon manifest revisions since probes can be added or removed during manifest revisions. A mismatch between the CN model file and the manifest will cause an error during `pgx copy-number call` and `cyto call`. ## Mask File The mask file (.msk) is a required input to the pgx copy-number train command to enable accurate pgx copy number training for pharmacogenomics. It does not need to be provided as an explicit input to the command line interface but should reside in the same folder as the BPM manifest. It should have the same base name as the manifest for the product. Illumina provides a mask file for each PGx array product and these can be found on the [product files support page.](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html) ## PGx Database File The PGx database file (.zip) contains the variant mapping information from Infinium PGx arrays to PGx variants. For each gene and each variant used in the star allele definitions of the gene, there is a mapping to the ID field in the SNV VCF file. Each line in the gene mapping file represents a single variant and contains the SNV VCF ID for that variant followed by the HGVS (Human Genome Variation Society) tag for the variant. The PGx database file is array specific and is one of the product files provided by Illumina for each PGx array product. ## Cytogenetics Database File The cytogenetics database file (.zip) contains information from Ensembl and RefSeq data sources used in the generation of Cytogenetics Annotation JSON File. This file can be used across products (beadchip/manifest types and versions). It is only necessary for input to local analysis (i.e., `cyto annotate`) as it is already stored in the cloud for cloud analysis. It may be updated in the future to accomodate changes in the underlying Ensembl and RefSeq datasources. ## Genome FASTA Files The genome FASTA file (.fa) is a text file with the reference genome sequences.The FASTA index file (.fai) contains metadata about chromosomal orchestration within the FASTA file for a particular species. DRAGEN Array PGx calling supports human genome build 37 and 38. The genome FASTA file and FASTA index file are both provided by Illumina for human species and should be stored together in the same input folder.\ For custom reference genomes, the contig identifiers in the provided genome FASTA file must match exactly the chromosome identifiers specified in the provided manifest. For a standard human product manifest, this means that the contig headers should read ">1" rather than ">chr1". ## IDAT Sample Sheet For local analysis, the IDAT sample sheet can be a CSV or JSON formatted file with direct paths to sample IDAT files. It enables easy analysis of samples from different directories. Example CSV format: `Green IDAT Path,Red IDAT Path` `/path/to/sample1_Grn.idat,/path/to/sample1_Red.idat` `/path/to/sample2_Grn.idat,/path/to/sample2_Red.idat` `/path/to/sample3_Grn.idat,/path/to/sample3_Red.idat` Example JSON format: `[` `{` `"Green IDAT Path": "/path/to/sample1_Grn.idat",` `"Red IDAT Path": "/path/to/sample1_Red.idat"` `},` `{` `"Green IDAT Path": "/path/to/sample2_Grn.idat",` `"Red IDAT Path": "/path/to/sample2_Red.idat"` `},` `{` `"Green IDAT Path": "/path/to/sample3_Grn.idat",` `"Red IDAT Path": "/path/to/sample3_Red.idat"` `},` `]` For cloud analysis, the IDAT sample sheet is a CSV formatted file.\ The template is available during sample selection on [BaseSpace](https://help.basespace.illumina.com/microarray/running-analysis). Here is an example:`beadChipName,sampleSectionName` `Beadchip 1 barcode (204753010023), sample section (R01C01)` `Beadchip 1 barcode (204753010023), sample section (R02C01)` `Beadchip 2 barcode (204753010024), sample section (R01C01)` `Beadchip 2 barcode (204753010024), sample section (R02C01)` For DRAGEN Array Methylation QC on cloud, additional optional sample sheet fields are available. Following Sample\_Group, any number of additional columns can be added to include meta data fields such as sex, sample type, plate and well information, etc. Additional columns added after the Sample\_Group column may have user-defined column header values. The Sample\_ID field and any additional metadata added will be replicated in the Sample QC Summary output files. The Sample\_Group field will be used to populate the PCA Control Plot within the Sample QC Summary Plots file and the Principal Component Summary file. For the PCA Control Plot, each sample group will be assigned a unique color. Samples assigned to the same Sample\_Group value will be the same color in the PCA Control Plot. `beadChipName,sampleSectionName,Sample_ID,Sample_Group,MetaData1` `Beadchip 1 barcode (204753010023), sample section (R01C01),NA1231,Group1,F` `Beadchip 1 barcode (204753010023), sample section (R02C01),NA1232,Group2,F` `Beadchip 2 barcode (204753010024), sample section (R01C01),NA1233,Group2,M` `Beadchip 2 barcode (204753010024), sample section (R02C01),NA1234,Group1,M` ## GTC Sample Sheet The GTC sample sheet is a CSV or JSON formatted file with direct paths to sample GTC files. It enables easy analysis of samples from different directories. Example CSV format: `GTC Path` `/path/to/sample1.gtc` `/path/to/sample2.gtc` `/path/to/sample3.gtc` Example JSON format: `[` `{` `"GTC Path": "/path/to/sample1.gtc"` `},` `{` `"GTC Path": "/path/to/sample2.gtc"` `},` `{` `"GTC Path": "/path/to/sample3.gtc"` `}` `]` ## Input File Summary Table In addition to the input files, there are set of intermediate files, including GTC, SNV VCF, CNV VCF and PGx CSV, which are outputs of some DRAGEN Array Local commands and inputs to other commands. The table below summarizes the input files or intermediate file, their sources, and the associated DRAGEN Array Local commands and options. | Input File | Source | Command | Option | | --------------------- | --------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------- | | IDAT | User provided from scanning instrument | genotype call | --idat-folder | | CSV Manifest | Product file from Illumina | genotype gtc-to-vcf | --csv-manifest | | BPM Manifest | Product file from Illumina |

pgx copy-number train

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

| --bpm-manifest | | Cluster File | Product file from Illumina or user created using GenomeStudio | genotype call | --cluster-file | | PGx CN Model | Product file from Illumina or user created using DRAGEN Array Local | pgx copy-number call | --cn-model | | Cytogenetics CN Model | Product file from Illumina | cyto call | --cn-model | | PGx Database | Product file from Illumina | pgx star-allele call | --database | | Cytogenetics Database | Product file from Illumina | cyto annotate | --database | | Genome FASTA | Product file from Illumina |

genotype gtc-to-vcf

pgx copy-number train

| --genome-fasta-file | | IDAT Sample Sheet | User provided | genotype call | --idat-sample-sheet | | GTC Sample Sheet | User provided |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

| --gtc-sample-sheet | | GTC | DRAGEN Array output from genotype call |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

pgx copy-number call

pgx copy-number train

| --gtc-folder | | SNV and PGx CNV VCF | DRAGEN Array output from genotype gtc-to-vcf and pgx copy-number call | pgx star-allele call | --vcf-folder | | PGx CSV | DRAGEN Array output from pgx star-allele call | pgx star-allele annotate | --star-alleles | | Cytogenetics CNV VCF | DRAGEN Array output from cyto call | cyto annotate | --vcf-folder | # Output Files The following section describes the outputs produced by DRAGEN Array. ## PGx CNV VCF File DRAGEN Array produces one PGx CNV variant call file (VCF) (\*.cnv.vcf) per sample to report the CN status on the gene and sub gene level, along with the CN events for PGx targets. The PGx CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV call quality. The CNV call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<7) are flagged by the Q7 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The PGx CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The PGx CNV VCF output file includes the following content. `##fileformat=VCFv4.1` `##source=dragena 1.1.0` `##genomeBuild=38` `##reference=file:///hg38_with_alt/hg38_nochr_MT.fa` `##FORMAT=` `##FORMAT=` `##ALT=` `##FILTER=` `##FILTER=` `##INFO=` `##INFO=` `##INFO=` `##INFO=` `##CNVOverallPloidy=1.8` `##CNVGCCorrect=True` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 204619760001_R01C01` `1 109687842 CNV:GSTM1:chr1:109687842:109693526 N 60 PASS CNVLEN=5685;PROBE=124;END=109693526;SVTYPE=CNV CN:NR 2:0.966631132771593` `4 68537222 CNV:UGT2B17:chr4:68537222:68568499 N 60 PASS CNVLEN=31278;PROBE=383;END=68568499;SVTYPE=CNV CN:NR 0:0.376696837881692` `10 133527374 CNV:CYP2E1:chr10:133527374:133539096 N 60 PASS CNVLEN=11723;PROBE=194;END=133539096;SVTYPE=CNV CN:NR 2:0.980059731860893` `16 28615068 CNV:SULT1A1:chr16:28603587:28613544 N 57 PASS CNVLEN=8315;PROBE=164;END=28623382;SVTYPE=CNV CN:NR 2:0.980552325552963` `19 40844791 CNV:CYP2A6.intron.7:chr19:40844791:40845293 N 60 PASS CNVLEN=503;PROBE=38;END=40845293;SVTYPE=CNV CN:NR 2:0.9663775484762` `19 40850267 CNV:CYP2A6.exon.1:chr19:40850267:40850414 N 60 PASS CNVLEN=148;PROBE=21;END=40850414;SVTYPE=CNV CN:NR 2:0.9663775484762` `22 42126498 CNV:CYP2D6.exon.9:chr22:42126498:42126752 N 48 PASS CNVLEN=255;PROBE=370;END=42126752;SVTYPE=CNV CN:NR 2:0.981703411438716` `22 42129188 CNV:CYP2D6.intron.2:chr22:42129188:42129734 N 10 PASS CNVLEN=547;PROBE=333;END=42129734;SVTYPE=CNV CN:NR 2:0.965498002434641` `22 42130886 CNV:CYP2D6.p5:chr22:42130886:42131379 N 60 PASS CNVLEN=494;PROBE=172;END=42131379;SVTYPE=CNV CN:NR 2:0.970341562236357` `22_KI270879v1_alt 270316 CNV:GSTT1:chr22_KI270879v1_alt:270316:278477 N 60 PASS CNVLEN=8162;PROBE=91;END=278477;SVTYPE=CNV CN:NR 2:1.01191145130511` ## Cytogenetics CNV and LOH VCF File DRAGEN Array produces one cytogenetics Variant Call File (VCF) (\*.cnv.vcf) per sample to report the CN and LOH status of the detected variants. The cytogenetics CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV/LOH call quality. The CNV/LOH call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<10) are flagged by the Q10 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The cytogenetics CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as cyto annotate. One example file can be found below: `##fileformat=VCFv4.1` `##source=dragena 1.2.0 Cyto` `##genomeBuild=37` `##product=GDACyto-8v1-0_A` `##reference=file://genome.fa` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##platform=cytoplatform` `##ALT=` `##ALT=` `##ALT=` `##FILTER=` `##FILTER=` `##INFO=` `##INFO=` `##INFO=` `##INFO=` `##CNVgenomicPloidy=1.9` `##CNVGCCorrect=True` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 208588190001_R02C01`\ `1 109687842 DEL:chr1:109687842:109693526 N 60 PASS SVLEN=5685;PROBE=99;END=109693526 GT:CN:NR:LRD 1/1:1:0.8860:0.21`\ `16 28603587 DUP:chr16:28603587:28613544 N 60 PASS SVLEN=9958;PROBE=197;END=28613544 GT:CN:NR:LRD 1/1:3:1.1666:0.11`\ `22 42129188 AOH:chr22:42129188:42129734 N 37 PASS SVLEN=547;PROBE=198;END=42129734;LOHTYPE=AOH GT:CN:NR:LRD 1/1:2:1.0208:0.25` ## SNV VCF File The software produces one genotyping variant call file (\*.snv.vcf) file per sample, covering single nucleotide variants (SNV) and indels for the sample. It reports GenCall score (GS), B Allele Frequency (BAF), and Log R Ratio (LRR) per variant. The VCF file output follows [VCF4.1 format](https://samtools.github.io/hts-specs/VCFv4.1.pdf). Some additional details: * The FILTER column is hardcoded to `PASS` and is not dependent on the `GT` value. It does not reflect the underlying quality of the call. Refer to the `GS` value for quality information. * Genotypes are adjusted to reflect the sample ploidy. Calls are haploid for loci on Y, MT, and non-PAR chromosome X for males. * Multiple SNPs in the input manifest which are mapped to the same chromosomal coordinate (e.g. tri-allelic loci or duplicated sites) are collapsed into one VCF entry and a combined genotype generated. To produce the combined genotype, the set of all possible genotypes is enumerated based on the queried alleles. Genotypes which are not possible based on called alleles and assay design limitations (e.g. Infinium II designs cannot distinguish between A/T and C/G calls) are filtered. If only one consistent genotype remains after the filtering process, then the site is assigned this genotype. Otherwise, the genotype is ambiguous (more than 1) or inconsistent (less than 1) and a no-call is returned. * Certain SNV and indel calls can be skipped when reported in the VCF. Skipped data can include unmapped loci, intensity-only probes used for CNV identification, and indels that do not map back to the genome. See [Warning/Error Messages and Logs](#toc150786153) for messages that may be seen with DRAGEN Array Local related to the skipped data. * The BAF and LRR are oriented with Ref as A and Alt as B relative to the reference genome, while GS is agnostic to the reference genome. Users familiar with GenomeStudio may observe BAF and LRR reported in the VCF as 1 minus the value reported in GenomeStudio depending on the Ref Alt allele orientation with the reference genome. GenomeStudio reports these values based on the information in the manifest without knowledge of the reference genome. * The SNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the [TBI Index File](#toc150786155). To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The SNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The SNV VCF output file includes the following content. The last row shows an example of variant call. `##fileformat=VCFv4.1` `##source=dragena 1.2.0` `##genomeBuild=38` `##reference=file:///genomes/38/genome.fa` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 202937470021_R06C01` `1 2290399 rs878093 G A . PASS . GT:GS:BAF:LRR 0/1:0.7923:0.50724137:0.14730307` ## Genotype Call (GTC) File The genotype call algorithm produces one genotype call file (.gtc) per sample analyzed. The Genotype Call (GTC) file contains the small variant (SNV and indel) genotype for each marker specified by the product and sample quality metrics. The sample marker location is not included and must be extracted from the manifest file. Binary proprietary format can be parsed using the Illumina open-source tool [BeadArray Library File Parser](https://github.com/Illumina/BeadArrayFiles). **Note on legacy GTCs:** Other Illumina software (such as AutoConvert and Beeline) also product GTC files.\ These "legacy GTC" files will work in DRAGEN Array genotyping commands such as `genotype gtc-to-vcf` but they will not work with all other downstream analyses such as [Cytogenetics (CNV and LOH) calling](/dragen-array-v1.2/overview/our-features#dragen-array---cnv-and-loh-calling) and [PGx](/dragen-array-v1.2/overview/our-features#dragen-array--pgx--star-allele-annotation). We recommend using DRAGEN Array end-to-end starting from IDATs for these analyses. ## BedGraph Files The BedGraph files contains the Log R Ratios (LRR.bedgraph) and B-Allele Frequencies (BAF.bedgraph) from the genotyping algorithm for use in visual tools. ## Star Allele CSV File The Star Allele CSV file is an intermediate file generated by the pgx star-allele call command and serves as the input to the pgx star-allele annotate command. It contains all the star allele calls for all samples in a run. Each row in the file provides either a star allele diplotype or simple variant call for a PGx-related gene. Star allele diplotype calls for a sample and a gene may span multiple lines where alternative solutions can be listed. The Star Allele CSV file also contains meta information marked by # at the top of the file for the genome build and PGx database used for the star allele calling. The star\_allele.csv file contains the following details per sample: | Field | Description | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Sentrix barcode and position of the sample. | | Rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | Gene or Variant | The gene symbol, or gene symbol plus rsID for variants. | | Type | ‘Haplotype’ (star allele) or ‘Variant’ PGx calling type. | | Solution | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | Solution Long |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | Supporting Variants |

All variants present in the array that support the star allele solution. The field has the following format: Long Solution Star Allele: (Supporting Variants).

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome, Location, Reference allele, Alternative allele, Genotype, GenCall score (GS), and B-allele frequency (BAF).

| | Missing/Masked Core Variants | All variants not present in the array or not called in the SNV VCF file for the star allele. The field has the following format: Long Solution Star-Allele: (Missing Variants). | | All Missing Variants in Array | All core definition variants that are not on the array or are not called in the SNV VCF along with the associated star alleles that are impacted. The field has the following format: Missing Variant: (List of impacted star alleles). | | Collapsed Star-Alleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | Score | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Raw Score: | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Copy Number Solution | Estimated copy number for each gene region. The field has the following format: Gene Region: Copy Number. | Below is an example of the first 4 columns from a star allele CSV file: `Sample,Rank,Gene or Variant,Type,Solution` `204650490282_R02C01,1,CYP2C9,Haplotype,*9/*11` `204650490282_R02C01,1,CYP2C19,Haplotype,*2/*10` ## Genotype Summary Files The software produces genotype summary files (gt\_sample\_summary.csv and gt\_sample\_summary.json) that contains the following details per sample: * Sample ID * Sample Name * Sample Folder * Autosomal Call Rate * Call Rate * Log R Ratio Std Dev * Sex Estimate * TGA\_Ctrl\_5716 Norm R The TGA\_Ctrl\_5716 Norm R field is specific to PGx products (e.g., Global Diversity Array with enhanced PGx). The field value is the Normalized R value of one probe and is meant as an assay control where < 1 indicates the sample failed in the TGA (Targeted Gene Amplification) process. If the product does not have this probe, it is not included in the gt\_sample\_summary. ## Final Report DRAGEN Array Cloud produces a Final Report (gtc\_final\_report.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus per sample: | Field | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | SNP Name | SNP identifier. | | SNP | SNP alleles as reported by assay probes. Alleles on the Design strand (the ILMN strand) are listed in order of Allele A/B. | | Sample ID | Sample identifier. | | Allele 1 – Top | Allele 1 corresponds to Allele A and are reported on the Top strand. | | Allele 2 – Top | Allele 2 corresponds to Allele B and are reported on the Top strand. | | Allele 1 – Forward | Allele 1 corresponds to Allele A and are reported on the Forward strand. | | Allele 2 – Forward | Allele 2 corresponds to Allele B and are reported on the Forward strand. | | Allele 1 – Plus | Allele 1 corresponds to Allele A and are reported on the Plus strand. | | Allele 2 – Plus | Allele 2 corresponds to Allele B and are reported on the Plus strand. | | GC Score | Quality metric calculated for each genotype (data point), and ranges from 0 to 1. | | GT Score | The SNP cluster quality. Score for a SNP from the GenTrain clustering algorithm. | | Log R Ratio | Base-2 log of the normalized R value over the expected R value for the theta value (interpolated from the R-values of the clusters). For loci categorized as intensity only; the value is adjusted so that the expected R value is the mean of the cluster. | | B Allele Freq | B allele frequency for this sample as interpolated from known B allele frequencies of 3 canonical clusters: 0, 0.5 and 1 if it is equal to or greater than the theta mean of the BB cluster. B Allele Freq is between 0 and 1, or set to NaN for loci categorized as intensity only. | | Chr | Chromosome containing the SNP. | | Position | SNP chromosomal position. | *Note: Analyses on products with large numbers of loci (>1 Million) and large numbers of samples (>100) yield a large (50+ Gigabyte) Final Report that are difficult to download and review. It’s recommended to create analysis configurations that do not produce this report if large batches are desired.* For more information on interpreting DNA strand and allele information, see Illumina Knowledge article [How to interpret DNA strand and allele information for Infinium genotyping array data](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001489). ## Locus Summary DRAGEN Array Cloud produces a Locus Summary (locus\_summary.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus: | Field | Description | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Locus\_Name | Locus name from the manifest file. | | Illumicode\_Name | Locus ID from the manifest file. | | #No\_Calls | Number of loci with GenCall scores below the call region threshold. | | #Calls | Number of loci with GenCall scores above the call region threshold. | | Call\_Freq | Call frequency or call rate calculated as follows: #Calls/(#No\_Calls + #Calls) | | A/A\_Freq | Frequency of homozygote allele A calls. | | A/B\_Freq | Frequency of heterozygote calls. | | B/B\_Freq | Frequency of homozygote allele B calls. | | Minor\_Freq | Frequency of the minor allele. | | Gentrain\_Score | Quality score for samples clustered for this locus. | | 50%\_GC\_Score | 50th percentile GenCall score for all samples. | | 10%\_GC\_Score | 10th percentile GenCall score for all samples. | | Het\_Excess\_Freq | Heterozygote excess frequency, calculated as (Observed -Expected)/Expected for the heterozygote class. If $f\_{ab}$ is the heterozygote frequency observed at a locus, and p and q are the major and minor allele frequencies, then het excess calculation is the following: $(f\_{ab} - 2pq)/(2pq + \varepsilon)$ | | ChiTest\_P100 | Hardy-Weinberg p-value estimate calculated using genotype frequency. The value is calculated with 1 degree of freedom and is normalized to 100 individuals. | | Cluster\_Sep | Cluster separation score. | | AA\_T\_Mean | Normalized theta angles mean for the AA genotype. | | AA\_T\_Std | Normalized theta angles standard deviation for the AA genotype. | | AB\_T\_Mean | Normalized theta angles mean for the AB genotype. | | AB\_T\_Std | Standard deviation of the normalized theta angles for the AB genotype. | | BB\_T\_Mean | Normalized theta angles mean for the BB genotypes. | | BB\_T\_Std | Standard deviation of the normalized theta angles for the BB genotypes. | | AA\_R\_Mean | Normalized R value mean for the AA genotypes. | | AA\_R\_Std | Standard deviation of the normalized R value for the AA genotypes. | | AB\_R\_Mean | Normalized R value mean for the AB genotypes. | | AB\_R\_Std | Standard deviation of the normalized R value for the AB genotypes. | | BB\_R\_Mean | Normalized R value mean for the BB genotypes. | | BB\_R\_Std | Standard deviation of the normalized R value for the BB genotypes. | | Plus/Minus Strand | Designated "+" or "-" with respect to the reference genome strand. "U" designates unknown. | ## CN Summary File The sample summary contains per sample key stats for each sample in a batch that contains the following details per sample: * Sample ID * Sample Name * Sample Folder ## Copy Number Batch File The copy number batch summary file (cn\_batch\_summary.csv) shows the total copy number gain, loss, and neutral (CN=2) values for each target region across all the samples in the analysis. Example copy number batch summary file content: `Target Region,Total CN gain,Total CN loss,Total CN neutral` `CYP2A6.exon.1,0,1,47` `CYP2A6.intron.7,0,1,47` `CYP2D6.exon.9,2,4,42` `CYP2D6.intron.2,7,2,39` `CYP2D6.p5,13,2,33` `CYP2E1,2,0,46` `GSTM1,0,42,6` `GSTT1,0,33,15` `SULT1A1,0,0,48` `UGT2B17,0,34,14` `All Target Regions,24,119,337` ## Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Manifest file used to generate GTC is not the same as the manifest file used to generate the CN model. * FASTA files and FASTA index files do not match. For the following scenarios, the software reports messages to the terminal output (as either a warning or an error): * Indel processing for GTC to VCF conversion failed. * The input folder does not contain the required input files. * An input file is corrupt. Examples of such notifications can include the following: | Error | Type | Cause | | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Failed to normalize and gencall sample: {sample\_id}, it will be skipped. Error: The given key '{loci\_id}' was not present in the dictionary. | Warning | This generally occurs because of a mismatch between the manifest (bpm) and cluster file (egt) (i.e., the cluster file was generated via a different manifest). To remedy the issue, use the manifest and cluster files intended for use together. | | Reference allele is not queried for locus: {identifier} | Warning | True reference allele does not match any alleles in the manifest. The error is common for MNVs and will be addressed in future versions of the software. | | Skipping non-mapped locus: {identifier} | Warning | Locus has no chromosome position (usually 0) These loci may be used for quality purposes or CNV calling only. | | Skipping intensity only locus: {identifier} | Warning | Similar to non-mapped loci, intensity only probes have applications outside creating variants for SNV VCFs such as CNV calling. | | Skipping indel: {identifier} | Warning | Indel context (deletion/insertion) could not be determined. | | Failed to process entry for record: {identifier} | Warning | Unable to determine reference allele for indel. | | Incomplete match of source sequence to genome for indel: {identifier} | Warning | Indel not properly mapped to the reference genome. | | Failed to combine genotypes due to ambiguity - exm1068284 (InfiniumII): TT, ilmnseq\_rs1131690890\_mnv (InfiniumII): AA, rs1131690890\_mnv (InfiniumII): AA | Warning | Detailed information about a NoCall ("./.”) in the VCF as a result of combining multiple probes that assay the same variant with conflicting results. The example here is two probes with homozygous REF genotypes (AA) and one probe with homozygous ALT probe (TT) | | Cluster file ({GTC.egt}) is not the same as CN Model Cluster file ({CN\_Model.egt}). | Warning | Cluster file used to generated GTCs used for copy number calling is not the same as was used for the GTCs used during copy number training that created the input CN model. Though CNV model is robust to minor cluster file updates, CNV training should be considered when there are significant updates in the cluster file. To remove the warning, copy number training needs to be re-run with the new GTCs generated via the new cluster file during genotyping, a different CN model with the expected cluster file needs to be used, or different GTCs should be used for copy number calling that were generated using the same cluster file as was used during the generation of the input CN model. | |

{numPassingSamples} sample(s) passed QC.

Requires at least {minPassingSamples} samples to proceed.

| Error | CNV calling is batch dependent and requires a certain number of samples with high-quality to make accurate calls. More high-quality samples need to be added to analysis batch to resolve error. | | Invalid manifest file path {manifestPath} | Error | Application could not find manifest file provided or user error. | | Failed to load cluster file: {e.Message} | Error | Corrupted file or unsupported version. | ## Star allele JSON File The star allele JSON file is produced per sample. It contains the fields present in the [star allele CSV file](#star_allele_csv) as well as additional meta data and annotations. Fields included in the star allele JSON header are described below. | Field | Description | | ------------------------- | ------------------------------------------------------------------------------------------------------------- | | softwareVersion | DRAGEN Array software version, e.g. dragena 1.0.0. | | genomeBuild | Genome build, e.g hg38. | | starAlleleDatabaseSources | Public databases with versions used as the sources of the star allele definitions and population frequencies. | | phenotypeDatabaseSources | Public databases with versions used as the sources of the star allele phenotypes. | | mappingFile | The PGx database file used for the star allele calling. | | pgxGuideline | The PGx guidelines used for metabolizer status/phenotype annotations, e.g. CPIC or DPWG | | sampleId | Sentrix barcode and position of the sample. | | locusAnnotations | The star allele call information. | Fields included in the star allele call (locusAnnotations) information are described below. | Field | Description | | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | gene | The gene symbol. | | callType | ‘Star Allele’ or ‘Variant’ PGx calling type. | | genotype | Most likely star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotypeDatabaseAnnotation | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), the variant ID (id), and the associated star allele IDs (alleleIds).

| | candidateSolutions | The set of alternative star allele calling solutions, this is only relevant for genes of the ‘Star Allele’ call type. | | missingVariantSites | All core variants that are not available (e.g. not on the array, or no calls in the SNV VCF) for star allele calling for this gene. For star alleles, the field provides an array (list) of variant "id" and impacted "alleleIds" pairs | | allelesTested | Alleles that are covered by the star allele caller. The capability to call star alleles is also dependent on array content coverage and data quality. This field is defined by the array's content and will be the same across all samples. | Fields included in the candidateSolution section, only available for star allele call type, are described below. | Field | Description | | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | genotype | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotype | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | alleles | The composite alleles of the candidate genotype solution. | | solutionLong |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), and the variant ID (id).

| | missingVariantSites | All variants not present in the array or not called in the SNV VCF file for the star allele solution. The field provides an array (list) of missing variants. | | collapsedAlleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | copyNumberRegions | Gene regions for the copy numbers listed in CopyNumberSolution. | | copyNumberSolution | Estimated copy number for each gene region listed in CopyNumberRegions | Example of JSON file content: ```json { "softwareVersion": "dragena 1.1.0+9f82ed31d8c17e42b80f67a3e2b271f1a873e1d1", "genomeBuild": "38", "starAlleleDatabaseSources": [ "PharmVar Version: 6.1", "PharmGKB Database Version: Snapshot-2024.05.16", "UGT Alleles Nomenclature: 2010.12.21", "The Human Cytochrome P450 (CYP) Allele Nomenclature Database, July 2024" ], "phenotypeDatabaseSources": [ "CPIC Database Version: 1.38.0", "DPWG Database Version: June 2023" ], "mappingFile": "DRAGENA-549-fix-annotate-sha.e56e884ed1f2d118e796cdab578ab895456bb94e.zip", "pgxGuideline": "CPIC", "sampleId": "207883050020_R08C03", "locusAnnotations": [ { "gene": "CYP2C9", "callType": "Star Allele", "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9999", "supportingVariants": [], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9999, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [], "missingVariantSites": [], "collapsedAlleles": "" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94938719T>G", "alleleIds": "*80" }, { "id": "NC_000010.11:g.94938788C>T", "alleleIds": "*83" }, { "id": "NC_000010.11:g.94938800G>A", "alleleIds": "*76" }, { "id": "NC_000010.11:g.94941975G>A", "alleleIds": "*77" }, { "id": "NC_000010.11:g.94942243T>G", "alleleIds": "*78" }, { "id": "NC_000010.11:g.94942306C>T", "alleleIds": "*72" }, { "id": "NC_000010.11:g.94942308C>T", "alleleIds": "*73" }, { "id": "NC_000010.11:g.94942309G>T", "alleleIds": "*27" }, { "id": "NC_000010.11:g.94947939G>T", "alleleIds": "*74" }, { "id": "NC_000010.11:g.94949145C>T", "alleleIds": "*82" }, { "id": "NC_000010.11:g.94949163del", "alleleIds": "*85" }, { "id": "NC_000010.11:g.94972183A>T", "alleleIds": "*81" }, { "id": "NC_000010.11:g.94981258C>T", "alleleIds": "*79" }, { "id": "NC_000010.11:g.94986136A>C", "alleleIds": "*75" }, { "id": "NC_000010.11:g.94986174G>C", "alleleIds": "*84" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*20,*21,*22,*23,*24,*25,*26,*27,*28,*29,*30,*31,*32,*33,*34,*35,*36,*37,*38,*39,*40,*41,*42,*43,*44,*45,*46,*47,*48,*49,*50,*51,*52,*53,*54,*55,*56,*57,*58,*59,*60,*61,*62,*63,*64,*65,*66,*67,*68,*69,*70,*71,*72,*73,*74,*75,*76,*77,*78,*79,*80,*81,*82,*83,*84,*85" }, { "gene": "CYP2C19", "callType": "Star Allele", "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9958", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G", "alleleIds": "*1" }, { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G", "alleleIds": "*2" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A", "alleleIds": "*2" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G", "alleleIds": "*2" } ], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9958, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "" }, { "solutionLong": "Complete: *2", "supportingVariants": [ { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "*2.001" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94762715T>C", "alleleIds": "*34" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*22,*23,*24,*25,*26,*28,*29,*30,*31,*32,*33,*34,*35,*38,*39" } ``` ### Guidance on alternative star-allele results Typically, the star allele solution with highest quality score is accepted as the final genotype (i.e. star allele diplotype) for the PGx locus. In rare cases, there are lower ranked star allele solutions with quality scores no less than 50% of the highest quality score, these lower ranked solutions are considered feasible and they are all listed in the genotype field of the locus annotation of the PGx gene in the PGx JSON file. Alternative solutions should also be considered if there are supporting variants for those solutions with low (less than 0.15) GS scores. The clustering of low GS scoring supporting variants should also be evaluated for cluster quality and any potential cluster shift. ## Cytogenetics Annotation JSON File DRAGEN Array produces one cytogenetics annotation JSON (\*.json) per sample to report more sample-level, chromosome-level, and event-level metrics and annotations. Example of JSON file content: ```json { "annotateDb": "CytoAnnotateData_DAv1.2.0.zip", "softwareVersion": "dragena 1.2.0 Cyto", "referenceGenome": "file://genome.fa", "annotationType": 0, "genomeBuild": "hg19", "databaseSources": "RefSeq (Version: GCF_000001405.40-RS_2023_10; Release Date: 2023-10-07),Ensembl (Version: 112; Release Date: 2024-05-14)", "iscnVersion": "ISCN 2020", "sampleId": "208662410005_R01C01", "gcCorrect": true, "minDelProbes": 10, "minDupProbes": 10, "minLOHProbes": 500, "minDelSize": "50kb", "minDupSize": "50kb", "minLOHSize": "3000kb", "minQual": 20, "overallPloidy": 2.012, "callRate": 0.9847303032875061, "logRDev": 0.19977793097496033, "bafDev": { "AA": 0.015344353947021572, "AB": 0.05078388443393606, "BB": 0.026002263878862304 }, "numLOHOver1M": 4, "numLOHOver8M": 3, "totalSizeLOHOver1M": 49319297, "copyNumberMedian": 2.0, "percentLOH": "1.59%", "sexEstimate": "Female", "traditionalNomenclature": "dup(2)(q32.3q33.1),dup(2)(q33.1q37.1),dup(2)(q37.1q37.3),del(2)(q37.3q37.3),del(2)(q37.3q37.3),dup(3)(p24.3p24.3),del(13)(q34q34)", "microarrayNomenclature": "1p12q21.1(120311442_144549929)x2 hmz,2q32.3q33.1(197045077_201353083)x3,2q33.1q37.1(201356309_234652155)x3,2q37.1q37.3(234653107_238195820)x3,2q37.3(238204076_238283050)x1,2q37.3(238283403_243062047)x1,3p24.3(23235392_23403815)x3,5p12q11.1(44708357_49847659)x2 hmz,11p11.2q12.1(47912150_56507812)x2 hmz,13q34(111358236_111423865)x1,Xp11.22q12(53907828_65253670)x2 hmz", "chromosomeAnnotations": [ { "id": "chr1", "numberDel": 0, "numberDup": 0, "numberLOH": 1, "numberMosaic": 0 }, ... ], "locusAnnotations": [ { "id": "AOH:1:120311442:144549929", "chrom": "chr1", "start": 120311441, "end": 144549929, "callType": "LOH", "mosaicState": false, "copyNumber": 2, "qualityScore": 35.0, "size": 24238488, "probeCount": 701, "percentHet": "1.01%", "lrrMedian": 0.05862508801510572, "lrrDev": 0.08330211160585141, "bafDev": 0.47622189059186604, "startCytoBand": "1p12", "endCytoBand": "1q21.1", "traditionalNomenclature": "N/A", "microarrayNomenclature": "1p12q21.1(120311442_144549929)x2 hmz", "geneCount": 96, "genes": [ "HMGCS2", "REG4", "NBPF7P", "PFN1P9", "NOTCH2P1", "ADAM30", "RP5-1042I8.7", "NOTCH2", "RP11-114O18.1", ... ] }, ... ] } ``` The fields in the annotation JSON for each sample are described as follows. | Field | Description | | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | annotateDb | File name of the database annotation file. | | softwareVersion | Version of DRAGEN Array used for the analysis. | | referenceGenome | File name of the reference genome. | | annotationType | Integer representing annotation methodology where 0=Constitutional and 1=Oncology. | | genomeBuild | Genome build e.g. hg19, hg38. | | databaseSources | Release versions of annotation data. | | iscnVersion | Release date of ISCN formatting used. | | sampleId | ID string assigned to the sample. | | gcCorrect | Boolean indicating whether GC correction was enabled. | | minDelProbes | Deletions must contain this many probes to be reported. | | minDupProbes | Duplications must contain this many probes to be reported. | | minLOHProbes | LOH variants must contain this many probes to be reported. | | minDelSize | Minimum length filter for reporting deletions in kilobases (kb). | | minDupSize | Minimum length filter for reporting a duplication in kb. | | minLOHSize | Minimum length filter for reporting a loss-of-heterzygozity (LOH) variant in kb. | | minQual | Minimum quality score filter for reporting a variant. | | overallPloidy | Arithmetic mean of the ploidy across the genome. This value accounts for the length of all variant calls. The baseline ploidy value without any variants will differ by sex. | | callRate | Frequency of expected calls i.e. #Calls/(#No\_Calls + #Calls). | | logRDev | Standard deviation of the Log R ratio values for all probes. | | bafDev | Standard deviation of the B allele frequency values for each assigned genotype (AA/AB/BB). | | numLOHOver1M | Count of LOH variants detected > 1 Mbp in length. | | numLOHOver8M | Count of LOH variants detected > 8 Mbp in length. | | totalSizeLOHOver1M | Cumulative length of all detected LOH variants > 1 Mbp in length. | | copyNumberMedian | Length-normalized genome-wide median copy number value. Copy number values are assigned to contiguous segements of variable size in the genome by the algorithm. Segments greater than 1 kB in length, including variant and expected copy number values, are aggregated to calculate this median. | | percentLOH | Percent of the genome comprised of LOH variants. | | sexEstimate | Detected sex of the sample. | | traditionalNomenclature | Simplified ISCN format designation for all detected variants in the sample. | | microarrayNomenclature | ISCN format designation for all detected variants in the sample. | | chromosomeAnnotations | Counts of each type of variant detected per chromosome, including mosaic calls. | | locusAnnotations | Locus level statistics (see additional table for locus-level statistics). | The fields within each variant (CNV/LOH event) under the locusAnnotations field of the Cyto annotation JSON are described below. | Field | Description | | ----------------------- | -------------------------------------------------------------------------------------- | | id | Unique variant ID containing variant type, chromosome and the start and end positions. | | chrom | Chromosome of the variant. | | start | Variant start position. | | end | Variant end position. | | callType | Variant class (DEL, DUP, LOH). | | mosaicState | Boolean indicating whether the locus is a mosaic variant. | | copyNumber | Copy number of the locus. | | qualityScore | Phred-scaled score of the variant call quality. | | size | Length of the variant. | | probeCount | Number of probes contained in the called variant region. | | percentHet | Percent of probes in the region call as heterzygous i.e. AB. | | lrrMedian | Median log R ratio value of the probes within the variant. | | lrrDev | Standard deviation of the log R ratio values of the probes within the variant. | | bafDev | Standard deviation of the B allele frequency values of the probes within the variant. | | startCytoBand | Cytoband in which the variant starts. | | endCytoBand | Cytoband in which the variant ends. | | traditionalNomenclature | Simplified ISCN format designation for the detected variant. | | microarrayNomenclature | ISCN format designation for the detected variant. | | geneCount | Count of annotated genes within the variant region. | | genes | List of names of all annotated genes within the variant region. | ## TBI Index File The TBI (TABIX) index file is associated with the bgzipped VCF files. It allows for data line lookup in VCF files for quick data retrieval. The format is a tab-delimited genome index file developed by Samtools as part of the HTSlib utilities. For more information, visit the [Samtools](http://www.htslib.org/doc/tabix.html) website. ## Methylation Control Probe Output File The software produces a control probe output file ({BeadChipBarcode}\_{Position}\_ctrl.tsv.gz) per sample that includes the raw methylated and unmethylated values for each control probe. Each control probe has an address, type, color channel, name, and probe ID. It also provides the raw signal for methylated green (MG), methylated red (MR), unmethylated green (UG) and unmethylated red (UR). The file can help identify which probes are available on a given BeadChip. ## Methylation CG Output File The software produces a CG output file ({BeadChipBarcode}\_{Position}\_cgs.tsv.gz) per sample that includes beta values, m-values and detection p-values for each CG site. Beta values measure methylation levels in a linear fashion for easy interpretation. Unmethylated probes are close to zero and methylated probes are close to 1. M-values are a log transformed beta value which provides a more representative measure of methylation. Detection p-values measure the likelihood that the signal is background noise. It is recommended that p-value >0.05 are excluded from analysis as they are likely background noise. see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note for further detail on calculation of these metrics. ## Methylation Sample QC Summary Files The software produces methylation sample QC summary in .xlsx and .tsv file formats (sample\_qc\_summary.xlsx and sample\_qc\_summary.tsv) per analysis batch, which provides per sample QC data for all samples in the batch. The QC summary provides details on 21 controls metrics (see tables below), which are computed in same way as in the BeadArray Controls Reporter software from Illumina. In addition, it provides average red and green raw and normalized signals, time of scanning, proportion of probes passing, overall sample pass/fail status, and the failure codes for control metrics that did not pass. The sample pass status is defined as the passing of all 21 control metrics. The QC summary .xlsx file further highlights failing parameters for easy viewing. The QC summary files contain the following fields: | Field | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Sentrix\_ID | 12-digit BeadChip Barcode associated with the sample. | | Sentrix\_Position | Row and column on the BeadChip ie R01C01 | | Sample\_ID | Optional field that can be indicated using IDAT Sample Sheet | | User Defined Meta Data | Optional field(s) that can be indicated using IDAT Sample Sheet. Any number of fields indicated will appear in this output file. | | restoration |
  • The default threshold is 0.
  • If using the FFPE DNA Restore Kit, the restoration control identifies success of the FFPE restoration chemistry. Change the threshold from 0 to 1 if the FFPE DNA Restore Kit was used.
  • The green channel intensity is higher than Background. Therefore, the metric provided is the Green Channel Intensity/Background.
| |

staining\_green

staining\_red

|
  • Staining controls are used to examine the efficiency of the staining step in both the red and green channels. These controls are independent of the hybridization and extension step.
  • The green channel shows a higher signal for biotin staining when compared to biotin background, whereas the red channel shows higher signal for DNP staining when compared to DNP background.
  • The metric provided for green is the (Biotin High value)/ (Biotin Bkg) and the metric provided for red is (DNP High value)/(DNP Bkg value)
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

extension\_green

extension\_red

|
  • Extension controls test the extension efficiency of A, T, C, and G nucleotides from a hairpin probe, and are therefore sample independent.
  • In the green channel, the lowest intensity for C or G is always greater than the highest intensity for A or T.
  • The metric provided is the (lowest of the C or G intensity)/ (highest of A or T extension) for a single sample.
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

hybridization\_high\_medium

hybridization\_medium\_low

|
  • Hybridization controls test the overall performance of the Infinium Assay using synthetic targets instead of amplified DNA. These synthetic targets complement the sequence on the array, allowing the probe to extend on the synthetic target as a template. Synthetic targets are present in the Hybridization Buffer at 3 levels, monitoring the response from high-concentration (5 pM), medium concentration (1 pM), and low concentration (0.2 pM) targets. All bead type IDs result in signals with various intensities, corresponding to the concentrations of the initial synthetic targets.
  • The value for high concentration is always higher than medium and the value for medium concentration is always higher than low.
  • The metric provided is the value of high/medium and the value of medium/low.
  • The default thresholds are 1. Do not change the default threshold.
| |

target\_removal1

target\_removal2

|
  • Target removal controls test the efficiency of the stripping step after the extension reaction. In contrast to allele-specific extension, the control oligos are extended using the probe sequence as a template. This process generates labeled targets. The probe sequences are designed such that extension from the probe does not occur. All target removal controls result in low signal compared to the hybridization controls, indicating that the targets were removed efficiently after extension. Target removal controls are present in the Hybridization Buffer.
  • The Background for the same sample is close to or larger than either control.
  • The metric provided is Background/Control Intensity.
  • The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion1\_green

bisulfite\_conversion1\_background\_green

bisulfite\_conversion1\_red

bisulfite\_conversion1\_background\_red

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium I probe design and allele-specific single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "C" (Converted) probes matches the converted sequence and get extended. If the sample has unconverted DNA, the "U" (Unconverted) probes get extended. There are no underlying C bases in the primer landing sites, except for the query site itself.

  • The calculation is done in both the green and red channels separately to provide 2 unique sets of values:

    • Green Channel

      • Lowest value of C1 or C2 / Highest value of U1 or U2. The default threshold is 1. This value can be increased for some scanners.
      • Background/(U1, or U2). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.

    • Red Channel

      • Lowest value of C3, 4, or 5 / Highest value of U3, 4, or 5. The default threshold is 1. This value can be increased for some scanners.
      • Background /(Highest value of U4, U5, or U6). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion2

bisulfite\_conversion2\_background

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium II probe design and single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "A" base gets incorporated and the probe has intensity in the red channel. If the sample has unconverted DNA, the "G" base gets incorporated across the unconverted cytosine, and the probe has elevated signal in the green channel.
  • The calculation is done using both channels for 1 set of numbers returned.

  • The following metrics are provided:

    • (Lowest of red C 1, 2, 3, or 4) / (Highest of green C 1, 2, 3, or 4). The default threshold is 1. This value can be increased for some scanners.
    • Background/(Highest C1, C2, C3, or C4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

specificity1\_green

specificity1\_red

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor allele-specific extension for Infinium I probes. The methylation status of a particular cytosine is carried out following bisulfite treatment of DNA by using query probes for unmethylated and methylated state of each CpG locus. In assay oligo design, the A/T match corresponds to the unmethylated status of the interrogated C, and G/C match corresponds to the methylated status of C. G/T mismatch controls check for nonspecific detection of methylation signal over unmethylated background. PM controls correspond to A/T perfect match and give high signal. MM controls correspond to G/T mismatch and give low signal.
  • The metrics provided are the ratio of the lowest PM/highest MM in each channel.
  • The default threshold is 1. Do not change the default threshold.
| |

specificity2

specificity2\_background

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor extension specificity for Infinium II probes and check for potential nonspecific detection of methylation signal over unmethylated background. Specificity II probes incorporate the "A" base across the nonpolymorphic T and have intensity in the Red channel. If there was nonspecific incorporation of the "G" base, the probe has elevated signal in the Green channel.

  • The following metrics are provided:

    • (Lowest intensity of S1, S2, or S3 red) / (Highest intensity of S1, S2, or S3 green). The default threshold is 1. Do not change the default threshold.
    • Background/(Highest intensity S1, S2, S3, or S4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

nonpolymorphic\_green

nonpolymorphic\_red

|
  • Nonpolymorphic controls test the overall performance of the assay, from amplification to detection, by querying a particular base in a nonpolymorphic region of the genome. They let you compare assay performance across different samples. One nonpolymorphic control has been designed for each of the 4 nucleotides (A, T, C, and G).
  • In the green channel, the lowest intensity of C or G is always greater than the highest intensity of A or T.
  • The metric provided is the (lowest intensity for C or G) /(highest intensity for A or T) for a single sample.
  • The default threshold is 5. This value can be increased for some scanners.
| |

avg\_green\_raw

avg\_red\_raw

|
  • Average green and red raw signal for the given sample.
| |

avg\_green\_norm

avg\_red\_norm

|
  • Average green and red signal after dye bias correction and noob normalization for the given sample.
| | ScanTime |
  • The date (MM/DD/YY) and time (HH:MM) that the sample was scanned by the iScan system.
| | NProbes |
  • Number of probes on the BeadChip, including SNP and CG probes
| | NPassDetection |
  • Number of probes on the BeadChip that passed detection p-value at the threshold defined.
| | prop\_probes\_passing |
  • The proportion of probes passing defined as the number of probes passing detection p-value divided by the total number of probes on the BeadChip.
| | passQC |
  • 1 = sample passed all QC metrics for the thresholds defined
  • 0 = sample did not pass all QC metrics for the thresholds defined
| | failCodes |
  • The list of parameters that failed QC for the thresholds defined.
| The control metrics in the QC summary files are calculated as following. The default value for background correction offset (x) of 3,000 can be modified and applies to all background calculations indicated with (bkg + x). Note that the table uses default thresholds for EPIC arrays as example, the default thresholds changes with the methylation arrays. See section [Threshold Adjustment](/dragen-array-v1.2/product-guides/dragen-array-cloud-analysis#methylation-qc-threshold-adjustment) for additional details.
ControlCalculationAdditional Information
Restoration Green > bkg(Green/(bkg+x))> 0
  • If using the FFPE Restore kit, change the default threshold from 0 to 1.
  • bkg = Extension Green highest A or T intensity

Staining Green

Biotin High > Biotin Bkg

(High/Biotin Bkg) > 5

Staining Red

DNP High > DNP Bkg

(High/DNP Bkg) > 5
Extension Green Lowest CG/Highest AT(C or G/A or T) > 5Green channel—Lowest C or G intensity is used; highest A or T intensity is used.

Extension Red

Lowest AT/Highest CG

(A or T/C or G) > 5Red channel—Lowest A or T intensity is used; highest C or G intensity is used.
Hybridization Green High > Medium > Low(High/Med) > 1
(Med/Low) > 1
Target Removal Green ctrl 1 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity
Target Removal Green ctrl 2 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity

Bisulfite Conversion I Green

C1, 2 > U1, 2

(C/U) > 1
  • Lowest C intensity is used. Highest U intensity is used.

Bisulfite Conversion I Green

U ≤ bkg

((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Green channel—bkg = Extension Green highest AT
Bisulfite Conversion I Red C3, 4, 5 > U3, 4, 5(C/U) >1
  • Lowest C intensity is used. Highest U intensity is used.
Bisulfite Conversion I Red U ≤ bkg((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Red Channel—bkg = Extension Red highest CG
Bisulfite Conversion II C Red > C Green(C Red/ C Green) > 1
  • For MSA arrays, the default is 0.5
  • Lowest C Red intensity is used. Highest C Green intensity is used.
Bisulfite Conversion II C green ≤ bkg((bkg + x)/C Green) > 1
  • For MSA arrays, the default is 0.5
  • Highest C Green intensity is used.
  • Green channel—bkg = Extension Green highest AT
Specificity I Green PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used
Specificity I Red PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used

Specificity II

S Red > S Green

(S Red/ S Green) > 1
  • Lowest S Red intensity is used. Highest S Green intensity is used.

Specificity II

S Green ≤ bkg

((bkg + x)/ S green) > 1
  • bkg = Extension Green highest A or T intensity
  • Highest S Green intensity is used.
Nonpolymorphic Green Lowest CG/ Highest AT(C or G/ A or T) > 5
  • Lowest C or G intensity is used; highest A or T intensity is used
  • For MSA arrays, the default threshold is 2.5
Nonpolymorphic Red Lowest AT/ Highest CG(A or T/ C or G) >5
  • Lowest A or T intensity is used; highest C or G intensity is used
  • For MSA arrays, the default threshold is 3
## Methylation Sample QC Summary Plots The software produces methylation sample QC summary plots (sample\_qc\_summary.pdf) per analysis batch which provides visual depictions of two QC summary plots for quick visual review. The file contains the following control plots: | Control Plot | Description | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Proportion of Probes Passing Threshold | Histogram of the proportion of probes passing the p-value detection threshold. Samples passing QC are shown in one color, and samples failing QC are shown in another color. | | Principal Component Analysis (PCA) | Uses beta values for all analytical probes to compare samples. Principal component analysis (PCA) is applied to the beta values to reduce the dimensionality of the data to two “principal components” that reflect the most variation across samples. If more than 100 samples are used in the analysis, a random subset of 10,000 probes are used for the PCA analysis to reduce computational burden. PCA control plot assigns unique colors to each sample group defined by the IDAT Sample Sheet. If no groups were assigned, all samples will appear the same color. Sample groups may cluster together and can be used to explain some of the variation. Coordinates used to plot each sample in the PCA control plot are provided in the pcs.tsv.gz output file (see below). | ## Methylation Principal Component Summary The software produces a methylation principal component summary file (pcs.tsv.gz) per analysis batch which provides principal component data for each sample within the batch. This can be used to identify the specific samples associated with points on the PCA control plot within the Methylation Sample QC Control Plots output file. The files contain the following fields: | Field | Description | | --------------------- | -------------------------------------------------------------------------------------------------------------------- | | blank | BeadChip Barcode and Position ie 123456789101\_R01C01 | | principal component 1 | The variable of the first axis for the Principal Component Analysis | | principal component 2 | The variable of the second axis for the Principal Component Analysis | | Sample\_Group | Sample group defined by the user in the IDAT Sample Sheet. If no sample group was defined, all samples will show NA. | ## Methylation Manifest Files The software produces two methylation manifest files 1. Manifest in Sesame format (probes.csv) 2. Additional information for control probes (controls.csv) The probes.csv file has the following columns: | Field | Description | | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | | U | This is corresponds to the AddressA\_ID column in the standard Illumina manifest format. | | M | This corresponds to the AddressB\_ID column in the standard Illumina manifest format. | | col | This is the color channel for Infinium I probes (R/G). For Infinium I probes, this column will be NA. | The controls.csv file has the following columns: | Field | Description | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Address | The address of the probe | | Type | The control probe type | | Color\_Channel | A color used to denote certain control probes in legacy software | | Name | A human readable identifier for certain control probes | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | ## Methylation Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Missing IDATs or manifest * Incorrect sample sheet formatting * Duplicate BeadChip Barcode and Position within the sample sheet * Missing control or assay probes * Missing required columns in the manifest * Unable to compute certain metrics Examples of such notifications can include the following: | **Log** | **Error** | **Type** | **Cause** | | ----------------------------------- | ---------------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | write\_samplesheet.log | No IDATs found | Error | No IDATs provided for analysis | | format\_samplesheet.log | No samples in sample sheet | Error | No samples in user’s sample sheet input | | format\_samplesheet.log | Sample sheet not correctly formatted | Error | Sample sheet is not in CSV format or header lines do not start with “<” | | format\_samplesheet.log | beadChipName and sampleSectionName columns are required for the sample sheet. | Error | Sample sheet does not contain required columns: beadChipName and sampleSectionName. | | format\_samplesheet.log | Warning: \ samples have duplicate Sample\_ID | Warning | X lines in the sample sheet have duplicate \\_\. Duplicates are dropped from analysis. | | convert\_manifest\_ilmn\_sesame.log | Missing control probes in manifest | Error | Missing “\[Controls]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Probe section not found | Error | Missing “\[Assay]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Missing required columns: IlmnID, AddressA\_ID, AddressB\_ID, Color\_Channel | Error | Missing one of required columns in Assay section of manifest | | convert\_manifest\_ilmn\_sesame.log | Controls not formatted correctly. Must have 4 columns (Address,Type,Color\_Channel,Name) | Error | Missing one of required columns in Control section of manifest | | run\_sesame\_gs.log | Missing sample: \ | Error | Missing idats for a particular sample | | run\_sesame\_gs.log | No scan time available | Warning | No scan time in idat | | run\_sesame\_gs.log | Prep failed | Error | Dye bias correction or noob failure for sample | | run\_sesame\_gs.log |

Warning: missing control probe types

\

| Warning | Missing control probe types to compute a BACR metric. Metric will be set to NA. | | run\_sesame\_gs.log |

Warning: missing control probe names

\

| Warning | Missing control probes to compute a BACR metric. Metric will be set to NA. | | qc.log | No features, skipping PCA plot | Warning | No common betas found in all samples. This may occur if a sample has no signal intensity in the IDAT files. | [^1]: If using the FFPE Restore kit, change the default threshold from 0 to 1. [^2]: For MSA arrays, the default is 0.5. [^3]: For MSA arrays, the default is 2.5. [^4]: For MSA arrays, the default is 3. # Support and Additional Resources ## Technical Support For support, questions, and feedback on DRAGEN Array, please contact Illumina Tech Support at . ## Additional Resources | Resource | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | [DRAGEN Array Webpage](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html) | Product features and benefits and allows product ordering. | | [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) | Support site for DRAGEN Array which includes installers and product documentation. | | [DRAGEN Array Methylation QC analysis](https://developer.illumina.com/news-updates/dragen-array-1-0-now-supporting-methylation-qc-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 Methylation QC. | | [DRAGEN Array PGx Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 PGx analysis. | | [Infinium Lab Setup and Best Practices](http://support-docs.illumina.com/ARR/infinium-labsetup.htm) | Lab setup and maintenance information for Infinium assays. | | [Infinium Assay Consumables & Equipment List](http://support-docs.illumina.com/ARR/infinium-consumables.htm) | List of consumables and equipment used in Infinium assays. | | [iScan System Product Documentation](http://support-docs.illumina.com/ARR/iscan.htm) | Instructions for operating and maintaining the iScan System. | | [Polygenic Risk Score – Predict](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) | Instructions for using the Polygenic Risk Score – Predict Module. | | [Illumina Connected Analytics](https://help.connected.illumina.com/illumina-connected-analytics) | Instructions for using the hosted environment Illumina Connected Analytics. | | [BaseSpace Sequence Hub](https://help.basespace.illumina.com/) | Instructions for using the hosted environment BaseSpace Sequence Hub. | | [Emedgene](https://help.connected.illumina.com/emedgene) | Instructions for using Emedgene software | ### # Frequently Asked Questions 1. **Is DRAGEN Array analysis a local (on-premises) or cloud solution?**\ DRAGEN Array analysis is available locally (on-premises) and cloud. DRAGEN Array Local Analysis utilizes a command-line interface for power users to have granular control and flexibility to support large scale microarray genomic studies. Deployed on Windows or Linux operating systems, the local package is CPU-based and does not require a specialized server or hardware. DRAGEN Array Cloud Analysis utilizes the user-friendly, graphical interface of BaseSpace Sequence Hub to simplify analysis setup and kickoff. 2. **Which Infinium arrays is DRAGEN Array compatible with?**\ Refer to the Product and Analysis Compatibility table in the [Applications](/dragen-array-v1.2/overview/our-features) section. 3. **How many samples are needed per analysis?**\ **Genotyping:** As few as one sample can be used for genotyping. Multiple analysis batches can be kicked off and run in parallel. **Pharmacogenomics:** A minimum of 24 samples is required for PGx CNV calling with 22 passing QC. Passing QC is defined as Log R Dev < 0.2. 96 samples are recommended for the most accurate CNV results. Multiple analysis batches can be kicked off and run in parallel. 4. **Which PGx CNVs and star alleles are available?**\ Please refer to the DRAGEN Array [release notes](/dragen-array-v1.2/reference/release-notes). 5. **Where can I find demo data?**\ Demo data is available in BaseSpace under the “Demo Data” section. All array data starts with “iScan:” and includes the name of the type of analysis. Supported types of analysis can be found in the [Applications](/dragen-array-v1.2/overview/our-features) section. # Release Notes The following versions of DRAGEN Array have been released: * [DRAGEN Array v1.2.0 Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes) * [DRAGEN Array v1.2.0 EMGv38 Automatic Case Creation Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes/dragen-array-v1.2.0-cyto-emg-release-notes) * [DRAGEN Array v1.1.0 Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.1.0-release-notes) * [DRAGEN Array v1.0.0 Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.0.0-release-notes) * [DRAGEN Array Genotyping Cloud v1.0.0 Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-genotype-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-methylqc-release-notes) # DRAGEN Array v1.2.0 Release Notes ## **RELEASE DATE** February 2025 ## **RELEASE HIGHLIGHTS** * Whole-genome copy number and loss of heterozygosity (LOH) calling, with VCF output format, for any human genotyping array. * B-allele frequency bedgraph output file to power informative CNV visualizations. * Additional outputs including ISCN and cytoband nomenclature to support cytogenetics applications. ## **NEW FEATURES IN DETAIL** * Cytogenetic CNV and LOH Calling and VCF Output * Ability to obtain output files for any human genotyping array. Detection abilities vary by array probe density and spacing. * Detects copy number up to 4+. * Provides Phred scaled quality score to assess the event quality. * Addition of mosaic tagging to detect mosaic deletions and duplications. * Three arrays tested for performance including: * Infinium Global Diversity Array with Cytogenetics-8 * Infinium Global Screening Array with Cytogenetics-24 * Infinium CytoSNP-850K BeadChip using the iScan System * Ability to adjust minimum size and probe number for copy number and LOH event calling * BAF and LRR Bedgraph files * Additional bedgraph file output for B-allele frequency (BAF) for use in visualization. Updated file extensions to differentiate BAF.bedgraph and LRR.bedgraph files. * Added a smoothing parameter to the genotype gtc-to-bedgraph command for LRR.bedgraph (log R ratio bedgraph file) generation for improved visualization. * Bedgraph files are compatible with IGV (Integrative Genomics Viewer) for visualization purposes. * Cytogenetic annotation and JSON Output * Provides summary statistics per sample and per CNV/LOH event. Includes gene count and gene names within each event based on the RefSeq database. * Annotates each event using International System for Human Cytogenomic Nomenclature (ISCN) 2020 and cytoband nomenclature based on Ensembl database. * Pharmacogenomics * Added root command *pgx* for grouping PGx copy number and star allele calling. * Fixed issue causing pgx star-allele annotate command to fail mid-analysis from version 1.1. ## **KNOWN ISSUES** * If a sample's sex estimate is called as unknown in the genotyping module, the cytogenetic caller will assume the sample is male. Consequently, detection results on sex chromosomes could be inaccurate if the sample is actually female. * ISCN annotations in the cytogenetic annotation JSON output file are only provided for variants greater than 1 Kb in length. This is often cited as a minimum size limit used to define copy number variants. * Centromere regions typically have low sequence complexity and are prone to artifacts. As a result, cytogenetic calling results in these regions are likely to be false positives. * The `cyto annotate` subcommand produces extraneous logs (e.g., `No credential is provided`) that can be safely ignored. * During `cyto call`, there is a log for the `CytoPlatform` currently hardcoded to `LCG` regardless of the product used. This has no bearing on the underlying algorithm and is just what is reported in the log. It can be safely ignored. * A non-default value of the `--smoothing` parameter for the [genotype gtc-to-bedgraph](/dragen-array-v1.2/product-guides/dragen-array-local-analysis#genotype-gtc-to-bedgraph) command triggers a bug causing wrong values in the LogR Ratios (LRR) bedgraph. It is advised users use the default (0), which produces a valid LRR bedgraph with raw signal for visualization purposes. The --smoothing parameter will be disabled in next release of DRAGEN Array. * The `cyto call` command may throw an overflow error in very rare cases when no variants are detected in noisy or low-quality samples. Contact if you encounter this issue. ## **KNOWN LIMITATIONS** * DRAGEN Array CNV and LOH calling is intended for constitutional samples only, oncology samples not supported at this time. * DRAGEN Array CNV and LOH calling was only validated for specific array platforms (Infinium Global Diversity Array with Cytogenetics-8, Infinium Global Screening Array with Cytogenetics-24, Infinium CytoSNP-850K BeadChip using the iScan System). * DRAGEN Array CNV and LOH calling may call large events that are broken into smaller pieces and require visual confirmation. * DRAGEN Array CNV and LOH calling does not product mosaic fraction estimation or mosaic ISCN notation at this time. * When using CytoSNP-850Kv1-4\_iScan\_B, GSACyto-24v1\_20044998\_C, or GDACyto-8v1-0\_20047166\_E manifests, DRAGEN Array CNV and LOH calling will be unable to call events or visualize probes in the PAR (pseudo-autosomal regions). Please reach out to for additional details. * GT is hardcoded to homozygous alt (1/1) for cyto VCF entries. * IDATs originating from NextSeq550 not tested. # DRAGEN Array v1.2.0 EMGv38 Automatic Case Creation Release Notes ## **RELEASE DATE** June 2025 ## **RELEASE HIGHLIGHTS** * Automatic case creation in Emedgene (EMG) following the successful completion of a `DRAGEN Array - Cytogenetics analysis + Emedgene interpretation` analysis from Basespace (powered by ICA). ## **NEW FEATURES IN DETAIL** * See existing features for DRAGEN Array CNV and LOH calling in the [1.2.0 release notes](/dragen-array-v1.2/reference/release-notes/dragen-array-v1.2.0-release-notes) * For more details on the EMGv38 features, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN ISSUES** * See existing issues for DRAGEN Array CNV and LOH calling in the [1.2.0 release notes](/dragen-array-v1.2/reference/release-notes) * For more details on the EMGv38 known issues, see these [release notes](https://help.emg.illumina.com/release-notes/workbench-and-pipeline-updates/new-in-emedgene-v38.0-june-3rd-2025). ## **KNOWN LIMITATIONS** Please see the [Prerequisites](/dragen-array-v1.2/product-guides/dragen-array-cloud-analysis#prerequisites) section in the cloud setup page for detailed guidance on how to setup this analysis. The following limitation applies if these prerequisites are not met: * The "DRAGEN Array - Cytogenetics analysis + Emedgene interpretation" analysis type is available to all user regardless of the EMG subscription status or SNS notification settings. The software does not enforce the presence of EMG subscription in the workgroup. Without EMG subscription and SNS configuration, the analysis will start as normal, and behave identical to the "DRAGEN Array – CNV and LOH Calling" analysis type, although no "Automatic Case Creation on EMG" will occur. # DRAGEN Array v1.1.0 Release Notes ## **RELEASE DATE** September 2024 ## **RELEASE HIGHLIGHTS** * New EX PGx beadchips enabled for PGx analysis * Increased coverage of high priority PGx genes * Custom optimized .egt files accepted in PGx analysis * Up-to-date database reflecting latest versions of public PGx resources * DPWG guidelines now available for metabolizer status calling on cloud analysis ## **NEW FEATURES IN DETAIL** * DRAGEN Array supports multiple PGx products * Two new EX PGx beadchips enabled through genotyping, PGx CNV calling, and star allele annotation * Infinium Global Screening Array with Enhanced PGx-48 v4.0 Kit * Infinium Global Clinical Research Array with Enhanced PGx-24 v1.0 Kit * In total 3 PGx products supported: GDA-ePGx, GSAv4-ePGx, GCRA-ePGx. See the [Product & Analysis Compatibility table](/dragen-array-v1.2/overview/our-features#product--analysis-compatibility) for more details. * Increased coverage of high priority PGx genes * Star allele annotation now covers CYP2E1, CYP1A2, ABCG2, CYP2C8, HMGCR, UGT1A4, UGT2B15, F13A1, and HLA-B\*15:02 * CNV calling now covers SULT1A1 * Extended bi-allelic PGx variants from source databases to multi-allelic variants based on the designs in the supported PGx products. * See [PGx Star Allele Coverage](/dragen-array-v1.2/reference/pgx-star-allele-coverage) and [PGx CNV Coverage](/dragen-array-v1.2/reference/pgx-cnv-coverage) for the full coverage lists. * Allows flexibility for GTCs generated with a custom cluster file (.egt) to be used with the commercial CN model file (.dat). This alleviates the burden to retrain the CN model file. * The cluster file is a required input for the genotype call command in DRAGEN Array. The CN (Copy Number) model file is a required input to the copy-number call command to enable accurate copy number calling for pharmacogenomics. Custom cluster files and CN model files may be required for optimal genotyping and PGx performance. See section Optimizing cluster files and copy number models for additional details. * Database revision reflecting [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.2/reference/pgx-allele-definitions-and-pgx-guidelines) updates. * Standardization of star allele JSON output file * Renamed databaseSources to phenotypeDatabaseSources and starAlleleDatabaseSources * Renamed Phenotype to PhenotypeDatabaseAnnotation * Combined missingVariants and allMissingVariants to missingVariantSites * JSONized supportingVariants and missingVariants at the gene and candidate solution allele levels * Removed redundant info in the Alleles fields * Updated VCF tabix indexing, improving performance and disk usage for SNV VCF. ## **KNOWN ISSUES** * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report * Occasional star-allele solution score discorcordance between Linux and Windows OS with concordant solution ranking. * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To workaround the issue, user should restart star allele calling or run it on a machine with more memory. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. * Star allele annotation can fail mid-analysis in rare circumstances when a particular allele is unknown (e.g. for CYP2E1). The observed cases have all been mis-calls for CYP2E1 due to cluster drift. See [Optimizing cluster files](/dragen-array-v1.2/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for more details. ## **KNOWN LIMITATIONS** * Star allele calling does not support novel alleles but those defined in the PharmVar and PharmGKB databases. * CYP2D6 non-\*36 star alleles with exon 9 conversion, such as \*83, are reported as \*36 with \*83 as an underlying allele. * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * DRAGEN Array were only validated and intended to be used for commercial PGx beadchips with specified manifests (see table above). PGx star allele annotation is not backwards compatable with v1.0 manifest version, e.g., GDA\_PGx-8v1-0\_20042614\_E2 is supported in DRAGEN Array v1.0, GDA\_PGx-8v1-0\_20042614\_G2 is supported in DRAGEN Array v1.1. * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. # DRAGEN Array v1.0.0 Release Notes ## **RELEASE DATE** December 2023 ## **RELEASE HIGHLIGHTS** * Improved star allele calling accuracy for Global Diversity Array with enhanced PGx (GDA-ePGx) BeadChips. * Reports star allele calls with quality scores for greater transparency and confidence. * Provides missing variant reporting to improve data quality. ## **NEW FEATURES IN DETAIL** * Star Allele Calling * Star allele calling for genes listed in [PGx Star Allele Coverage](/dragen-array-v1.2/reference/pgx-star-allele-coverage) * For in-silico datasets, call rate ≥99%, diplotyping accuracy ≥ 90% * Includes reporting of the hybrid star alleles and allelic specific copy number * Provides quality score that estimates confidence in the star allele call as an additional quality metric * Star allele call rate increased through more robust error tolerance and missing data tolerance * Supporting variants and missing variants are listed and can be further reviewed * Quality score indicates confidence in result considering the missing data * Reports alternative ranked PGx star allele solutions * Allows an alternative to be investigated which may be desirable for samples with low confidence calls * Provides quality score (negative log likelihood) for alternative solutions * Function annotations for PGx genes listed in section [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.2/reference/pgx-allele-definitions-and-pgx-guidelines) * Metabolizer and function annotations are supported for two sets of guidelines from CPIC and DPWG respectively * Activity scores are provided for CYP2C9, CYP2D6, and DPYD * CNV VCF * CNV coverage for genes listed in PGx CNVs Coverage * Compressed and indexed files for size reduction and faster reading * Updated VCF header description to indicate copy number of 5 may be reported by the software * Revised filter field delimiter to comply with VCF 4.3 specification which allows VCF parsing software to parse the file successfully * Genotyping VCF * Compressed and indexed files for size reduction and faster reading ## **KNOWN ISSUES** * Corrupt or invalid GTC files will abort with an error instead of skipping. The corrupt or invalid GTC files will need to be removed before proceeding. * In the gtc-to-vcf subcommand a mismatch between BPM and CSV manifests will not cause the command to abort with an error. The mismatch will need to be addressed before proceeding. * For gtc-to-vcf, multi-allelic variants designed with multiple assays might not always collapse into one variant correctly and be reported as two separate variants instead. Some indel variants are missing from SNV VCF due to mapping issue between the designed indels and the reference genome. * Manifest names greater than 80 characters will cause failure when converting IDATs to GTCs. * Symbolic links for VCFs are not supported as the inputs to the “star-allele call” subcommand. * The local Linux CLI and Cloud offering do not sort the star\_alleles.csv and various fields in the metabolizer\_status.json. The local Windows CLI does. * The new license server (`license.dragen.illumina.com`) will not work (i.e., returns "No valid licenses found.") for local star allele calling. Users should continue to point to `license.edicogenome.com`. ## **KNOWN LIMITATIONS** * PGx CNV calling and star allele calling and annotation were only validated and intended to be used with GDA\_PGx\_E2 product files. * Using subcommands “unsquash-duplicates” and “filter loci” during gtc-to-vcf conversion should not be used when star allele calling is desired. * Only CPIC guidelines are available for star allele annotation (metabolizer status calling) for the cloud offering. For local, CPIC and DPWG are available. # DRAGEN Array Genotyping Cloud v1.0.0 Release Notes ## **RELEASE DATE** March 2024 ## **RELEASE HIGHLIGHTS** * Ability to genotype and produce related reports for human and non-human arrays in the cloud. * Configureable interfaces in Basespace that allows for flexibility and easy kick off. ## **NEW FEATURES IN DETAIL** * [SNV VCF File](/dragen-array-v1.2/product-guides/output-files#snv_vcf_file) * [Final Report](/dragen-array-v1.2/product-guides/output-files#final_report) * [Locus Summary](/dragen-array-v1.2/product-guides/output-files#locus_summary) ## **KNOWN ISSUES** * Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report ## **KNOWN LIMITATIONS** * Genotyping only works on diploid organisms at this time. Polyploid genotyping is not currently supported. # DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes ## RELEASE DATE May 2024 ## RELEASE HIGHLIGHTS * Adjustable thresholds to determine pass/fail status * Data summary plots for a quick visual check of each analysis batch * Determining detection p-value, beta-values, and m-values from each methylation sample * Deployment on BaseSpace™ Sequence Hub user interface for easy analysis kickoff ## NEW FEATURES IN DETAIL * Adjustable thresholds for 21 built in controls, p-value detection, proportion probes passing, and offset correction within BaseSpace Sequence Hub to customize for user’s study needs * Thresholds are used to assign pass (1) or fail (0) status to each sample * Failed metrics can be highlighted for easy viewing * Pinpoint areas of failure including bisulfite conversion, staining, hybridization, etc. to identify assay steps in need of troubleshooting * Quantitative values for each control removing ambiguity with manual interpretation * Data summary plots with information on passing p-value detection and principal component analysis of beta values * Provides detection p-value, beta-values and m-values for each CG site per sample to use in downstream analysis ## KNOWN ISSUES ## KNOWN LIMITATIONS * Standard thresholds may not be applicable for all discontinued, semi-custom or custom BeadChips and IDATs originating from NextSeq550 * Built-in controls may not be available on all discontinued, semi-custom or custom BeadChips # PGx CNV Coverage Copy number variation can be detected for genes and regions listed below. The chromosome locations are GRCh38 based. | Gene | Region Name | Chromosome | Start | End | | ------- | --------------- | ------------------- | --------- | --------- | | GSTM1 | GSTM1 | 1 | 109687842 | 109693526 | | UGT2B17 | UGT2B17 | 4 | 68537222 | 68568499 | | CYP2E1 | CYP2E1 | 10 | 133527374 | 133539096 | | SULT1A1 | SULT1A1 | 16 | 28603587 | 28613544 | | CYP2A6 | CYP2A6.intron.7 | 19 | 40844791 | 40845293 | | CYP2A6 | CYP2A6.exon.1 | 19 | 40850267 | 40850414 | | CYP2D6 | CYP2D6.exon.9 | 22 | 42126498 | 42126752 | | CYP2D6 | CYP2D6.intron.2 | 22 | 42129188 | 42129734 | | CYP2D6 | CYP2D6.p5 | 22 | 42130886 | 42131379 | | GSTT1 | GSTT1 | 22\_KI270879v1\_alt | 270316 | 278477 | # PGx Allele Definitions and PGx Guidelines ## PGx Allele Definitions and PGx Guidelines DRAGEN Array star allele calling leverages the star allele definitions provided by PharmVar and PharmGKB. DRAGEN Array star allele phenotype annotation, using the “star-allele annotate” command, is achieved through direct lookup into public PGx guidelines CPIC or DPWG, which is selected by the user when running DRAGEN Array. See table below for details of the data sources. | Data Source | Version | URL | | ----------------------------------------------------------------------- | ------------------- | -------------------------------------------------------------------------------------- | | PharmVar | 6.1 | | | PharmGKB | Snapshot-2024.05.16 | | | UGT Alleles Nomenclature | 2010.12.21 | | | Human Cytochrome P450 (CYP) Allele Nomenclature Database Legacy Content | July 2024 | | | CPIC guidelines | 1.38.0 |

| | DPWG guidelines | June 2023 | | DRAGEN Array “star-allele annotate” command provides both metabolizer status and activity score annotations for genes covered by the CPIC and DPWG guidelines. Specifically, CPIC metabolizer/phenotype annotations are supported for CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, MT-RNR1, NUDT15, RYR1, SLCO1B1, TPMT, UGT1A1, CFTR, IFNL3/IFNL4 and VKORC1, among them activity scores are supported for CYP2C9, CYP2D6, and DPYD. DPWG metabolizer/phenotype annotations are supported for CYP1A2, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1 and F5, among them activity scores are supported for CYP2D6 and DPYD. ## Extended Multi-allelic variants based on the designs in the supported PGx products * DRAGEN Array PGx extends any single allele variant definitions obtained from PharmVar or PharmGKB that have multiple alleles in Illumina's product files to include all alleles of the Multi Allelic Variant (MAV). The table below shows the MAVs that were extended in the DRAGEN Array Database to cover all alleles for that MAV that are in the product files. Allele Name describes the allele that was added to the database. | Gene Symbol | Allele Name | Hgvs | | ----------------- | --------------------------- | ---------------------------- | | CACNA1S.rs1800559 | rs1800559.C>A | NC\_000001.11:g.201060815C>A | | CFTR.rs113993958 | rs113993958.G>A | NC\_000007.14:g.117530953G>A | | CFTR.rs113993958 | rs113993958.G>T | NC\_000007.14:g.117530953G>T | | CFTR.rs11971167 | rs11971167.G>T | NC\_000007.14:g.117642528G>T | | CFTR.rs121908755 | rs121908755.G>T | NC\_000007.14:g.117587800G>T | | CFTR.rs121909005 | rs121909005.T>C | NC\_000007.14:g.117587801T>C | | CFTR.rs121909020 | rs121909020.G>C | NC\_000007.14:g.117611640G>C | | CFTR.rs150212784 | rs150212784.T>C | NC\_000007.14:g.117611595T>C | | CFTR.rs193922525 | rs193922525.G>C | NC\_000007.14:g.117664770G>C | | CFTR.rs267606723 | rs267606723.G>T | NC\_000007.14:g.117642451G>T | | CFTR.rs397508288 | rs397508288.A>C | NC\_000007.14:g.117590409A>C | | CFTR.rs397508759 | rs397508759.G>T | NC\_000007.14:g.117534363G>T | | CFTR.rs74551128 | rs74551128.C>T | NC\_000007.14:g.117548795C>T | | CFTR.rs75039782 | rs75039782.C>G | NC\_000007.14:g.117639961C>G | | CFTR.rs77834169 | rs77834169.C>A | NC\_000007.14:g.117530974C>A | | CFTR.rs77834169 | rs77834169.C>G | NC\_000007.14:g.117530974C>G | | CFTR.rs77932196 | rs77932196.G>C | NC\_000007.14:g.117540270G>C | | CFTR.rs77932196 | rs77932196.G>T | NC\_000007.14:g.117540270G>T | | CFTR.rs78655421 | rs78655421.G>C | NC\_000007.14:g.117530975G>C | | CFTR.rs78655421 | rs78655421.G>T | NC\_000007.14:g.117530975G>T | | COMT.rs13306278 | rs13306278.C>G | NC\_000022.11:g.19941504C>G | | DPYD.rs114096998 | rs114096998.2.G>C | NC\_000001.11:g.97078987G>C | | DPYD.rs140602333 | rs140602333.G>T | NC\_000001.11:g.97573919G>T | | DPYD.rs142619737 | rs142619737.C>G | NC\_000001.11:g.97515851C>G | | DPYD.rs143154602 | rs143154602.G>T | NC\_000001.11:g.97593289G>T | | DPYD.rs145548112 | rs145548112.C>A | NC\_000001.11:g.97306195C>A | | DPYD.rs190951787 | rs190951787.G>T | NC\_000001.11:g.97515889G>T | | DPYD.rs200687447 | rs200687447.2.C>A | NC\_000001.11:g.97193209C>A | | DPYD.rs3918289 | rs3918289.G>A | NC\_000001.11:g.97450059G>A | | DPYD.rs3918290 | rs3918290.C>G | NC\_000001.11:g.97450058C>G | | DPYD.rs6670886 | rs6670886.C>A | NC\_000001.11:g.97699506C>A | | DPYD.rs72549304 | rs72549304.G>C | NC\_000001.11:g.97549609G>C | | DPYD.rs72549304 | rs72549304.G>T | NC\_000001.11:g.97549609G>T | | DPYD.rs748620513 | rs748620513.C>A | NC\_000001.11:g.97573799C>A | | DPYD.rs748639205 | rs748639205.A>G | NC\_000001.11:g.97082415A>G | | DPYD.rs760663364 | rs760663364.G>C | NC\_000001.11:g.97515928G>C | | DPYD.rs777425216 | rs777425216.C>A | NC\_000001.11:g.97515815C>A | | RYR1.38499667G>A | NC\_000019.10:g.38499667G>T | NC\_000019.10:g.38499667G>T | | RYR1.rs118192116 | rs118192116.C>T | NC\_000019.10:g.38451850C>T | | RYR1.rs118192151 | rs118192151.G>C | NC\_000019.10:g.38584974G>C | | RYR1.rs118204423 | rs118204423.G>A | NC\_000019.10:g.38457539G>A | | RYR1.rs142474192 | rs142474192.G>T | NC\_000019.10:g.38443790G>T | | RYR1.rs143988412 | rs143988412.A>G | NC\_000019.10:g.38580066A>G | | RYR1.rs1801086 | rs1801086.G>T | NC\_000019.10:g.38446710G>T | | RYR1.rs186983396 | rs186983396.C>G | NC\_000019.10:g.38442434C>G | | RYR1.rs193922762 | rs193922762.C>A | NC\_000019.10:g.38448673C>A | | RYR1.rs193922767 | rs193922767.G>A | NC\_000019.10:g.38452996G>A | | RYR1.rs193922772 | rs193922772.G>A | NC\_000019.10:g.38457546G>A | | RYR1.rs193922826 | rs193922826.C>G | NC\_000019.10:g.38504319C>G | | RYR1.rs193922838 | rs193922838.G>A | NC\_000019.10:g.38529036G>A | | RYR1.rs193922842 | rs193922842.C>T | NC\_000019.10:g.38543821C>T | | RYR1.rs370634440 | rs370634440.G>T | NC\_000019.10:g.38463499G>T | ### Exceptions to Star Allele Definitions #### G6PD With the changes of reference genomes, the definition for a star allele sometimes need to be updated accordingly. `Mediterranean Haplotype` and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` are defined by two variants rs5030868 and rs2230037. In genome build GRCh37, `Mediterranean Haplotype` is defined by rs2230037 G>A and rs5030868 G>A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs5030868 G>A, with rs2230037 reference allele G. In genome build GRCh38, `Mediterranean Haplotype` is defined by rs5030868 G>A, with rs2230037 reference allele A, and `Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham` is defined by rs2230037 A>G and rs5030868 G>A. Variant rs2230037 is ignored in all other G6PD alleles except in the two Mediterranean alleles. #### \*0 Star Allele Definition A \*0 allele refers to a full gene deletion of the analyzed gene, if there is no existing star allele name for the deletion allele from source databases, such as PharmVar and PharmGKB. # PGx Star Allele Coverage ## Theoretical Coverage The PGx genes and star/variant alleles listed below can be detected by DRAGEN Array v1.1 if available on the microarray. PGx coverage for specific PGx microarrays can be found here: [PGx Star Allele Coverage for Specific PGx Products](https://help.dragenarray.illumina.com/reference/pgx-star-allele-coverage#pgx-star-allele-coverage-for-specific-pgx-products). Known and novel star alleles not in the below list will not be reported. Star allele definitions are sourced from PharmVar and PharmGKB. Among the PGx genes, HLA-A, HLA-B, and IFNL3/IFNL4 alleles are covered through tagging variants, specifically HLA-A,\*31:01 (rs1061235.A>T); HLA-B,\*15:02 (rs144012689.T>A); HLA-B,\*57:01 (rs2395029.T>G); HLA-B,\*58:01 (rs9263726.G>A); IFNL3/4, rs12979860 variant (T). Reliability of the tagging SNPs varies depending on the population. Additional information on PGx gene types, variant type versus star allele type, can be found here: [Introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Gene | PGx Alleles | | ------- | 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| | ABCG2 | Reference;rs2231142.G>T | | ADH1B | Reference;rs1229984.T>C;rs1229984.T>G;rs1229985.A>G;rs17033.T>C;rs1789891.C>A;rs2018417.C>A;rs2018417.C>T;rs2066702.G>A;rs75967634.C>T | | ALDH2 | Reference;rs671.G>A | | ANK3 | Reference;rs143414470.T>C | | ANKK1 | Reference;rs1800497.G>A;rs2587550.G>A;rs2734849.A>C;rs2734849.A>G;rs4938013.A>C;rs4938013.A>G;rs4938013.A>T;rs7118900.G>A;rs7118900.G>C | | APOE | E2;E3;E4 | | ATM | Reference;rs11212570.G>A;rs11212570.G>T;rs11212617.C>A;rs1801516.G>A;rs620815.T>A;rs620815.T>C | | BDNF | Reference;rs10835210.C>A;rs10835210.C>G;rs11030101.A>G;rs11030101.A>T;rs11030104.A>G;rs11030118.G>A;rs11030119.G>A;rs11030119.G>T;rs1491850.T>C;rs16917234.T>A;rs16917234.T>C;rs1967554.A>C;rs2030324.A>G;rs61888800.G>T;rs6265.C>T;rs7103411.C>T;rs7124442.C>G;rs7124442.C>T;rs7127507.T>C;rs7934165.G>A;rs962369.T>C;rs988748.C>G | | CACNA1C | Reference;rs1006737.G>A;rs1034936.C>A;rs1034936.C>G;rs1034936.C>T;rs1051375.G>A;rs1051375.G>C;rs10774053.A>C;rs10774053.A>G;rs10848635.T>A;rs10848635.T>C;rs11062040.C>T;rs12813888.A>C;rs12813888.A>T;rs2041135.T>C;rs215976.C>G;rs215976.C>T;rs215994.T>C;rs216008.C>T;rs216013.A>G;rs2238032.T>C;rs2238032.T>G;rs2238087.C>G;rs2238087.C>T;rs2239050.G>A;rs2239050.G>C;rs2239128.T>A;rs2239128.T>C;rs2283271.T>A;rs723672.C>A;rs723672.C>G;rs723672.C>T;rs7295250.T>C;rs7316246.G>A;rs7316246.G>C;rs758723.T>A;rs758723.T>C | | CACNA1S | Reference;rs1800559.C>A;rs1800559.C>T;rs772226819.G>A | | CFTR | Reference;rs113993958.G>A;rs113993958.G>C;rs113993958.G>T;rs115545701.C>T;rs11971167.G>A;rs11971167.G>T;rs121908752.T>G;rs121908753.G>A;rs121908755.G>A;rs121908755.G>T;rs121908757.A>C;rs121909005.T>C;rs121909005.T>G;rs121909013.G>A;rs121909020.G>A;rs121909020.G>C;rs121909041.T>C;rs141033578.C>T;rs150212784.T>C;rs150212784.T>G;rs186045772.T>A;rs193922525.G>A;rs193922525.G>C;rs200321110.G>A;rs202179988.C>T;rs267606723.G>A;rs267606723.G>T;rs368505753.C>T;rs397508256.G>A;rs397508288.A>C;rs397508288.A>G;rs397508387.G>T;rs397508442.C>T;rs397508513.A>C;rs397508537.C>A;rs397508759.G>A;rs397508759.G>T;rs397508761.A>G;rs74503330.G>A;rs74551128.C>A;rs74551128.C>T;rs75039782.C>G;rs75039782.C>T;rs75527207.G>A;rs75541969.G>C;rs76151804.A>G;rs77834169.C>A;rs77834169.C>G;rs77834169.C>T;rs77932196.G>A;rs77932196.G>C;rs77932196.G>T;rs78655421.G>A;rs78655421.G>C;rs78655421.G>T;rs78769542.G>A;rs80224560.G>A;rs80282562.G>A | | COMT | Reference;rs13306278.C>T;rs165599.G>A;rs165599.G>C;rs165722.C>T;rs165728.C>A;rs165728.C>G;rs165728.C>T;rs165774.G>A;rs174675.T>C;rs174696.C>A;rs174696.C>T;rs174699.C>T;rs2020917.C>T;rs2075507.G>A;rs2075507.G>C;rs2075507.G>T;rs2239393.A>G;rs4633.C>T;rs4646312.T>C;rs4646316.C>G;rs4646316.C>T;rs4680.G>A;rs4818.C>G;rs4818.C>T;rs5746849.A>G;rs5993882.T>C;rs5993882.T>G;rs5993883.T>G;rs6267.G>A;rs6267.G>T;rs6269.A>G;rs6269.A>T;rs7287550.T>C;rs7287550.T>G;rs737865.A>G;rs737866.T>A;rs737866.T>C;rs740603.A>G;rs9332377.C>A;rs9332377.C>T;rs933271.T>A;rs933271.T>C;rs9606186.C>A;rs9606186.C>G;rs9606186.C>T | | CYP1A2 | \*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1A;\*1B;\*1C;\*1D;\*1E;\*1F;\*1G;\*1J;\*1K;\*1L;\*1M;\*1N;\*1P;\*1Q;\*1R;\*1S;\*1T;\*1U;\*1V;\*2;\*20;\*21;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2A6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*31;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*6;\*7;\*8;\*9 | | CYP2B6 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*3;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*6;\*7;\*8;\*9 | | CYP2C19 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*22;\*23;\*24;\*25;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*38;\*39;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C8 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | CYP2C9 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*49;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*57;\*58;\*59;\*6;\*60;\*61;\*62;\*63;\*64;\*65;\*66;\*67;\*68;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*76;\*77;\*78;\*79;\*8;\*80;\*81;\*82;\*83;\*84;\*85;\*9 | | CYP2D6 | \*1;\*1-\*90;\*10;\*100;\*101;\*102;\*103;\*104;\*105;\*106;\*107;\*108;\*109;\*10x2;\*11;\*110;\*111;\*112;\*113;\*114;\*115;\*116;\*117;\*118;\*119;\*12;\*120;\*121;\*122;\*123;\*124;\*125;\*126;\*127;\*128;\*129;\*13;\*13-\*1;\*13-\*2;\*13-\*4-\*68;\*130;\*131;\*132;\*133;\*134;\*135;\*136;\*137;\*138;\*139;\*13x2-\*1;\*13x2-\*2;\*14;\*140;\*141;\*142;\*143;\*144;\*145;\*146;\*147;\*148;\*149;\*15;\*150;\*151;\*152;\*153;\*154;\*155;\*156;\*157;\*158;\*159;\*160;\*161;\*162;\*163;\*164;\*165;\*166;\*167;\*168;\*169;\*17;\*170;\*171;\*172;\*17x2;\*18;\*19;\*1x2;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*29x2;\*2x2;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*35x2;\*36;\*36;\*36-\*10;\*36-\*10x2;\*36x2-\*10;\*36x3-\*10;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*43x2;\*44;\*45;\*46;\*47;\*48;\*49;\*4M;\*4N-\*4;\*4x2;\*5;\*50;\*51;\*52;\*53;\*54;\*55;\*56;\*58;\*59;\*6;\*60;\*62;\*64;\*65;\*68;\*68-\*4;\*69;\*7;\*70;\*71;\*72;\*73;\*74;\*75;\*8;\*81;\*82;\*83;\*84;\*85;\*86;\*87;\*88;\*89;\*9;\*90;\*91;\*92;\*93;\*94;\*95;\*96;\*97;\*98;\*99;\*9x2 | | CYP2E1 | \*1A;\*1B;\*2;\*3;\*4;\*5A;\*5B;\*6;\*7A;\*7B;\*7C | | CYP3A4 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*26;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*35;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*48;\*5;\*6;\*7;\*8;\*9 | | CYP3A5 | \*1;\*3;\*6;\*7;\*8;\*9 | | CYP4F2 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*17;\*2;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | DPYD | Reference;rs111858276.T>C;rs112766203.1.G>A;rs112766203.2.G>C;rs114096998.1.G>T;rs114096998.2.G>A;rs114096998.2.G>C;rs115232898.T>C;rs116364703.T>A;rs1180771326.T>C;rs137878450.C>A;rs137999090.C>T;rs138391898.C>T;rs138545885.C>A;rs138616379.C>T;rs139459586.A>C;rs139834141.C>T;rs140039091.C>G;rs140114515.C>T;rs140602333.G>A;rs140602333.G>T;rs140989814.C>G;rs141044036.T>C;rs141439344.C>T;rs141462178.T>C;rs141726921.C>T;rs142512579.C>T;rs142619737.C>G;rs142619737.C>T;rs143154602.G>A;rs143154602.G>T;rs143815742.1.C>A;rs143815742.2.C>T;rs143879757.1.G>T;rs143879757.2.G>A;rs143986398.G>C;rs144395748.1.G>C;rs144395748.2.G>T;rs144935781.T>C;rs145112791.G>A;rs145529148.T>C;rs145548112.C>A;rs145548112.C>T;rs145773863.C>T;rs146356975.T>C;rs146529561.G>A;rs147545709.G>A;rs147601618.A>G;rs148799944.C>G;rs148994843.C>T;rs150036960.G>C;rs150385342.1.C>T;rs150385342.2.C>A;rs150437414.A>G;rs151074666.C>T;rs17376848.A>G;rs1801158.C>T;rs1801159.T>C;rs1801160.C>T;rs1801265.A>G;rs1801266.G>A;rs1801267.C>T;rs1801268.C>A;rs183105782.A>G;rs183385770.C>T;rs186169810.A>C;rs187713395.A>G;rs188052243.T>C;rs190577302.G>C;rs190951787.G>C;rs190951787.G>T;rs199549923.G>T;rs199634007.G>T;rs199646142.C>T;rs199777072.C>T;rs200064537.A>T;rs200296941.T>C;rs200562975.T>C;rs200643089.A>C;rs200687447.1.C>T;rs200687447.2.C>A;rs200687447.2.C>G;rs200693895.A>G;rs200709381.T>G;rs201018345.C>T;rs201035051.T>G;rs201268750.G>T;rs201433243.C>T;rs201615754.1.C>A;rs201615754.2.C>T;rs201648613.C>G;rs201785202.G>A;rs202144771.G>A;rs202212118.C>A;rs2297595.T>C;rs267598785.G>A;rs267598786.C>T;rs267598789.G>A;rs367619008.T>C;rs368146607.T>G;rs368152149.T>C;rs368327291.C>G;rs368519011.T>C;rs368970772.G>T;rs369103276.A>G;rs369575517.G>A;rs370569731.1.C>G;rs370569731.2.C>T;rs370615432.C>A;rs370707404.A>G;rs371258350.C>T;rs371313778.C>T;rs371587702.1.G>A;rs371587702.2.G>C;rs371792178.1.G>A;rs371792178.2.G>C;rs372058915.T>C;rs372307932.A>T;rs372909322.T>C;rs374527058.A>G;rs374531732.C>T;rs374825099.1.G>T;rs374825099.2.G>C;rs374827081.G>C;rs375436137.C>T;rs375990187.A>G;rs376073289.1.C>T;rs376073289.2.C>A;rs376128878.G>T;rs376273539.G>C;rs377143350.C>T;rs377169736.C>G;rs3918289.G>A;rs3918289.G>C;rs3918290.C>G;rs3918290.C>T;rs45589337.T>C;rs527580106.T>C;rs528152707.C>A;rs528430685.G>A;rs528768620.C>T;rs529019871.T>C;rs532341730.A>T;rs536577604.T>C;rs538336580.T>A;rs538703919.G>A;rs547099198.G>A;rs548783838.C>T;rs55674432.C>A;rs556933127.A>C;rs557220418.G>A;rs558354142.G>A;rs55886062.1.A>C;rs55886062.2.A>T;rs559427764.C>A;rs55971861.T>G;rs56005131.G>T;rs56038477.C>T;rs568169006.T>C;rs568367673.C>A;rs569661196.A>G;rs570122671.G>A;rs571114616.A>G;rs573299212.C>T;rs575763449.G>A;rs575853463.C>T;rs576409484.T>A;rs57918000.G>A;rs59086055.G>A;rs60139309.T>C;rs60511679.A>C;rs61622928.C>T;rs61757362.G>A;rs6670886.C>A;rs6670886.C>T;rs672601273.1.C>A;rs672601273.2.C>T;rs672601275.T>G;rs672601276.C>A;rs672601282.G>A;rs672601284.C>T;rs672601285.T>C;rs672601287.T>G;rs672601288.C>A;rs67376798.T>A;rs72547601.T>C;rs72547602.T>A;rs72549303.del;rs72549304.G>A;rs72549304.G>C;rs72549304.G>T;rs72549305.T>C;rs72549306.1.C>A;rs72549306.2.C>T;rs72549307.T>C;rs72549308.T>G;rs72549309.ATGA\[1];rs72549310.G>A;rs72975710.1.G>A;rs72975710.2.G>C;rs745512069.G>A;rs745704371.G>C;rs745833535.T>C;rs745911874.C>T;rs745982505.1.T>C;rs745982505.2.T>A;rs746115989.C>T;rs746329786.T>A;rs746777181.C>T;rs747132274.C>G;rs747161261.C>T;rs747627716.A>C;rs747633945.C>T;rs747858350.G>A;rs747872037.C>A;rs748214188.A>T;rs748235192.1.T>A;rs748235192.2.T>C;rs748266854.G>A;rs748320430.A>C;rs748620513.C>A;rs748620513.C>G;rs748639205.A>C;rs748639205.A>G;rs748853941.T>C;rs748958293.G>A;rs748974194.G>A;rs749157068.C>A;rs749269410.C>T;rs749354734.A>T;rs749586100.T>A;rs749699298.A>C;rs749982106.G>A;rs750147471.T>C;rs75017182.G>C;rs750224169.G>A;rs750423752.A>C;rs750687600.C>T;rs750721736.A>T;rs751049055.C>A;rs751104498.T>C;rs751113340.G>A;rs751190912.G>A;rs751340819.A>G;rs751374989.T>A;rs751399062.G>T;rs751841116.1.C>A;rs751841116.2.C>T;rs751848058.T>A;rs752020412.C>T;rs752228747.G>A;rs752388408.C>T;rs752518145.C>A;rs752985272.C>A;rs753166888.C>G;rs753217888.G>C;rs753296078.C>G;rs753419296.C>G;rs753527420.C>G;rs753707032.G>A;rs753710779.G>A;rs753820482.T>C;rs753950237.G>A;rs754028972.A>G;rs754125729.1.G>A;rs754125729.2.G>T;rs754467630.G>A;rs754786483.T>C;rs755155824.C>A;rs755407188.T>G;rs755416212.C>T;rs755428442.C>G;rs755645831.A>C;rs755692084.T>G;rs755729055.T>C;rs756020314.G>C;rs756372042.A>G;rs756613407.T>C;rs756684474.T>C;rs756890859.T>C;rs756992995.C>T;rs757155354.T>C;rs757227327.C>T;rs757342874.C>T;rs757376267.C>A;rs757695236.C>T;rs757954074.C>T;rs757958938.T>C;rs757994597.G>A;rs758154803.A>G;rs758489611.C>T;rs758514990.C>T;rs758649719.C>T;rs758699471.T>C;rs759082282.C>A;rs759249769.G>T;rs759424419.A>T;rs759479759.T>C;rs759562628.T>G;rs759766897.T>C;rs759967863.A>G;rs760038956.C>T;rs760222167.T>C;rs760235888.C>T;rs760485592.G>A;rs760553268.G>C;rs760570391.A>G;rs760663364.G>A;rs760663364.G>C;rs761302217.T>C;rs761351410.G>A;rs761479700.G>C;rs761555670.T>C;rs761609256.T>G;rs762083671.T>A;rs762102298.A>C;rs762198241.G>A;rs762430779.G>T;rs762446803.A>C;rs762468894.G>C;rs762523739.T>A;rs762533012.C>T;rs762598766.T>C;rs762779297.T>C;rs762858106.C>T;rs762911226.T>A;rs763008163.T>G;rs763061658.A>G;rs763449831.C>T;rs763506271.T>C;rs763557204.A>G;rs763572567.T>G;rs763623595.A>C;rs763784786.G>C;rs763862486.C>T;rs763893877.T>C;rs763984510.G>C;rs764111543.C>T;rs764270260.G>A;rs764555085.A>G;rs764635955.G>T;rs764666241.C>A;rs764679468.A>C;rs764945792.C>T;rs765001324.C>T;rs765034707.C>A;rs765075551.T>C;rs765131182.G>A;rs765247038.G>A;rs765309287.G>T;rs765465250.T>C;rs765640386.C>A;rs765990958.G>A;rs766411970.A>C;rs766438205.T>C;rs766635900.C>T;rs766700777.C>G;rs766761199.T>G;rs766833304.G>C;rs766885021.A>C;rs767200577.T>C;rs767376585.C>G;rs767437717.G>T;rs767464878.C>A;rs767468952.C>T;rs767482279.A>G;rs767547827.G>C;rs767818267.C>T;rs767836989.T>C;rs767986711.T>G;rs768117152.T>C;rs768157853.G>C;rs768200107.T>G;rs768288280.T>C;rs768501828.T>C;rs768507975.A>T;rs768680499.G>T;rs768915005.C>T;rs769190350.T>A;rs769306962.C>T;rs769466648.1.T>G;rs769466648.2.T>C;rs769514867.G>T;rs769696395.T>C;rs769709846.T>C;rs769820114.C>T;rs769847078.T>C;rs769932607.G>A;rs770229152.T>A;rs770566506.A>G;rs770958862.G>A;rs771194906.A>G;rs771534236.T>C;rs771536388.C>T;rs771573678.T>A;rs771646887.C>T;rs771648776.T>C;rs771885007.A>G;rs771930534.1.A>T;rs771930534.2.A>G;rs772097379.G>A;rs772264512.G>A;rs772320654.T>C;rs772358811.C>G;rs772544099.G>T;rs772826416.A>G;rs772906420.C>T;rs773159364.C>G;rs773407491.T>C;rs773584401.C>A;rs773652644.T>C;rs773815814.1.C>A;rs773815814.2.C>T;rs773868825.C>T;rs773983635.A>T;rs774134971.T>C;rs774500505.A>T;rs774579695.1.C>T;rs774799003.G>A;rs774883578.A>C;rs775494607.G>A;rs775526810.C>A;rs775570841.G>C;rs775601164.G>A;rs775926386.G>C;rs776082092.C>T;rs776236081.C>T;rs776289153.C>T;rs776321529.G>C;rs776662759.T>G;rs776973423.C>T;rs776984091.T>C;rs777220476.1.C>T;rs777220476.2.C>A;rs777238016.T>C;rs777347164.C>T;rs777368221.A>C;rs777425216.C>A;rs777425216.C>T;rs777560627.G>A;rs777673186.G>C;rs777902288.T>A;rs778022685.C>T;rs778054451.C>T;rs778141885.T>C;rs778298325.C>T;rs778601245.C>T;rs778754188.A>G;rs778760295.C>G;rs778776264.T>C;rs778867644.T>C;rs778911905.A>C;rs779465366.A>G;rs779557503.G>A;rs779573574.T>A;rs779728902.A>T;rs779925747.T>G;rs779967271.T>C;rs780025995.G>A;rs780047918.T>C;rs780120302.T>C;rs78060119.C>A;rs780813130.C>T;rs780873985.T>C;rs780885126.T>C;rs781184141.T>C;rs80081766.C>T;rs866110709.C>T;rs866869468.C>A;rs867143119.C>A;rs867226255.C>T;rs867232786.C>T;rs867600987.C>T;rs868047175.C>T;rs868235016.C>T | | DRD2 | Reference;rs1076560.C>A;rs1076560.C>G;rs1076563.A>C;rs1079596.C>A;rs1079596.C>T;rs1079597.C>T;rs1079598.A>G;rs1079598.A>T;rs1110976.T>G;rs11214607.T>G;rs1124491.G>A;rs1124491.G>C;rs1124493.T>G;rs1125394.T>C;rs12364283.A>G;rs12574471.C>G;rs12574471.C>T;rs17601612.G>C;rs1799732.\_113475530insG;rs1799732.dup;rs1799978.T>C;rs1800497.G>A;rs1800498.G>A;rs1801028.G>C;rs2075652.G>A;rs2234689.G>C;rs2283265.C>A;rs2440390.T>C;rs2514218.C>T;rs2587548.G>A;rs2587548.G>C;rs2734833.G>A;rs2734841.A>C;rs2734841.A>G;rs2734841.A>T;rs2734842.G>C;rs4274224.G>A;rs4274224.G>C;rs4436578.C>G;rs4436578.C>T;rs4460839.C>G;rs4460839.C>T;rs4648317.G>A;rs4648318.T>A;rs4648318.T>C;rs4648318.T>G;rs4936274.A>G;rs4936274.A>T;rs6275.A>G;rs6277.G>A;rs6279.G>C;rs7122246.G>A;rs7131056.A>C;rs7131056.A>G;rs7131440.C>T | | F13A1 | Reference;rs5985.C>A;rs5985.C>T | | F2 | Reference;rs1799963.G>A;rs3136516.G>A;rs5896.C>G;rs5896.C>T | | F5 | Reference;rs6025.C>T | | FKBP5 | Reference;rs1360780.T>A;rs1360780.T>C;rs17614642.T>C;rs3800373.C>A;rs3800373.C>G;rs4713916.A>C;rs4713916.A>G;rs4713916.A>T;rs73748206.C>T;rs9380524.C>A;rs9380524.C>T | | G6PD | 202G>A\_376A>G\_1264C>G;A;A- 202A\_376G;A- 680T\_376G;A- 968C\_376G;Aachen;Abeno;Acrokorinthos;Alhambra;Amazonia;Amiens;Amsterdam;Anadia;Ananindeua;Andalus;Arakawa;Asahi;Asahikawa;Aures;Aveiro;B (reference);Bajo Maumere;Bangkok;Bangkok Noi;Bao Loc;Bari;Belem;Beverly Hills, Genova, Iwate, Niigata, Yamaguchi;Brighton;Buenos Aires;Cairo;Calvo Mackenna;Campinas;Canton, Taiwan-Hakka, Gifu-like, Agrigento-like;Cassano;Chatham;Chikugo;Chinese-1;Chinese-5;Cincinnati;Cleveland Corum;Clinic;Coimbra Shunde;Cosenza;Costanzo;Covao do Lobo;Crispim;Dagua;Durham;Farroupilha;Figuera da Foz;Flores;Fukaya;Fushan;Gaohe;Georgia;Gidra;Gond;Guadalajara;Guangzhou;Haikou;Hammersmith;Harilaou;Harima;Hartford;Hechi;Hermoupolis;Honiara;Ierapetra;Ilesha;Insuli;Iowa, Walter Reed, Springfield;Iwatsuki;Japan, Shinagawa;Kaiping, Anant, Dhon, Sapporo-like, Wosera;Kalyan-Kerala, Jamnaga, Rohini;Kambos;Kamiube, Keelung;Kamogawa;Kawasaki;Kozukata;Krakow;La Jolla;Lages;Lagosanto;Laibin;Lille;Liuzhou;Loma Linda;Ludhiana;Lynwood;Madrid;Mahidol;Malaga;Manhattan;Mediterranean Haplotype;Mediterranean, Dallas, Panama, Sassari, Cagliari, Birmingham;Metaponto;Mexico City;Miaoli;Minnesota, Marion, Gastonia, LeJeune;Mira d'Aire;Mizushima;Montalbano;Montpellier;Mt Sinai;Munich;Murcia Oristano;Musashino;Namouru;Nankang;Nanning;Naone;Nara;Nashville, Anaheim, Portici;Neapolis;Nice;Nilgiri;No name;North Dallas;Olomouc;Omiya;Orissa;Osaka;Palestrina;Papua;Partenope;Pawnee;Pedoplis-Ckaro;Piotrkow;Plymouth;Praha;Puerto Limon;Quing Yan;Radlowo;Rehevot;Rignano;Riley;Riverside;Roubaix;S. Antioco;Salerno Pyrgos;Santa Maria;Santiago;Santiago de Cuba, Morioka;Sao Borja;Seattle, Lodi, Modena, Ferrara II, Athens-like;Seoul;Serres;Shenzen;Shinshu;Sibari;Sierra Leone;Sinnai;Songklanagarind;Split;Stonybrook;Sugao;Sumare;Sunderland;Surabaya;Suwalki;Swansea;Taipei, Chinese-3;Telti, Kobe;Tenri;Tokyo, Fukushima;Toledo;Tomah;Tondela;Torun;Tsukui;Ube Konan;Union,Maewo, Chinese-2, Kalo;Urayasu;Utrecht;Valladolid;Vancouver;Vanua Lava;Viangchan, Jammu;Villeurbanne;Volendam;Wayne;West Virginia;Wexham;Wisconsin;Yunan | | GRIK1 | Reference;rs2832407.C>A;rs2832407.C>T | | GRIK4 | Reference;rs12800734.G>A;rs1954787.T>C | | GRIN2B | Reference;rs1019385.C>A;rs1072388.G>A;rs1072388.G>C;rs1806191.G>A;rs1806191.G>T;rs1806201.G>A;rs2058878.T>A;rs2058878.T>C;rs2160733.A>C;rs2160734.C>G;rs2160734.C>T;rs2284411.C>T;rs890.A>C;rs890.A>G | | HLA-A | \*31:01;Reference | | HLA-B | \*15:02;\*57:01;\*58:01;Reference | | HMGCR | Reference;rs10474433.T>C;rs10474433.T>G;rs12654264.A>T;rs17238540.T>G;rs17244841.A>T;rs17671591.C>T;rs3846662.A>G;rs3846662.A>T | | HTR2A | Reference;rs17288723.T>C;rs17289304.T>C;rs17289304.T>G;rs1928040.G>A;rs1928040.G>C;rs2274639.C>G;rs2274639.C>T;rs2770296.C>G;rs2770296.C>T;rs3742278.A>G;rs3803189.T>G;rs6305.G>A;rs6311.C>A;rs6311.C>T;rs6312.C>A;rs6312.C>G;rs6312.C>T;rs6313.G>A;rs6313.G>C;rs6314.G>A;rs659734.G>A;rs659734.G>C;rs659734.G>T;rs7997012.A>C;rs7997012.A>G;rs7997012.A>T;rs9316233.C>A;rs9316233.C>G;rs9316233.C>T;rs9567746.A>C;rs9567746.A>G | | HTR2C | Reference;rs1023574.C>G;rs1023574.C>T;rs12836771.A>G;rs1414334.C>G;rs2497538.A>C;rs3813928.G>A;rs3813929.C>G;rs3813929.C>T;rs498207.G>A;rs518147.C>A;rs518147.C>G;rs539748.C>T;rs6318.C>G;rs6318.C>T;rs9698290.T>A;rs9698290.T>C | | IFNL3/4 | Reference;rs12979860 variant (T) | | IL6 | Reference;rs10242595.G>A;rs10242595.G>C;rs10242595.G>T;rs10499563.T>C;rs1524107.C>G;rs1524107.C>T;rs1800795.C>G;rs1800795.C>T;rs1800796.G>A;rs1800796.G>C;rs1800797.A>C;rs1800797.A>G;rs1800797.A>T;rs2066992.G>A;rs2066992.G>C;rs2066992.G>T;rs2069835.T>C;rs2069837.A>C;rs2069837.A>G;rs2069840.C>G | | ITGB3 | Reference;rs11871251.G>A;rs11871251.G>C;rs2317676.A>G;rs3785873.G>A;rs3785873.G>T;rs58847127.G>A;rs58847127.G>C;rs58847127.G>T;rs5918.T>C;rs8069732.C>A;rs8069732.C>T | | KIF6 | Reference;rs20455.A>G;rs9462535.C>A;rs9462535.C>G;rs9462535.C>T;rs9471077.A>G | | LPA | Reference;rs10455872.A>G;rs3798220.T>C | | MT-RNR1 | NC\_012920.1:m.1520T>C;NC\_012920.1:m.1537C>T;NC\_012920.1:m.1556C>T;NC\_012920.1:m.669T>C;NC\_012920.1:m.747A>G;NC\_012920.1:m.786G>A;NC\_012920.1:m.807A>C;NC\_012920.1:m.807A>G;NC\_012920.1:m.839A>G;NC\_012920.1:m.896A>G;NC\_012920.1:m.930A>G;NC\_012920.1:m.960delC;NC\_012920.1:m.988G>A;Reference;rs1556422499.delT;rs200887992.G>A;rs267606617.A>G;rs267606618.T>C;rs267606619.C>T;rs28358569.A>G;rs28358571.T>C;rs28358572.T>C;rs3888511.T>G;rs56489998.A>G | | MTHFR | Reference;rs1476413.C>G;rs1476413.C>T;rs17367504.A>G;rs17421511.G>A;rs1801131.T>G;rs1801133.G>A;rs1801133.G>C;rs2274976.C>T;rs3737967.G>A;rs4846051.G>A;rs4846051.G>C;rs4846051.G>T | | NUDT15 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*3;\*4;\*5;\*6;\*7;\*8;\*9 | | OPRD1 | Reference;rs1042114.G>C;rs1042114.G>T;rs10753331.G>A;rs10753331.G>T;rs12749204.A>G;rs204047.G>C;rs204047.G>T;rs204055.T>A;rs204055.T>C;rs204069.A>G;rs204076.T>A;rs204076.T>C;rs204076.T>G;rs2234918.C>G;rs2234918.C>T;rs2236855.C>A;rs2236855.C>G;rs2236857.T>C;rs2236861.G>A;rs2298895.A>T;rs2298896.T>G;rs2298897.C>G;rs3766951.T>C;rs419335.A>G;rs421300.A>C;rs421300.A>G;rs4654327.G>A;rs4654327.G>T;rs482387.G>A;rs482387.G>C;rs508448.A>G;rs529520.A>C;rs529520.A>G;rs533123.G>A;rs533123.G>C;rs569356.A>G;rs581111.A>C;rs581111.A>G;rs581111.A>T;rs6669447.T>C;rs678849.C>G;rs678849.C>T;rs680090.G>A;rs760589.G>A;rs797397.G>A | | OPRK1 | Reference;rs10111937.C>T;rs1051660.C>A;rs1051660.C>G;rs1051660.C>T;rs16918842.C>A;rs16918842.C>T;rs16918875.G>A;rs16918909.A>G;rs16918941.A>G;rs3802279.C>T;rs3802281.T>C;rs3808627.C>G;rs3808627.C>T;rs6473797.T>C;rs6473799.A>G;rs6985606.T>A;rs6985606.T>C;rs7016778.A>T;rs702764.T>C;rs702764.T>G;rs7813478.T>C;rs963549.C>T;rs997917.T>C | | OPRM1 | Reference;rs10457090.A>G;rs10457090.A>T;rs10485057.A>G;rs10485058.A>G;rs10485060.C>A;rs1074287.A>G;rs11575856.G>A;rs12190259.A>C;rs12205732.G>A;rs12209447.C>T;rs12210856.T>G;rs1294092.A>G;rs1319339.T>A;rs1319339.T>C;rs13195018.A>C;rs13195018.A>T;rs13203628.A>G;rs1323040.A>G;rs1323042.G>C;rs1323042.G>T;rs1381376.C>A;rs1381376.C>G;rs1381376.C>T;rs1461773.G>A;rs17174629.A>G;rs17174794.C>G;rs17174794.C>T;rs17174801.A>G;rs17180982.dup;rs17181352.A>G;rs1799971.A>G;rs1799972.C>A;rs1799972.C>G;rs1799972.C>T;rs1852629.T>A;rs1852629.T>C;rs1852629.T>G;rs2010884.G>A;rs2075572.G>C;rs2236256.C>A;rs2236257.G>C;rs2236258.C>G;rs2236258.C>T;rs2236259.T>A;rs2236259.T>C;rs2236259.T>G;rs2281617.C>G;rs2281617.C>T;rs3778148.G>T;rs3778150.T>C;rs3778151.T>C;rs3778152.A>G;rs3778156.A>G;rs3798676.C>T;rs3798677.A>G;rs3798678.A>C;rs3798678.A>G;rs3798683.G>A;rs3798688.G>T;rs3823010.G>A;rs483481.G>A;rs483481.G>C;rs4870266.G>A;rs495491.A>G;rs497976.G>A;rs497976.G>T;rs499796.A>G;rs506247.A>C;rs510769.C>T;rs511435.C>G;rs511435.C>T;rs518596.G>A;rs524731.C>A;rs527434.T>A;rs527434.T>C;rs538174.T>C;rs540825.A>C;rs540825.A>G;rs540825.A>T;rs544093.G>A;rs544093.G>T;rs548646.T>A;rs548646.T>C;rs548646.T>G;rs553202.C>T;rs558025.A>G;rs558948.C>G;rs558948.C>T;rs562859.C>A;rs562859.C>G;rs562859.C>T;rs563649.C>T;rs569284.A>C;rs583664.T>C;rs589046.C>T;rs598160.G>A;rs598160.G>C;rs598682.A>C;rs598682.A>G;rs598682.A>T;rs599548.G>A;rs606545.G>A;rs606545.G>C;rs609148.G>A;rs609148.G>T;rs609623.T>A;rs609623.T>C;rs610231.G>A;rs610231.G>C;rs613355.C>A;rs613355.C>G;rs613355.C>T;rs618207.A>C;rs618207.A>G;rs618207.A>T;rs62436463.C>T;rs62638690.G>T;rs632499.A>C;rs632499.A>G;rs632499.A>T;rs639855.C>A;rs639855.C>G;rs642489.G>A;rs642489.G>T;rs644261.G>A;rs644261.G>C;rs644261.G>T;rs645027.A>G;rs647192.G>A;rs647192.G>C;rs648007.A>C;rs648007.A>G;rs648893.A>G;rs650825.G>A;rs6557337.C>A;rs6557337.C>T;rs658156.A>C;rs658156.A>G;rs658156.A>T;rs671531.A>G;rs671531.A>T;rs675026.A>C;rs675026.A>G;rs677830.C>A;rs677830.C>G;rs677830.C>T;rs681243.T>A;rs681243.T>C;rs6902403.T>C;rs6912029.G>T;rs73576470.A>G;rs7748401.T>G;rs7763748.C>A;rs7763748.C>T;rs7776341.A>C;rs79910351.C>T;rs9282815.C>A;rs9282815.C>T;rs9322446.G>A;rs9322447.A>C;rs9322447.A>G;rs9322447.A>T;rs9322453.G>C;rs9371773.G>A;rs9371776.G>A;rs9384174.C>G;rs9384174.C>T;rs9384179.G>A;rs9384179.G>T;rs9397685.A>G;rs9397685.A>T;rs9397687.C>T;rs9479757.G>A;rs9479779.A>G | | RYR1 | NC\_000019.10:g.38440818G>C;NC\_000019.10:g.38444179C>A;NC\_000019.10:g.38444252G>T;NC\_000019.10:g.38444257A>C;NC\_000019.10:g.38444257A>G;NC\_000019.10:g.38448680\_38448681insGGA;NC\_000019.10:g.38448715G>A;NC\_000019.10:g.38451785C>A;NC\_000019.10:g.38452985C>T;NC\_000019.10:g.38455253C>G;NC\_000019.10:g.38455254T>C;NC\_000019.10:g.38455347T>C;NC\_000019.10:g.38455504G>T;NC\_000019.10:g.38466392G>A;NC\_000019.10:g.38469404A>C;NC\_000019.10:g.38485679T>C;NC\_000019.10:g.38486095A>G;NC\_000019.10:g.38490642A>C;NC\_000019.10:g.38494454G>A;NC\_000019.10:g.38496455G>A;NC\_000019.10:g.38499234T>C;NC\_000019.10:g.38499642C>A;NC\_000019.10:g.38499667G>A;NC\_000019.10:g.38499667G>T;NC\_000019.10:g.38499680T>A;NC\_000019.10:g.38499683G>A;NC\_000019.10:g.38499696C>G;NC\_000019.10:g.38499719A>G;NC\_000019.10:g.38499730G>A;NC\_000019.10:g.38499985A>T;NC\_000019.10:g.38500000G>A;NC\_000019.10:g.38502669C>G;NC\_000019.10:g.38504298G>A;NC\_000019.10:g.38506508C>G;NC\_000019.10:g.38506865C>T;NC\_000019.10:g.38507821C>T;NC\_000019.10:g.38512279G>A;NC\_000019.10:g.38515052C>T;NC\_000019.10:g.38516181T>C;NC\_000019.10:g.38516208G>C;NC\_000019.10:g.38517470T>C;NC\_000019.10:g.38517523T>A;NC\_000019.10:g.38519424C>A;NC\_000019.10:g.38519432A>T;NC\_000019.10:g.38519447A>G;NC\_000019.10:g.38525432C>T;NC\_000019.10:g.38527710G>C;NC\_000019.10:g.38528372G>T;NC\_000019.10:g.38529002G>C;NC\_000019.10:g.38529042C>T;NC\_000019.10:g.38543380A>T;NC\_000019.10:g.38543566G>A;NC\_000019.10:g.38543810C>T;NC\_000019.10:g.38548253A>T;NC\_000019.10:g.38561140G>C;NC\_000019.10:g.38561213C>T;NC\_000019.10:g.38561362G>A;NC\_000019.10:g.38561363G>T;NC\_000019.10:g.38565023T>G;NC\_000019.10:g.38570649C>G;NC\_000019.10:g.38577931A>C;NC\_000019.10:g.38578205G>T;NC\_000019.10:g.38580039\_38580040delinsAA;NC\_000019.10:g.38580041C>A;NC\_000019.10:g.38580126C>G;NC\_000019.10:g.38580397G>C;NC\_000019.10:g.38580416C>T;NC\_000019.10:g.38585078A>G;NC\_000019.10:g.38585099G>A;NC\_000019.10:g.38586190A>G;NC\_000019.10:g.38587362G>C;NC\_000019.10:g.38587363G>C;Reference;rs111272095.C>T;rs111364296.G>A;rs111565359.G>A;rs111657878.T>C;rs111888148.G>A;rs112151058.G>A;rs112196644.A>G;rs112563513.G>A;rs112596687.T>A;rs112772310.G>A;rs113210953.A>G;rs113332073.G>A;rs113332073.G>T;rs117886618.C>G;rs118192113.C>A;rs118192116.C>G;rs118192116.C>T;rs118192121.A>C;rs118192122.G>A;rs118192123.T>C;rs118192124.C>T;rs118192126.A>G;rs118192130.G>A;rs118192135.G>A;rs118192140.C>T;rs118192151.G>A;rs118192151.G>C;rs118192158.G>A;rs118192159.C>G;rs118192160.G>A;rs118192160.G>T;rs118192161.C>T;rs118192162.A>C;rs118192162.A>G;rs118192163.G>A;rs118192163.G>C;rs118192163.G>T;rs118192167.A>G;rs118192168.G>A;rs118192170.T>C;rs118192172.C>T;rs118192175.C>T;rs118192176.G>A;rs118192177.C>G;rs118192177.C>T;rs118192178.C>G;rs118192178.C>T;rs118192181.C>T;rs118204421.C>T;rs118204422.T>C;rs118204423.G>A;rs118204423.G>C;rs121918592.G>A;rs121918592.G>C;rs121918593.G>A;rs121918594.G>A;rs121918594.G>T;rs121918595.C>T;rs121918596.\_38499648delGAG;rs137932199.G>A;rs137933390.A>G;rs138874610.G>A;rs139161723.G>A;rs139647387.A>G;rs140152019.G>A;rs140616359.G>A;rs141646642.C>G;rs141942845.G>A;rs142474192.G>A;rs142474192.G>T;rs143398211.G>A;rs143520367.C>T;rs143987857.G>A;rs143988412.A>G;rs143988412.A>T;rs144336148.G>A;rs144685735.C>T;rs145573319.A>G;rs145801146.C>T;rs146306934.G>A;rs146429605.A>G;rs146504767.G>A;rs146876145.C>T;rs147136339.A>G;rs147213895.A>G;rs147303895.G>A;rs147707463.C>T;rs147723844.A>G;rs148399313.G>A;rs148623597.G>A;rs150396398.G>C;rs151029675.C>T;rs151119428.G>A;rs1801086.G>A;rs1801086.G>C;rs1801086.G>T;rs180714609.G>A;rs186983396.C>G;rs186983396.C>T;rs192863857.C>T;rs193922744.T>G;rs193922745.\_38440752delTGA;rs193922746.A>G;rs193922747.T>C;rs193922748.C>T;rs193922749.C>A;rs193922750.C>A;rs193922751.G>A;rs193922752.A>G;rs193922753.G>A;rs193922753.G>T;rs193922754.G>A;rs193922755.G>A;rs193922756.A>G;rs193922757.C>T;rs193922759.G>A;rs193922760.A>T;rs193922761.G>T;rs193922762.C>A;rs193922762.C>T;rs193922764.C>A;rs193922764.C>G;rs193922764.C>T;rs193922766.G>A;rs193922766.G>T;rs193922767.G>A;rs193922767.G>T;rs193922768.C>A;rs193922768.C>T;rs193922769.T>C;rs193922769.T>G;rs193922770.C>T;rs193922772.G>A;rs193922772.G>T;rs193922775.C>T;rs193922776.C>T;rs193922777.C>T;rs193922781.C>T;rs193922782.T>G;rs193922783.T>A;rs193922788.G>C;rs193922789.G>A;rs193922790.A>T;rs193922791.C>T;rs193922792.G>T;rs193922793.T>A;rs193922795.G>A;rs193922797.G>A;rs193922798.G>C;rs193922799.G>A;rs193922801.A>G;rs193922802.G>A;rs193922803.C>T;rs193922804.A>G;rs193922805.T>G;rs193922806.C>G;rs193922807.G>C;rs193922809.G>A;rs193922810.G>A;rs193922810.G>T;rs193922812.C>T;rs193922813.G>C;rs193922815.G>A;rs193922815.G>C;rs193922816.C>T;rs193922817.C>T;rs193922818.G>A;rs193922819.T>C;rs193922822.C>G;rs193922822.C>T;rs193922824.C>T;rs193922826.C>G;rs193922826.C>T;rs193922827.G>C;rs193922828.G>A;rs193922829.G>A;rs193922830.C>T;rs193922831.T>A;rs193922832.G>A;rs193922833.G>A;rs193922834.G>A;rs193922838.G>A;rs193922838.G>T;rs193922839.G>A;rs193922840.T>G;rs193922842.C>G;rs193922842.C>T;rs193922843.G>T;rs193922844.C>A;rs193922848.A>T;rs193922849.C>A;rs193922850.T>C;rs193922852.G>C;rs193922852.G>T;rs193922853.A>T;rs193922855.C>T;rs193922860.G>A;rs193922862.\_38572267delinsCT;rs193922863.C>T;rs193922864.T>C;rs193922865.T>G;rs193922866.G>A;rs193922867.C>T;rs193922868.G>A;rs193922873.G>A;rs193922873.G>T;rs193922874.T>C;rs193922876.C>T;rs193922877.delA;rs193922878.C>G;rs193922879.G>A;rs193922880.C>G;rs193922883.T>C;rs193922888.G>A;rs193922895.C>A;rs193922896.G>T;rs193922898.T>A;rs199738299.A>G;rs199870223.C>T;rs200766617.G>A;rs201321695.A>G;rs2145447772.G>A;rs2145447772.G>C;rs28933396.G>A;rs28933396.G>T;rs28933397.C>T;rs34390345.A>G;rs34694816.A>G;rs34934920.C>T;rs35180584.C>G;rs35364374.G>T;rs370634440.G>A;rs370634440.G>T;rs372958050.T>C;rs373406011.C>T;rs375626634.T>C;rs375915752.C>T;rs376149732.C>T;rs4802584.C>G;rs537994744.G>A;rs549201486.C>T;rs551223467.C>T;rs553055844.G>A;rs55876273.G>C;rs587784372.C>T;rs63749869.G>A;rs727504129.C>T;rs746818096.T>A;rs747177274.G>C;rs748575133.T>A;rs749040743.G>A;rs751180702.G>A;rs752652072.C>T;rs754476250.C>T;rs754785770.A>G;rs755088027.G>A;rs756850145.A>G;rs757753317.G>A;rs759500310.T>C;rs761616815.G>A;rs762401851.G>A;rs763112609.C>T;rs763352221.C>T;rs767553612.A>G;rs768360593.G>A;rs768535909.T>C;rs769482889.C>T;rs770593660.G>C;rs771058055.G>A;rs771741606.C>T;rs773040531.A>G;rs778241277.G>A;rs781104539.A>G;rs781126470.C>T;rs901087791.G>A;rs914804033.G>A;rs914804033.G>C;rs917523269.C>T;rs936513262.G>A;rs959170123.G>A;rs976108591.A>G;rs995399438.T>C | | SLCO1B1 | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*19;\*2;\*20;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*3;\*30;\*31;\*32;\*33;\*34;\*36;\*37;\*38;\*39;\*4;\*40;\*41;\*42;\*43;\*44;\*45;\*46;\*47;\*5;\*6;\*7;\*8;\*9 | | TNF | Reference;rs1799724.C>T;rs1799964.T>C;rs1800610.G>A;rs1800629.G>A;rs1800630.C>A;rs1800750.G>A;rs2736195.A>G;rs3093548.C>T;rs3093662.A>G;rs3093726.T>C;rs361525.G>A;rs4248158.C>T;rs4248159.C>A;rs4248160.G>A;rs4248163.C>A;rs4248163.C>G;rs4248163.C>T;rs4647198.C>T;rs4987086.G>A;rs55634887.G>A;rs55994001.C>A;rs55994001.C>T | | TPMT | \*1;\*10;\*11;\*12;\*13;\*14;\*15;\*16;\*17;\*18;\*19;\*2;\*20;\*21;\*22;\*23;\*24;\*25;\*26;\*27;\*28;\*29;\*30;\*31;\*32;\*33;\*34;\*35;\*36;\*37;\*38;\*39;\*3A;\*3B;\*3C;\*4;\*40;\*41;\*42;\*43;\*44;\*5;\*6;\*7;\*8;\*9 | | UGT1A1 | \*1;\*27;\*28;\*36;\*37;\*6;\*80;\*80+\*28;\*80+\*37 | | UGT1A4 | \*1a;\*1b;\*1c;\*2;\*3a;\*3b;\*4;\*7 | | UGT2B15 | \*1;\*2;\*3;\*4;\*5;\*6;\*7 | | VKORC1 | Reference;rs9923231 variant (T) | | YEATS4 | Reference;rs7297610.C>T | ## PGx Star Allele Coverage for Specific PGx Products PGx star alleles can only be called when the related variants in the star allele definition are present in a PGx product. An auxiliary file (\[Product]\_GS\_import.txt) is provided for each product with the PGx variants and associated star alleles. The product files pages that contain the auxiliary files are listed in the table below. Instructions on how to use the auxiliary file can be found here: [How to use the auxiliary file](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000008505). | Product | GS Import File Name | Product Files Link | | ---------- | ---------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | GDA-ePGx | GDAePGx\_G2\_GS\_import.txt | [GDA-ePGx G2 product files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html) | | GSAv4-ePGx | GSAePGx\_E2\_GS\_import.txt | [GSAv4-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | | GCRA-ePGx | GCRAePGx\_E2\_GS\_import.txt | [GCRA-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | ## Known Limitations of GDA-ePGx, GSAv4-ePGx, and GCRA-ePGx. * APOE: GSAv4-ePGx and GCRA-ePGx do not support calling E2 and E4 due to the lack of functional probes for rs7412 and rs429358. * CYP2A6: GDA-ePGx does not support \*5 due to lack of coverage for \*5 core variants. * CYP4F2: for all three products * \*1 and \*2 are not distinguishable due to the lack of probes for rs30193105. Samples with \*2 will be called as \*1. * \*3 and \*4 are not distinguishable due to the lack of probes for rs30193105, while \*3 core variant rs2108622 is covered by all three products. Samples with \*4 will be called as \*3. * UGT1A1: \*28 (rs8175347 \[TA]8) and \*37 (rs8175347 \[TA]9) are not covered in all three PGx products due to the lack of functional probes. * UGT2B15: GSAv4-ePGx and GCRA-ePGx do not support \*4 or \*5 due to the lack of probes for rs4148269 and rs1902023. ## PGx Variants Masked in DRAGEN Array During DRAGEN Array star allele calling, poorly performing PGx variants are masked and treated as "No Calls". Star alleles that are solely defined by the masked variants will NOT be called by DRAGEN Array. The tables below provide the variants that are masked per product with each row represents a single variant. The Variant\_ID matches the ID field of the corresponding SNV VCF entry of the PGx product. ### GDA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GDA\_PGx-8v1-0\_20042614\_G | CYP1A2 | ilmnseq\_rs35694136\_ilmnfwd;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup1;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GDA\_PGx-8v1-0\_20042614\_G | CYP2D6 | ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_IlmnRep;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_deg3a3b3\_ilmndup3;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup1;ilmnseq\_rs72549352\_ilmnrev\_F2BTindel\_ilmndup3 | | GDA\_PGx-8v1-0\_20042614\_G | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GDA\_PGx-8v1-0\_20042614\_G | UGT1A1 | ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel;ilmnseq\_rs8175347\_ilmnfwd\_F2BTindel\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev;ilmnseq\_rs8175347\_ilmnrev\_ilmndup1;ilmnseq\_rs8175347\_ilmnrev\_ilmndup2;ilmnseq\_rs8175347\_ilmnrev\_ilmndup3 | ### GSAv4-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | --------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GSA-PGx-48v4-0\_20079540\_E | CYP1A2 | IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup3;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup4;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup7 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs367543002,ilmnseq\_rs367543002\_ilmnfwd,ilmnseq\_rs367543002\_ilmnfwd\_ilmndup1,ilmnseq\_rs367543002\_ilmnrev\_deg3a1b0\_ilmndup1,rs367543002 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1,ilmnseq\_rs17882687\_ilmnrev\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GSA-PGx-48v4-0\_20079540\_E | CYP2C9 | 10:96701973,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup1,ilmnseq\_rs774607211\_ilmnfwd\_ilmndup2 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | ilmnseq\_rs1135836\_ilmnrev\_deg3a3b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev\_F2BT,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0 | | GSA-PGx-48v4-0\_20079540\_E | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GSA-PGx-48v4-0\_20079540\_E | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | | GSA-PGx-48v4-0\_20079540\_E | UGT1A1 | ilmnseq\_rs8175347.2\_ilmnrev\_F2BTindel\_cei\_ilmndup31 | ### GCRA-ePGx | Manifest | Gene\_Symbol | Variant\_ID | | ---------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | GCRA-PGx-24v1-0\_20084467\_C | COMT | ilmnseq\_rs7287550\_ilmnfwd\_F2BT | | GCRA-PGx-24v1-0\_20084467\_C | CYP1A2 | IlmnSeq\_rs35694136;IlmnSeq\_rs35694136\_IlmnFWD;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup2;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup5;ilmnseq\_rs35694136\_ilmnfwd\_ilmndup6;rs35694136 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs367543002\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | ilmnseq\_rs17882687\_ilmnfwd\_ilmndup2,ilmnseq\_rs17882687\_ilmnrev\_ilmndup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C19 | IlmnSeq\_rs113934938,ilmnseq\_rs113934938\_ilmnfwd,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup1,ilmnseq\_rs113934938\_ilmnfwd\_ilmndup2,rs113934938 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2C9 | ilmnseq\_rs774607211\_ilmnrev,ilmnseq\_rs774607211\_ilmnrev\_ilmndup2 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | ilmnseq\_rs2004511\_dup1 | | GCRA-PGx-24v1-0\_20084467\_C | CYP2D6 | PGX\_IlmnSeq\_rs769157652\_BEST,ilmnseq\_rs769157652\_ilmnrev,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0,ilmnseq\_rs769157652\_ilmnrev\_deg3a1b0\_ilmndup1,ilmnseq\_rs769157652\_ilmnrev\_ilmndup1,seq-rs61737947 | | GCRA-PGx-24v1-0\_20084467\_C | CYP4F2 | ilmnseq\_rs4020346\_ilmnfwd | | GCRA-PGx-24v1-0\_20084467\_C | OPRM1 | ilmnseq\_rs9384179.1\_F2BT | # Document Revision History The version history for DRAGEN Array product documentation: | Version | Date | Description of Change | | ------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 01 | December 2023 | Initial release. | | 02 | March 2024 | Added details for DRAGEN Array v1.0.0 cloud genotype pipeline release. | | 03 | May 2024 | Added details for DRAGEN Array methylation QC pipeline v1.0.0 release. Error correction in the CNV VCF example (CN=4 to CN=5). | | 04 | September 2024 | DRAGEN Array v1.1.0 release | | 05 | February 2025 | DRAGEN Array v1.2.0 release | | 06 | February 2025 | Updated DRAGEN Array v1.2.0 release notes | | 07 | June 2025 |

• Updated DRAGEN Array v1.2.0 release notes: added gtc-to-bedgraph LRR smoothing bug. "Bedgraph Smoothing window size" disabled in cloud interface.

• Added details for DRAGEN Array - Cytogenetics analysis + Emedgene interpretation pipeline 1.2.0 release and corresponding release notes

| # Welcome to DRAGEN Array DRAGEN (Dynamic Read Analysis for GENomics) Array secondary analysis is a powerful bioinformatics software for Illumina Infinium array-based assays. DRAGEN Array uses cutting-edge data analysis tools to provide accurate, comprehensive, and highly efficient secondary analysis to maximize genomic insights and meet your research needs across multiple applications. DRAGEN Array is offered as a local package with command-line interface (no specialized server or hardware required) and as a cloud-based package with an intuitive graphical user interface, as summerized in the table below.
DescriptionKey featuresLocal analysisCloud analysis
GenotypingProvides genotyping results for any human Infinium genotyping array.
  • Greater than 99.5% genotyping accuracy
  • Genotyping VCF in as little as 35 seconds per sample
PGx – CNV callingProvides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Greater than 95% PGx CNV accuracy
PGx – star allele annotationProvides PGx star allele and variant coverage across 2400+ targets for over 50 genes, plus PGx CNV and genotyping outputs for Infinium microarrays with enhanced PGx content.
  • Assess hard to discern PGx genes, including the elusive CYP2D6 with greater than 97% call rate
  • Obtain all PGx analysis results in ~1 minute per sample
Methylation QCProvides high-throughput, quantitative methylation quality control for Infinium methylation arrays.
  • 21 algorithm-based quantitative control metrics with adjustable thresholds
  • Data summary plots
  • Proportion of CG probes passing with user defined p-value threshold
This product documentation describes the installation and setup, analysis execution, and result outputs. For the latest updates and release details, see the [DRAGEN Array Release Notes](/dragen-array-v1.1/reference/release-notes). See [Introducing DRAGEN™ Array 1.0 for Infinium™ Array-Based Pharmacogenomics Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) for additional details on DRAGEN Array genotyping, PGx CNV calling and PGx star allele annotation. # DRAGEN Array Applications The following Types of Analysis are currently supported by DRAGEN Array: * DRAGEN Array – Genotyping * DRAGEN Array – PGx – CNV calling * DRAGEN Array – PGx – Star allele annotation * DRAGEN Array - Methylation QC ### Product & Analysis Compatibility These products/beadchips have been verified to be compatible with the following analyses and versions of DRAGEN Array: | Product | DRAGEN Array Version(s) | Analysis | Genome(s) | | ---------------------------- | ----------------------- | ----------------------------------------- | -------------- | | BovineSNP50\_v3\_A | v1.0, v1.1 | DRAGEN Array – Genotyping | UMD3 | | GDA-8v1-0\_D | v1.0, v1.1 | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | GDA\_PGx-8v1-0\_20042614\_E | v1.0, v1.1 | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | GDA\_PGx-8v1-0\_20042614\_E | v1.0, v1.1 | DRAGEN Array – PGx - CNV calling | GRCh37, GRCh38 | | GDA\_PGx-8v1-0\_20042614\_E | v1.0 | DRAGEN Array – PGx - Star allele annotate | GRCh38 | | GDA\_PGx-8v1-0\_20042614\_G | v1.1 | DRAGEN Array – Genotyping | GRCh38 | | GDA\_PGx-8v1-0\_20042614\_G | v1.1 | DRAGEN Array – PGx - CNV Calling | GRCh38 | | GDA\_PGx-8v1-0\_20042614\_G | v1.1 | DRAGEN Array – PGx - Star allele annotate | GRCh38 | | GSA-24v3-0\_A | v1.0, v1.1 | DRAGEN Array – Genotyping | GRCh37, GRCh38 | | GSA-PGx-48v4-0\_20079540\_E | v1.1 | DRAGEN Array – Genotyping | GRCh38 | | GSA-PGx-48v4-0\_20079540\_E | v1.1 | DRAGEN Array – PGx - CNV Calling | GRCh38 | | GSA-PGx-48v4-0\_20079540\_E | v1.1 | DRAGEN Array – PGx - Star allele annotate | GRCh38 | | GCRA-PGx-24v1-0\_20084467\_C | v1.1 | DRAGEN Array – Genotyping | GRCh38 | | GCRA-PGx-24v1-0\_20084467\_C | v1.1 | DRAGEN Array – PGx - CNV Calling | GRCh38 | | GCRA-PGx-24v1-0\_20084467\_C | v1.1 | DRAGEN Array – PGx - Star allele annotate | GRCh38 | | PRSbooster\_20083382\_A | v1.0, v1.1 | DRAGEN Array – Genotyping | GRCh37 | | EPIC-8v1-0\_B5 | v1.0 | DRAGEN Array – Methylation - QC | GRCh38 | | EPIC-8v2-0\_A2 | v1.0 | DRAGEN Array – Methylation - QC | GRCh38 | | MSA-48v1-0\_20102838\_A1 | v1.0 | DRAGEN Array – Methylation - QC | GRCh38 | ## DRAGEN Array – Genotyping | Item | Description | | ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides genotyping results for any human Infinium genotyping array. | | Variant types detected |

SNV

Indel

| | Sample minimum | 1 sample | | Arrays supported | Any human Infinium genotyping array including custom and semi-custom to create a SNV VCF output. Illumina provides [Genome FASTA Files](/dragen-array-v1.1/product-guides/input-files#toc150786139) required to map to the reference genome for human, genome build 37 and 38. DRAGEN Array Cloud offers additional output formats including Locus Summary and Final Report which are applicable for Infinium arrays for human and non-human species. | | Related Local Commands |

Genotype Call

Genotype GTC-to-VCF

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - Genotyping** from the dropdown. Max 1152 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on cloud and local]

• TBI Index File \[optional on cloud and local]

Per analysis batch:

• Genotype Summary Files

• Final Report \[cloud only]

• Locus Summary \[cloud only]

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array - PGx – CNV calling | Item | Description | | ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides CNV calling on 7 target PGx genes across 10 target regions, plus genotyping outputs. | | Variant types detected |

SNV

Indel

CNV

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

Genotype Call

Genotype GTC-to-VCF \[optional]

Copy-number Call

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - PGx – CNV calling** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• CN Model File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• CNV VCF File

• BedGraph File \[optional on local]

Per analysis batch:

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: No cost download from Illumina Support Site.

Cloud: iCredits to analyze and store data as needed.

| ## DRAGEN Array – PGx – Star Allele Annotation | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Summary | Provides PGx annotation on over 50 genes, plus PGx CNV and genotyping outputs | | Variant types detected |

SNV

Indel

CNV

Star allele diplotype

| | Sample minimum | Minimum of 24 samples with 22 passing QC defined as Log R Dev < 0.2. 96 samples are recommended for best results. | | Arrays supported |

Check Product & Analysis Compatibility here Product & Analysis Compatibility

See Pharmacogenomic Analysis for semi-custom arrays for further detail.

| | Related Local Commands |

Genotype call

Genotype GTC-to-VCF \[optional]

Copy-number call

Star-allele call

Star-allele annotate

| | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array - PGx – Star Allele Annotation** from the dropdown. Max 384 samples are supported. | | Inputs |

• IDAT(s)

• Manifest Files \[may be pre-setup on cloud]

• Cluster File \[may be pre-setup on cloud]

• Genome FASTA Files \[pre-setup on cloud]

• CN Model File \[pre-setup on cloud]

• PGx Database File \[pre-setup on cloud]

• Sample Sheet \[optional on cloud and local]

| | Outputs |

Per sample:

• Genotype Call (GTC) File

• SNV VCF File \[optional on local]

• TBI Index File \[optional on local]

• CNV VCF File

• BedGraph File \[optional on local]

• Star Allele JSON File

Per analysis batch:

• Star Allele CSV File

• Genotype Summary Files

• CN Summary File

• Copy Number Batch File

• Warning/Error Messages

| | Cost |

Local: Per sample analysis.

Cloud: Per sample analysis. iCredits to store data as needed.

Visit the Illumina Product Page to learn more.

| ## DRAGEN Array – Methylation QC | Item | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Summary | Provides methylation QC for Infinium methylation arrays. | | Variant types detected | N/A | | Sample minimum | 1 sample | | Arrays supported | Recommended thresholds and all built-in control probes are available for Methylation Screening Array (MSA) and MethylationEPIC (v1 & v2) originating from iScan. In non-human and custom arrays, availability of built-in QC probes may vary, and failure thresholds must be defined by the user. | | Related Local Commands | Not available on DRAGEN Array Local. | | Related Cloud Specifics | Select Type of Analysis **DRAGEN Array – Methylation – QC** from the dropdown. Adjust customizable thresholds as desired. Further detail can be found in Additional information for [DRAGEN Array Methylation QC](/dragen-array-v1.1/product-guides/dragen-array-cloud-analysis#dragen-array-methylation-qc). A maximum of 1152 samples are supported, [with known limitations](/dragen-array-v1.1/product-guides/dragen-array-cloud-analysis#known-issues) when sample sheet is used. | | Inputs |

• IDAT(s) \[from iScan instrument]

• Manifest Files \[may be pre-setup on cloud]

• IDAT Sample Sheet \[optional on cloud]

| | Outputs |

Per sample:

• Methylation Control Probe Output File

• Methylation CG Output File

Per analysis batch:

• Methylation Sample QC Summary Files

• Methylation Sample QC Summary Plots

• Methylation Principal Component Summary

• Methylation Manifest Files

• Methylation Logs and Error Files

| | Cost | Cloud: [iCredits](https://www.illumina.com/products/by-type/informatics-products/icredits.html) to analyze and store data as needed. | # DRAGEN Array Cloud Analysis ## DRAGEN Array Cloud Analysis Overview DRAGEN Array Cloud utilizes the user-friendly graphical interface of BaseSpace Sequence Hub to simplify DRAGEN Array analysis setup and kickoff. Optional integration with the iScan System allows data to be streamed directly from the instrument to the cloud platform. Analysis data is stored on the Illumina Connected Platform providing secure storage for both microarray and sequencing data. ## Getting Started The following prerequisites are needed to get started with DRAGEN Array Cloud: * **Illumina Connected Analytics subscription**: An ICA Basic, Professional or Enterprise subscription can be used which include access to BaseSpace Sequence Hub. Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) to register the software. * **Workgroup setup**: Workgroups must be created before login. Using a workgroup allows all members of the workgroup to share access to resources, analyses, and data. Learn more about [managing a Workgroup](https://help.basespace.illumina.com/collaborate/manage-workgroups). * Designating a workgroup as ‘Collaborative’ allows projects to be shared with collaborators or Illumina Tech Support to assist with troubleshooting. To create a collaborative workgroup, select the Enable collaborators outside of this domain checkbox during workgroup creation. * **Software consumables**: iCredits can be purchased for storage on the cloud platform and analysis pipelines with a compute charge. Per sample analysis can be purchased for relevant pipelines as listed in section [Applications](/dragen-array-v1.1/overview/our-features). Follow the [Illumina Software Registration Guide](https://stratus-documentation-us-east-1-public.s3.amazonaws.com/downloads/Illumina_Connected_Software_Registration_Guide_final.pdf) (found under *Example 3: Configuring the Software Consumables*) to register the software consumables. * **\[Optional] iScan integration**: The iScan System is integrated with Illumina Connected Platform and can send IDATs for further analysis. The iScan System must be running iScan Control Software version 4.2.1 or later. * [Instructions to Use Illumina Connect Analytics (ICA) with the iScan System](http://support-docs.illumina.com/ARR/iScan/Content/ARR/iScan/UseICA_fIS.htm) * [Troubleshooting iScan integration](#troubleshooting_iscan_integration_1) * **EULA acceptance**: Accept all necessary End User License Agreements in BaseSpace Sequence Hub before scanning begins. * **Internet connection**: For uploading product files or IDATs, a network connection 1 GbE or faster is recommended. Note: Accessioning BeadChips before scanning and starting analysis is no longer a required step and has been automated within the system. ## Running Analysis Before beginning analysis, ensure workgroup context is being used so analysis can be viewed by all members of your workgroup. The name of your workgroup should appear in the top right corner. Use the following steps to run the Microarray Analysis Setup on BaseSpace Sequence Hub: 1. Select the **Runs** tab 2. Select **New Run** 3. Select **Microarray Analysis Setup** 4. Enter the Analysis Name (Figure 1) ![Figure 1. Configuration step of Microarray Analysis Setup](https://3264477228-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FtVdbUe5JiDSR7mSjsBFZ%2Fuploads%2Fgit-blob-f37dcbd018c7953f272f9168dbc113db2bedbc75%2F1.png?alt=media) 5. Use the **Select Project** link to choose the project for your output files\ To select an existing project, click the radio button next to the desired project name. You can also create a project by clicking the **New** button in the project selection window. 6. Select the Type of Analysis\ Further detail of each Type of Analysis is available in section [Applications](/dragen-array-v1.1/overview/our-features). **Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 7. **(Optional)** Create a custom configuration via the "Add Custom Configuration" option in Configuration Settings. Custom configurations must be assigned a name and product files can be uploaded or selected (Figure 2). Custom configuration options vary by Type of Analysis including: * **DRAGEN Array - Genotyping** provides flexibility for turning off/on specific output files and adjusting GenCall score cutoff. Its recommended to turn off VCF output for non-human species and Final Report output for large sample numbers. * **DRAGEN Array - Methylation - QC** provides options to adjust thresholds as detailed in section DRAGEN Array Methylation QC [Threshold Adjustment](#threshold-adjustment). * **DRAGEN Array - Star allele annotation** provides an option to change the default metabolizer status database used from [CPIC](https://cpicpgx.org/) to [DPWG](https://www.pharmgkb.org/page/dpwg). ![Figure 2. Optional Custom Configuration step of Microarray Analysis Setup](https://3264477228-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FtVdbUe5JiDSR7mSjsBFZ%2Fuploads%2Fgit-blob-38cae339e6f946e78b37b0d956d4d62bde623238%2F4.png?alt=media) 8. Select your preferred option in the Configuration Settings drop-down menu\ Configuration setup will vary based on the Type of Analysis selected. More details are available in section [Applications](/dragen-array-v1.1/overview/our-features). 9. Select Next 10. Select either **Import Sample Sheet,** **Select BeadChips,** or **Import IDAT Files** (Figure 3) * **Import Sample Sheet** presents a link to upload sample sheet. Users may download a template sample sheet by selecting the Download Template link. * **Select BeadChips** allows users to select BeadChips from the displayed list of available BeadChips. If selecting specific samples within the BeadChip is desired the Import Sample Sheet option should be used. * **Import IDAT Files** allows users to upload the IDAT files from a local folder to the cloud platform for use with the current and future analyses by users within the same workgroup. 11. Select **Launch Analysis** ![Figure 3. Sample Selection step of Microarray Analysis Setup](https://3264477228-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FtVdbUe5JiDSR7mSjsBFZ%2Fuploads%2Fgit-blob-c5adf73fdf33c60f74ba6583a146064156ce59c6%2F2.png?alt=media) ## View Outputs 1. On the Analyses tab, view the analysis status, e.g., initializing or complete. 2. After the analysis is complete, select the analysis and select the Files tab. 3. From the Files tab, select the Output folder. ## Manage Data The data management tab allows you to view and manage all your scanned IDAT files in the cloud. Before viewing, ensure workgroup context is being used so all data available your workgroup can be seen. The name of your workgroup should appear in the top right corner. For more information, see [BaseSpace Data Management](https://help.basespace.illumina.com/microarray/data-management) ## DRAGEN Array Methylation QC ### Threshold Adjustment When using **DRAGEN Array – Methylation – QC** cloud analysis type, additional customization options will appear after product files are selected within Configuration Settings. Adjustments to these thresholds will be saved as part of the Configuration Setting. Thresholds can be adjusted based on study objectives. Adjusting thresholds will impact the pass or fail status of samples in the output files. Illumina recommends thresholds for MethylationEPIC v1 & v2 and Methylation Screening Array (MSA). Users may use these thresholds as a starting point when defining thresholds for their custom or semi-custom BeadChip or other Infinium Methylation arrays. Further tuning may be required based on BeadChip used, laboratory conditions, iScan settings, bisulfite conversion methods, FPPE sample type, etc. A dataset deemed acceptable to the user based on proportion probes passing can be used for these additional threshold adjustments. To customize thresholds, use the toggle to allow additional thresholds to be displayed and adjust as desired by typing in a numeric value or using the arrows to adjust up or down. Further detail of these thresholds including calculation method can be found in the [Methylation Sample QC Summary Files](/dragen-array-v1.1/product-guides/output-files#methyl_qc_report) section. The recommended thresholds are pre-set within the software for MethylationEPIC and Methylation Screening Array with the following values: | Threshold | Methylation Screening Array | MethylationEPIC | | ----------------------------------- | --------------------------- | --------------- | | Restoration[^1] | 0 | 0 | | StainingGreen | 5 | 5 | | StainingRed | 5 | 5 | | ExtensionGreen | 5 | 5 | | ExtensionRed | 5 | 5 | | HybridizationHighMedium | 1 | 1 | | HybridizationMediumLow | 1 | 1 | | TargetRemoval1 | 1 | 1 | | TargetRemoval2 | 1 | 1 | | BisulfiteConversion1Green | 1 | 1 | | BisulfiteConversion1BackgroundGreen | 0.5 | 1 | | BisulfiteConversion1Red | 1 | 1 | | BisulfiteConversion1BackgroundRed | 0.5 | 1 | | BisulfiteConversion2 | 0.5 | 1 | | BisulfiteConversion2Background | 0.5 | 1 | | Specificity1Green | 1 | 1 | | Specificity1Red | 1 | 1 | | Specificity2 | 1 | 1 | | Specificity2Background | 1 | 1 | | NonpolymorphicGreen | 2.5 | 5 | | NonpolymorphicRed | 3 | 5 | | BgCorrectionOffset | 3000 | 3000 | | PvalThreshold | 0.05 | 0.05 | The first 21 rows in the tables correspond to the 21 control metrics used in the methylation sample QC. See section [Methylation Sample QC Summary Files](/dragen-array-v1.1/product-guides/output-files#methyl_qc_report) for details. ### DRAGEN Array Methylation QC and GenomeStudio Methylation Module Differences DRAGEN Array Methylation QC software provides automated methylation sample QC using assay control probes on the Infinium Methylation Arrays. Unlike the manual visual QC in GenomeStudio, DRAGEN Array ultilizes 21 numerical metrics defined based on the control probes and uses standard thresholds to determine pass/fail status of a sample. Unlike GenomeStuio, probe detection rate (proportion of probes passing at a given p-value threshold) is not utilized to determine sample pass/fail status in DRAGEN Array. For more information, see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note. DRAGEN Array Methylation QC performs background normalization, dye bias correction, and detection p-value calculation differently in comparison to the GenomeStudio Methylation module, leading to differences in probe detection p-values and detection rates. For the GenomeStudio Methylation Module, non-cancer samples at standard DNA input typically have detection rate > 96%. The detection rates from DRAGEN Array Methylation QC are typically lower compared to GenomeStudio, because the detection p-value from DRAGEN Array is more stringent than that from the GenomeStudio Methylation Module. The table below shows example detection rates from the DRAGEN Array Methylation QC software from MSA (Methylation Screening Array) datasets. | Dataset | Min detection rate | Mean detection rate | Sample Count | | ------- | ------------------ | ------------------- | ------------ | | A | 86% | 93% | 220 | | B | 61% | 83% | 951 | | C | 63% | 85% | 34 | | D | 77% | 85% | 22 | Note that only samples passing QC are included and all samples are at or above 50ng DNA input. Detection p-value threshold 0.05. ## Troubleshooting and Additional Support ### Troubleshooting iScan integration The firewall protects the iScan control computer by filtering incoming traffic to remove potential threats. The firewall is enabled by default to block all inbound connections. Keep the firewall enabled and allow outbound connections. For the instrument to connect to BaseSpace Sequence Hub, you will need to add regional platform endpoints and instrument specific endpoints to the allow list on your firewall. Regional endpoints and further detail can be found in [Security and Networking for Illumina instrument control computers](https://support-docs.illumina.com/SHARE/NetworkSecurity/Content/SHARE/FrontPages/NetworkingSecurity.htm). The following table shows the applicable endpoints for the iScan.
EndpointCategoryPurpose
ica.illumina.comRequiredSend IDAT files to ICA
o.ss2.usRequiredCertificate authorization
ocsp.digicert.comRequiredCertificate authorization
ocsp.pki.goog/gsr2RequiredCertificate authorization
ocsp.rootca1.amazontrust.comRequiredCertificate authorization
ocsp.rootg2.amazontrust.comRequiredCertificate authorization
ocsp.sca1b.amazontrust.comRequiredCertificate authorization
fonts.gstatic.comRequiredDisplay fonts
fonts.googleapis.comRecommendedDisplay fonts
cdn.walkme.comRecommendedTelemetry
cdn3.userzoom.comRecommendedTelemetry
dpm.demdex.netRecommendedTelemetry
illuminainc.demdex.netRecommendedTelemetry
illuminainc.tt.omtrdc.netRecommendedTelemetry
smetrics.illumina.comRecommendedTelemetry
google.comRecommendedTelemetry
google-analytics.comRecommendedTelemetry
stats.g.doubleclick.netRecommendedTelemetry
illumina.comOptionalAccess Illumina support material
**Some notes on IDAT fail status:** iScan will mark certain samples with a FAIL status if the registration quality is too poor for that particular section. Selected samples that are marked with FAIL status will be excluded from analysis and there would be no results for that sample, even though IDATs are generated. The registration quality can be found in the metrics.txt file. ### Sharing a project Project sharing allows a user to share files with users outside the workgroup for collaboration or with Illumina Tech Support for troubleshooting. To share a project on BaseSpace Sequence Hub, first set the Workgroup type as ‘Collaborative’ during [Workgroup setup](#getting_started), and then use the following steps to obtain a link to your project. The project can then be accessed by anyone with the link. All files in the project are shared. 1. Navigate to the Projects tab 2. Click the button next to the desired project 3. Select the Share button above to list (Figure 3) 4. Select the Get Link Option to Activate a link for the project 5. Copy the link and send it to the desired recipient(s) **Additional Notes:** * The project owner maintains ownership and write access. If project owner deletes the data, the collaborators lose access to it. * Either sending or receiving domain must be collaborative * -> Workgroup Setup * Must be in the same AWS regional instance * -> "Data cannot be transferred directly between instances, however you can download and share data separately." * For Enterprise domains, use this same method (share-by-link, not share-by-transfer) * ![Figure 3. Share data on BaseSpace Sequence Hub](https://3264477228-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FtVdbUe5JiDSR7mSjsBFZ%2Fuploads%2Fgit-blob-a0ab1b3138aedc34855258f1fe17d816392d2a89%2F3.png?alt=media) [^1]: If FFPE restore kit is used, Restoration threshold should be increased from 0 to 1. # DRAGEN Array Local Analysis ## DRAGEN Array Local Overview DRAGEN Array provides accurate, comprehensive, and efficient analysis of Infinium microarray data. The local command-line interface makes it easy for power users to have granular control and flexibility to support large scale microarray genomic studies. ## Getting Started DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. If new to using command-line interface, please review the [Command-line interface Basics](#toc150786129). ### Computing Requirements Before downloading and installing the software, ensure the following specifications are met for best performance: | Category | Recommendation | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | CPU | 8 cores | | Memory | 16 GB or more | | Hard Drive | 30 GB or more of free disk space | | Operating System |

One of the following:

  • Windows 10 or later – win10-x64
  • CentOS 7 or later, Ubuntu 20.04 or later – linux-x64
| ### Quota Specifications The star-allele call command in DRAGEN Array Local requires quota to run. The quota is charged per sample analyzed and can be purchased on the [Illumina Product Page](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html). Quota is used for all samples analyzed including re-analysis or low-quality samples. The credential provided in the activation email after purchasing should be used as an input to the star-allele call command through the "--license-server-url" option. During runtime, the [logs](/dragen-array-v1.1/product-guides/output-files#toc150786153) will record the remaining quota at the beginning and the end of the analysis. Internet is required to do a software license check and ensure paid quota is available for all samples in the analysis batch. For the software license check, the following endpoint is used: license.edicogenome.com. ## Installation Please follow the steps below to install the software on your compute infrastructure: 1. Click on the latest DRAGEN Array version installation package for the platform of your choice. Installers for Windows and Linux are available on the [Illumina Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html).\ \ Once download is completed, move the DRAGEN Array installation package to the desired folder. Administrative permissions may be required for system folders, for example `/usr/local/bin for Linux`, and `C:\Program Files` for Windows.\ \ **Note**: Throughout the remainder of the document, Linux will be assumed in the examples. 2. Unzip and extract the package. The executable can be found in the dragena subfolder of the software download after extraction. 3. To check that the DRAGEN Array installation was successful, follow these steps: * Open a command prompt (Windows) or terminal (Linux). * \[Optional] Add `/path/to/dragena/`, e.g. `/usr/local/bin/dragena-linux-x64-DAv1.1.0/dragena/`, to your PATH – to access the executable anywhere in the folder structure * Execute the following command: `/path/to/dragena/dragena version`, or if the environmental variable PATH is set: dragena version The version of the software will be displayed in the terminal window when the installation was successful. ## Run DRAGEN Array Local For genotyping analysis, there is no sample minimum required to run analysis. For CNV PGx analysis, a minimum of 24 samples is required to run analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. With a standard hardware specification in section [Computing Requirements](#computing_requirements), up to 500 GDA-ePGx samples can be processed per analysis batch. To optimize performance of the targeted PGx CNV caller and minimize batch effect, it is recommended to: * Group samples in the same assay batch (e.g. whole genome amplication and targeted gene application assay batch) into the same analysis batch. * Avoid combining sample batches processed on different reagent lots. * Analyze batches of 96 samples or more. * Samples processed in a two-week period from multiple library preparation batches can be grouped together to meet size requirement of an analysis batch. In such cases, it is recommended to use the same lot of reagents and instruments used in the workflow. * Use the CN Model and PGx Database File provided as part of the standard product files ## Quick Start Use the following instructions to start the full PGx analysis, covering genotyping, PGx CNV and PGx star allele calling. Refer to [Command Index](#command_index_1) for parameters for all commands. Review section [DRAGEN Array Applications](/dragen-array-v1.1/overview/our-features) for information on input files to use, sample minimums per analysis type and other best practices. Command examples show analysis for a Linux system using folders instead of sample sheets. For Windows users, make sure to substitute the file paths in the commands following windows conventions, e.g., using backslash (\\) instead of forward-slash (/). A sample sheet can be used to select specific samples out of a folder. 1. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/clusterfile.egt --idat-folder /user/IDATs --output-folder /user/gtc` 3. Use the genotype gtc-to-vcf command to create SNV VCF files from the GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/manifest.bpm --csv-manifest /user/productfiles/manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --output-folder /user/vcf` 4. Use the copy-number call command to call PGx CNVs from the GTC files and produce CNV VCF files. It is recommended to use the same output folder used for SNV VCF since the star-allele call command accepts one VCF folder with SNV and CNV VCFs.\ `dragena copy-number call --cn-model /user/productfiles/cnv_model.dat --gtc-folder /user/gtc --output-folder /user/vcf` **Note**: For PGx CNV calling, it is recommended that 96 or more samples passing LogRDev <= 0.2 are included in the analysis. 5. Use the star-allele call command to generate star allele calls using the CNV and SNV VCF files generated by the gtc-to-vcf and copy-number call commands.\ `dragena star-allele call --vcf-folder /user/vcf --database /user/productfiles/GDA_ePGx_E2_DAv1.0.0.zip --output-folder /user/star-alleles --license-server-url https://username:password@license.edicogenome.com` **Note**: For PGx star allele calling, it is recommended to QC the samples and review the samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 to assess the reliability of the analysis. These metrics are provided in the genotyping sample summary file (gt\_sample\_summary.csv). 6. Use the star-allele annotate command to summarize the star alleles and add metabolizer statuses to the star alleles generated by the star-allele call command. Guidelines (CPIC or DPWG) can be specified.\ `dragena star-allele annotate --star-alleles star_alleles.csv --guidelines CPIC --output-folder /user/metabolizer-statuses` 7. \[Optional] Use the copy-number train command to retrain the copy number model.\ `dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/gtc --platform LCG --output-folder /user/productfiles/cnmodelnew` **Note**: DRAGEN Array will overwrite older files if using the same `--output-folder` from a previous analysis.\ If this is not desired, use different `--output-folder` for re-analyses. ## Command Index Use the following syntax when using the command-line interface: `dragena [command] [required parameters] [optional parameters]` ### **copy-number** The root command for actions that act on copy number variants. | Command | Description | | ------------------- | --------------------------------------------------------------------- | | copy-number call | Determines copy number variants given genotypes (GTC to CNV VCF). | | copy-number help | Displays help information for a copy-number command. | | copy-number train | Trains copy number model for a set of samples (GTC to CN Model File). | | copy-number version | Displays version information for copy-number. | #### **copy-number call** The command used to call copy number variants. A batch of 24 samples or more are required for analysis. For a successful analysis, 22 samples must pass QC defined as having log R dev < 0.2. | Option | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --cn-model | \[Required] Specifies the path to the copy number model parameters file (.dat). | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. The command cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). The sample sheet can be in CSV or JSON format. The command cannot be used with --gtc-folder. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the copy-number call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if the GTC folder is provided. | | --version | Displays version information. | #### **copy-number help** Displays help information for a copy-number command. #### **copy-number train** Trains copy number (CN) model for a set of samples. Generate a new CN model if using a customized cluster file (.egt) optimized for the specific data set. * Execute the train command using the data sets that were used to optimize the cluster file. * To use a CN model generated by the train command, the mask file for the manifest must be saved in the same directory as the manifest. * A minimum of 96 samples is required to use the copy-number train command. For optimal performance, at least 150 is recommended. * For best performance, validate the CN model using truth data before using in CN calling. See [Optimizing cluster files and copy number models](#optimizing_cluster_files) for further details. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. Assumes mask file (.msk) is in the same directory. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. Can be in CSV or JSON format. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --platform | \[Required] Specifies which microarray platform generated the data. Set this to 'LCG' for GDA-ePGx, 'EX' for GSAv4-ePGx or GCRA-ePGx. | | --debug | Includes stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --help | Displays help information for the copy-number train command. | | --json-log | Outputs logs in JSON format. Default is false. | | --version | Displays version information. | | --output-folder | \[Optional] The location to output the CN model. By default, the output folder is the current working directory. | #### **copy-number version** Displays version information for copy-number command. ### **genotype** The root command for genotype calling. | Command | Description | | ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- | | genotype call | Determines genotype calls (GTC) from IDAT files. | | genotype gtc-to-bedgraph | Converts GTC to BedGraphs, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files. | | genotype gtc-to-vcf | Converts GTC to VCF. | | genotype help | Displays the help information for the genotype command. | | genotype version | Displays version information for the genotype command. | #### **genotype call** Determines genotype calls (GTC) from IDAT files. | Option | Description | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --cluster-file | \[Required] Specifies the path to the EGT cluster file to use. | | --idat-folder |

\[Required] Specifies the path to the directory where all intensity data IDATs (for the samples to be processed) are located. Must be in IDAT format. Cannot be used with --idat-sample-sheet.

This path also includes the contents of all subdirectories.

| | --idat-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to intensity data IDATs. Can be in CSV or JSON format. Cannot be used with --idat-folder. | | --debug | Includes stack traces in logs. Default is false. | | --gencall-cutoff | GenCall score cutoff to label a NoCall. Default is 0.15. | | --help | Displays help information for the genotype call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --num-threads | Number of parallel threads to run. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the IDAT folder, if the IDAT folder is provided. | | --version | Displays version information. | #### **genotype gtc-to-bedgraph** Converts GTC to BedGraph files, producing BedGraph formatted visualization files from the log R ratio data contained in the GTC intermediate files. | Option | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype (.gtc) files are located. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --debug | Include stack traces in logs. Default is false. | | --help | Displays help information for the genotype gtc-to-bedgraph command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if the GTC folder is provided. | | --version | Displays version information. | #### **genotype gtc-to-vcf** Converts GTC (v5) to [SNV VCF Files](/dragen-array-v1.1/product-guides/output-files#snv_vcf_file). The command is only applicable for [Genotype Call Files](/dragen-array-v1.1/product-guides/output-files#genotype_call_file) produced by DRAGEN Array. | Option | Description | | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | --bpm-manifest | \[Required] Specifies the path to the bead pool manifest in BPM format. | | --csv-manifest | \[Required] Specifies the path to the CSV manifest with SourceSeq column. | | --genome-fasta-file | \[Required] Specifies the path to the genome FASTA file (.fa). Assumes FASTA index file (.fai) is in the same directory. | | --gtc-folder |

\[Required] Specifies the path to the directory where all genotype files (.gtc) are located. Cannot be used with --gtc-sample-sheet.

This path also includes the contents of all subdirectories.

| | --gtc-sample-sheet | \[Required] Specifies the path to a sample sheet containing paths to genotype files (.gtc). Can be in CSV or JSON format. Cannot be used with --gtc-folder. | | --auxiliary-loci | Specifies the path to the VCF file with auxiliary definitions of loci, such as for multi-nucleotide variants. | | --debug | Include stack traces in logs. Default is false. | | --disable-genome-cache | Disables the reference genome cache. | | --filter-loci | Generates a text file containing a list of probe names to be filtered. | | --unsquash-duplicates | Generates unique VCF records for duplicate assays. Default is false. | | --help | Displays help information for the genotype gtc-to-vcf command. | | --json-log | Outputs logs in JSON format. Default is false. | | --no-bgzip | VCFs are not bgzip compressed (.gz) and no tabix index files (.tbi) are output. Default is false. | | --output-folder | \[Optional] Specifies the path to the folder where the output files are saved. The output directory structure matches the directory structure of the GTC folder, if GTC folder is provided. | | --version | Displays version information. | **Squashing duplicates** In the manifest, there can be cases where the same variant is probed by multiple different assays. These assays may be the same design or alternate designs for the same locus. In the default mode of operation, these duplicates will be "squashed" into a single record in the VCF to reflect a true variant rather than probe genotype. The method used to incorporate information across multiple assays is defined further in the [VCF description ](/dragen-array-v1.1/product-guides/output-files#snv-vcf-file). When the `--unsquash-duplicates` option is provided, this "squashing" behavior is disabled, and each duplicate assay will be reported in a separate entry in the VCF file. This option is helpful when you are interested in investigating or validating the performance of individual assays, rather than trying to generate genotypes for specific variants. Note that if a locus has more than two alleles and is also queried with duplicated designs, the duplicates will not be unsquashed (i.e., in the case of multi-allelic variants). **DO NOT** use `--unsquash-duplicates` option if doing star allele calling downstream as that command expects squashed variants. **Genome cache** By default, the entire reference genome will be read into memory. Generally, this will be more efficient than reading data from the indexed reference on disk at the expense of greater memory utilization. For situations in which the genome caching is not desirable (low memory availability or a small input manifest), it is possible to disable this default behavior with the `--disable-genome-cache` option. **Auxiliary loci** Certain classes of variant types (such as multi-nucleotide variants) are not currently supported in the upstream analysis software that produces GTC files. However, it is possible to query this type of variant by creating a SNP design that differentiates the specific multi-nucleotide alleles of interest. For example, if the true source sequence is ATGC\[AT/CG]GTAA This assay could be designed as a SNP assay with the following source sequence ATGC\[A/C]NNNN `gtc-to-vcf` provides an option (`--auxiliary-loci`) to supply a list of auxiliary records (in VCF format) to restore the true alleles for these cases in the output VCF. There are several restrictions around this function * The auxiliary definition must NOT be a multi-allelic variant. * The auxiliary definition must be a multi-nucleotide variant. * There must NOT be multiple array assays (e.g., duplicates) for the locus. **Note:** The genome fasta files for human genomes are provided by illumina on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). #### **genotype help** Displays the help information for a genotype command. #### **genotype version** Displays current DRAGEN Array Local version. ### **help** Displays the help information. ### **version** Displays current DRAGEN Array Local version. ### **star-allele** The root command PGx star allele calling. | Command | Description | | -------------------- | ---------------------------------------------------- | | star-allele call | Determines PGx star allele and variant genotypes. | | star-allele annotate | Annotate PGx gene functions and product JSON report. | | star-allele help | Displays help information for a star allele command. | | star-allele version | Displays version information for star allele. | #### **star-allele help** Displays help information for a star-allele command. #### **star-allele version** Displays version information for star-allele. #### **star-allele call** Calls PGx star allele diplotypes. The SNV VCF files should be generated using the DRAGEN Array gtc-to-vcf command with unsquash-duplicates off (default) and without filter loci. | Option | Description | | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | --database | \[Required] The PGx database file (.zip). | | --license-server-url | \[Required] The license server url with credentials. | | --vcf-folder | \[Required] The directory containing \*.snv.vcf.gz and \*.cnv.vcf.gz files. | | --query-license-quota | During beginning and end of analysis, the license server will be queried for the quotas on the valid license(s) and display the result. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele call command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | #### **star-allele annotate** Annotates and summarizes the star-alleles, specifically for metabolizer statuses and outputs in a consolidated JSON report. Metabolizer status is determined through direct lookup into public PGx guidelines CPIC or DPWG as specified by the user. | Option | Description | | --------------- | -------------------------------------------------------------------------------------------- | | --star-alleles | \[Required] Path to star alleles file (.csv) generated by the call subcommand. | | --guidelines | PGx guidelines to use for annotation. Valid values are ‘CPIC’ and ‘DPWG’. Default is ‘CPIC’. | | --debug | Includes stack traces in logs. Default is false. | | --help | Displays help information for the star-allele annotate command. | | --json-log | Outputs logs in JSON format. Default is false. | | --output-folder | \[Optional] Directory path to output files. Default is the current working directory. | | --version | Displays version information. | ## Troubleshooting and Additional Support ### Tips for using the Command-line interface DRAGEN Array Local utilizes a command-line interface which allows full user control of software functionality and easy automation of tasks. The software is designed to be used by power users and bioinformaticians. When using command-line consider the following tips: * Spaces cannot be part of a file name in a command. If the file name has spaces, use quotes around the file name * To correct a typing error in a previously entered command, use the up arrow to repeat the previous command, then correct the error before re-entering it. * Double check the command. Misspelling, extra, or missing dashes, etc. will cause the command to be unrecognizable by the software. * When entering paths or long names, copy and paste the values to help avoid errors. * If using Windows, use a File Explorer window to navigate to the product file or folder that is needed by the DRAGEN Array Local command. While holding down the shift button on the keyboard, right click the file and select the 'Copy as Path' option. Then paste the copied path into the command prompt to use the file or folder. * To cancel a command while it is running, press Control + C on the keyboard. ### Optimizing cluster files and copy number models A [Cluster File](/dragen-array-v1.1/product-guides/input-files#toc150786136) (.egt) contains the cluster positions of every probe used for genotyping analysis. Illumina provides a standard cluster file for all commercial Infinium BeadChips. It may be desirable to create a custom cluster file if the one provided does not fit the data well or if a semi-custom or custom BeadChip, that do not come with a cluster file, are used. [GenomeStudio 2.0](https://www.illumina.com/techniques/microarrays/array-data-analysis-experimental-design/genomestudio.html) is the software used to create custom cluster files. To facilitate the review and optimization of PGx variant GenTrain cluster positions, a GenomeStudio auxiliary file is provided for each PGx Array product through the [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) and array product files page, e.g. [Infinium Global Diversity Array with Enhanced PGx Product Files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html). The auxiliary file is a tab-delimited text file that can be imported into GenomeStudio through Column Import. The file contains the Infinium Assay to PGx star allele mapping, covering the variants involved in DRAGEN Array PGx star allele calling. When updating the cluster file for pharmacogenomic applications, understand the specifications for the copy number model file before beginning. Before creating a custom cluster file, review the [Infinium Genotyping Data Analysis Technical Note](https://www.illumina.com/Documents/products/technotes/technote_infinium_genotyping_data_analysis.pdf), the [Infinium Arrays Support Webinar Video](https://youtu.be/4JTrbMUbVN0?si=ZgRDLwN6umGBhv2G), and [Custom cluster file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). A [Copy Number (CN) Model File](/dragen-array-v1.1/product-guides/input-files#cn_model_file) (.dat) contains the data needed to make accurate copy number calls for pharmacogenomics. This file is used in the creation CNV VCFs which are inputs to the star allele calling command. Illumina provides a standard CN model file for all commercial PGx Infinium BeadChips. If it is determined the cluster file needs to be customized, the CN Model File should also be updated using the copy-number train command available with DRAGEN Array Local only. i.e., 1. Use GenomeStudio 2.0 to generate a new cluster file. 2. Use the genotype call command to call genotypes and generate GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/manifest.bpm --cluster-file /user/productfiles/new_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/new_gtcs` 3. Use the copy-number train command to retrain the copy number model. **Note: The --platform option can be found in the `Assay Format` heading value from the CSV manifest.**\ `dragena copy-number train --bpm-manifest /user/productfiles/manifest.bpm --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/new_gtcs --platform LCG --output-folder /user/productfiles/new_cnmodel` 4. Use the `new_cnmodel` for subsequent `copy-number call` commands. Note the difference in the cluster file requirement based upon the version of DRAGEN Array used: * **Version 1.1**: If using a CN model with a different cluster file, the software will provide a warning but will proceed with copy number calling. As a result, a user can choose to keep using the commercial CN model from illumina in combination with custom updated EGT file in the PGx analysis. * **Version 1.0**: The same cluster file used for copy number training must be used to generate GTC files for copy number calling. Otherwise, the software will produce an error and exit. For reference, see the [Command Index](#command_index_1) for details of `copy-number train` command. To retrain the CN model file, 96 samples must be used at minimum with 90 of those samples passing QC defined as Log R Dev less than or equal to 0.2. It is recommended to train with at least 150 samples. A greater number of samples can be advantageous, but diminishing returns and longer computation times are seen after 3,000 samples. It is recommended to manually QC the training samples and remove samples that have Log R Dev > 0.2, call rate < 0.99, or TGA Control probe < 1.0 so only the highest quality samples are used in the training. The same samples used to create the new cluster file should be used to retrain the CN Model. To minimize batch effect in the training sample set, the samples should be analyzed in as few batches as possible and come from the same reagent lots. The copy-number train algorithm is designed with the assumption that the copy number distribution resembles the standard population distributions. This ensures the updated CN model file is representative of the normal populations in which it will be used to calculate copy number for key pharmacogenomic targets. ### Pharmacogenomic analysis for semi-custom arrays Semi-custom arrays add additional content or other pre-designed [Infinium booster content](https://www.illumina.com/science/consortia/human-consortia.html) to enhance the commercial array content. This additional content can be analyzed for [genotyping applications](/dragen-array-v1.1/overview/our-features#toc150786108) to obtain information on SNV and indel calls. For [pharmacogenomic applications](/dragen-array-v1.1/overview/our-features#toc150786109), PGx CNV and star allele calls are limited to content included on the commercial Infinium PGx arrays. Additional semi-custom content will not be included in the pharmacogenomic results. When designing a semi-custom array using a commercial Infinium PGx array backbone, such as the Global Diversity Array with enhanced PGx, it is important to retain all backbone content in the design as removing content could decrease the quality of result. Pharmacogenomic analysis for semi-custom arrays should be run using [DRAGEN Array Local](/dragen-array-v1.1/product-guides/dragen-array-local-analysis). Because the PGx CNV calling and PGx star allele calling algorithms are only compatible with commercial product files (see [Applications](/dragen-array-v1.1/overview/our-features)), to fully analyze semi-custom PGx beadchips some steps of the pipeline can be run twice; once with the semi-custom product files (to get complete semi-custom SNV VCF files), and once with the commercial product files (to get the PGx CNV VCF files, PGx Star Allele output, and metabolizer report). The semi-custom product files can be used via the Command-line interface in `genotype call`, `genotype gtc-to-vcf`, and used in GenomeStudio, i.e., 1. Use GenomeStudio 2.0 to prepare a custom cluster file for the semi-custom array, following guidance outlined in [Custom cluster file creation for improved copy number analysis](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/custom-cluster-file-tech-note-m-gl-02142/custom-cluster-file-tech-note-m-gl-02142.pdf). 2. Open a command prompt (Windows) or terminal window (Linux) and navigate to the directory where the software was installed. Or a different, desired directory if the executable was added to the PATH environmental variable. 3. Use the genotype call command to call all semi-custom genotypes and generate custom content GTC files using IDAT files as input.\ `dragena genotype call --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --cluster-file /user/productfiles/semi_custom_clusterfile.egt --idat-folder /user/IDATs --output-folder /user/semi_custom_gtcs` 4. Use the genotype gtc-to-vcf command to create custom content SNV VCF files from the custom content GTC files generated by the genotype call command.\ `dragena genotype gtc-to-vcf --bpm-manifest /user/productfiles/semi_custom_manifest.bpm --csv-manifest /user/productfiles/semi_custom_manifest.csv --genome-fasta-file /user/productfiles/genome.fa --gtc-folder /user/semi_custom_gtcs --output-folder /user/semi_custom_vcfs` 5. Perform [Quick Start](#_toc150786126) steps 1-6 using the **commercial** Infinium PGx array product files to obtain PGx CNV VCFs, star allele calls, and metabolizer status annotations. Keep the GTC files and SNV VCF files generated using the semi-custom product files in clearly labelled folders to distinguish them from the GTC and SNV VCF files generated using the commercial product files. Note that the GTC and SNV VCFs generated using the commercial product files will not contain genotypes for the semi-custom/add-on content. The GTC and SNV VCFs generated using the semi-custom product files cannot be used for downstream PGx analysis commands. # Input Files The following section describes the input files required by DRAGEN Array. Product files (anything other than the IDATs) can be found on the [support site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html). ## IDAT Files For each sample a pair of raw intensity files (.idat) are generated from the iScan System or NextSeq550 (for select arrays). They provide intensities in the red and green channels for each probe on the Infinium array. More information on which arrays can be used with NextSeq550, can be found on the [Illumina Knowledge page on NextSeq550](https://knowledge.illumina.com/microarray/nextseq-500-550/microarray-nextseq-500-550-faq-list/000003871). An IDAT file is identified by the BeadChip Barcode (12-digit unique Sentrix ID, i.e. 123456789101), BeadChip Position (row and column of the sample, i.e. R01C01), and Grn (Green) or Red for the specific channel. ## Manifest Files The CSV and BPM manifest files can be found on the Illumina Support Site for all commercial Infinium BeadChips or on [MyIllumina](http://my.illumina.com/) for custom and semi-custom designs. DRAGEN Array only supports manifest files from the Illumina Support site. For instructions on obtaining manifest files from MyIllumina, see Illumina Knowledge article, [How to access custom array product files (manifest and product definition files) in MyIllumina](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001531). The CSV manifest file (.csv) provides complementary data to the BPM manifest file in a human readable format. It is a required input to the genotype gtc-to-vcf command to enable VCF generation for insertion/deletion variants. `gtc-to-vcf` depends on the presence of accurate mapping information within the manifest, and may produce inaccurate results if the mapping information is incorrect. Mapping information follows the implicit dbSNP standard, where * Positions are reported with 1-based indexing. * Positions in the PAR are reported with mapping position to the X chromosome. * For an insertion relative to the reference, the position of the base immediately 5' to the insertion (on the plus strand) is given. * For a deletion relative to the reference, the position of the most 5' deleted based (on the plus strand) is given. ## Cluster File The cluster file (.egt) is a standard product file provided by Illumina for commercial genotyping products and it is a required input for the genotype call command in DRAGEN Array. Custom cluster files may be required for optimal genotyping performance. See section [Optimizing cluster files and copy number models](/dragen-array-v1.1/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## CN Model File The CN (Copy Number) model file (.dat) is a required input to the copy-number call command to enable accurate copy number calling for pharmacogenomics. Illumina provides a standard CN model file for each PGx array product. See section [Optimizing cluster files and copy number models](/dragen-array-v1.1/product-guides/dragen-array-local-analysis#optimizing_cluster_files) for additional details. ## Mask File The mask file (.msk) is a required input to the copy-number train command to enable accurate copy number training for pharmacogenomics. It does not need to be provided as an explicit input to the command line interface but should reside in the same folder as the BPM manifest. It should have the same base name as the manifest for the product. Illumina provides a mask file for each PGx array product and these can be found on the [product files support page.](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis/downloads.html) ## PGx Database File The PGx database file (.zip) contains the variant mapping information from Infinium PGx arrays to PGx variants. For each gene and each variant used in the star allele definitions of the gene, there is a mapping to the ID field in the SNV VCF file. Each line in the gene mapping file represents a single variant and contains the SNV VCF ID for that variant followed by the HGVS (Human Genome Variation Society) tag for the variant. The PGx database file is array specific and is one of the product files provided by Illumina for each PGx array product. ## Genome FASTA Files The genome FASTA file (.fa) is a text file with the reference genome sequences.The FASTA index file (.fai) contains metadata about chromosomal orchestration within the FASTA file for a particular species. DRAGEN Array PGx calling supports human genome build 37 and 38. The genome FASTA file and FASTA index file are both provided by Illumina for human species and should be stored together in the same input folder. For custom reference genomes, the contig identifiers in the provided genome FASTA file must match exactly the chromosome identifiers specified in the provided manifest. For a standard human product manifest, this means that the contig headers should read ">1" rather than ">chr1". ## IDAT Sample Sheet For local analysis, the IDAT sample sheet can be a CSV or JSON formatted file with direct paths to sample IDAT files. It enables easy analysis of samples from different directories. Example CSV format: `Green IDAT Path,Red IDAT Path` `/path/to/sample1_Grn.idat,/path/to/sample1_Red.idat` `/path/to/sample2_Grn.idat,/path/to/sample2_Red.idat` `/path/to/sample3_Grn.idat,/path/to/sample3_Red.idat` Example JSON format: `[` `{` `"Green IDAT Path": "/path/to/sample1_Grn.idat",` `"Red IDAT Path": "/path/to/sample1_Red.idat"` `},` `{` `"Green IDAT Path": "/path/to/sample2_Grn.idat",` `"Red IDAT Path": "/path/to/sample2_Red.idat"` `},` `{` `"Green IDAT Path": "/path/to/sample3_Grn.idat",` `"Red IDAT Path": "/path/to/sample3_Red.idat"` `},` `]` For cloud analysis, the IDAT sample sheet can be a CSV formatted file. `beadChipName,sampleSectionName` `Beadchip 1 barcode (204753010023), sample section (R01C01)` `Beadchip 1 barcode (204753010023), sample section (R02C01)` `Beadchip 2 barcode (204753010024), sample section (R01C01)` `Beadchip 2 barcode (204753010024), sample section (R02C01)` For DRAGEN Array Methylation QC on cloud, additional optional sample sheet fields are available. Following Sample\_Group, any number of additional columns can be added to include meta data fields such as sex, sample type, plate and well information, etc. Additional columns added after the Sample\_Group column may have user-defined column header values. The Sample\_ID field and any additional metadata added will be replicated in the Sample QC Summary output files. The Sample\_Group field will be used to populate the PCA Control Plot within the Sample QC Summary Plots file and the Principal Component Summary file. For the PCA Control Plot, each sample group will be assigned a unique color. Samples assigned to the same Sample\_Group value will be the same color in the PCA Control Plot. `beadChipName,sampleSectionName,Sample_ID,Sample_Group,MetaData1` `Beadchip 1 barcode (204753010023), sample section (R01C01),NA1231,Group1,F` `Beadchip 1 barcode (204753010023), sample section (R02C01),NA1232,Group2,F` `Beadchip 2 barcode (204753010024), sample section (R01C01),NA1233,Group2,M` `Beadchip 2 barcode (204753010024), sample section (R02C01),NA1234,Group1,M` ## GTC Sample Sheet The GTC sample sheet is a CSV or JSON formatted file with direct paths to sample GTC files. It enables easy analysis of samples from different directories. Example CSV format: `GTC Path` `/path/to/sample1.gtc` `/path/to/sample2.gtc` `/path/to/sample3.gtc` Example JSON format: `[` `{` `"GTC Path": "/path/to/sample1.gtc"` `},` `{` `"GTC Path": "/path/to/sample2.gtc"` `},` `{` `"GTC Path": "/path/to/sample3.gtc"` `}` `]` ## Input File Summary Table In addition to the input files, there are set of intermediate files, including GTC, SNV VCF, CNV VCF and PGx CSV, which are outputs of some DRAGEN Array Local commands and inputs to other commands. The table below summarizes the input files or intermediate file, their sources, and the associated DRAGEN Array Local commands and options. | Input File | Source | Command | Option | | ----------------- | ------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | ------------------- | | IDAT | User provided from scanning instrument | genotype call | --idat-folder | | CSV Manifest | Product file from Illumina | genotype gtc-to-vcf | --csv-manifest | | BPM Manifest | Product file from Illumina |

copy-number train

genotype call

genotype gtc-to-bedgraph

genotype gtc-to-vcf

| --bpm-manifest | | Cluster File | Product file from Illumina or user created using GenomeStudio | genotype call | --cluster-file | | CN Model | Product file from Illumina or user created using DRAGEN Array Local | copy-number call | --cn-model | | PGx Database | Product file from Illumina | star-allele call | --database | | Genome FASTA | Product file from Illumina |

genotype gtc-to-vcf

copy-number train

| --genome-fasta-file | | IDAT Sample Sheet | User provided | genotype call | --idat-sample-sheet | | GTC Sample Sheet | User provided |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

copy-number call

copy-number train

| --gtc-sample-sheet | | GTC | DRAGEN Array output from genotype call |

genotype gtc-to-bedgraph

genotype gtc-to-vcf

copy-number call

copy-number train

| --gtc-folder | | SNV and CNV VCF | DRAGEN Array output from genotype gtc-to-vcf and copy-number call | star-allele call | --vcf-folder | | PGx CSV | DRAGEN Array output from star-allele call | star-allele annotate | --star-alleles | # Output Files The following section describes the outputs produced by DRAGEN Array. ## CNV VCF File DRAGEN Array produces one CNV variant call file (VCF) (\*.cnv.vcf) per sample to report the CN status on the gene and sub gene level, along with the CN events for PGx targets. The CNV VCF output file follows the standard VCF format. The QUAL field in the VCF file measures the CNV call quality. The CNV call quality is a Phred-scaled score capped at 60 and the minimal value is 0. Low quality calls (QUAL<7) are flagged by the Q7 filter. Low quality samples with LogRDev greater than a threshold 0.2 are flagged with the SampleQuality flag. The CNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the TBI Index File. To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The CNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The CNV VCF output file includes the following content. `##fileformat=VCFv4.1` `##source=dragena 1.1.0` `##genomeBuild=38` `##reference=file:///hg38_with_alt/hg38_nochr_MT.fa` `##FORMAT=` `##FORMAT=` `##ALT=` `##FILTER=` `##FILTER=` `##INFO=` `##INFO=` `##INFO=` `##INFO=` `##CNVOverallPloidy=1.8` `##CNVGCCorrect=True` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 204619760001_R01C01` `1 109687842 CNV:GSTM1:chr1:109687842:109693526 N 60 PASS CNVLEN=5685;PROBE=124;END=109693526;SVTYPE=CNV CN:NR 2:0.966631132771593` `4 68537222 CNV:UGT2B17:chr4:68537222:68568499 N 60 PASS CNVLEN=31278;PROBE=383;END=68568499;SVTYPE=CNV CN:NR 0:0.376696837881692` `10 133527374 CNV:CYP2E1:chr10:133527374:133539096 N 60 PASS CNVLEN=11723;PROBE=194;END=133539096;SVTYPE=CNV CN:NR 2:0.980059731860893` `16 28615068 CNV:SULT1A1:chr16:28603587:28613544 N 57 PASS CNVLEN=8315;PROBE=164;END=28623382;SVTYPE=CNV CN:NR 2:0.980552325552963` `19 40844791 CNV:CYP2A6.intron.7:chr19:40844791:40845293 N 60 PASS CNVLEN=503;PROBE=38;END=40845293;SVTYPE=CNV CN:NR 2:0.9663775484762` `19 40850267 CNV:CYP2A6.exon.1:chr19:40850267:40850414 N 60 PASS CNVLEN=148;PROBE=21;END=40850414;SVTYPE=CNV CN:NR 2:0.9663775484762` `22 42126498 CNV:CYP2D6.exon.9:chr22:42126498:42126752 N 48 PASS CNVLEN=255;PROBE=370;END=42126752;SVTYPE=CNV CN:NR 2:0.981703411438716` `22 42129188 CNV:CYP2D6.intron.2:chr22:42129188:42129734 N 10 PASS CNVLEN=547;PROBE=333;END=42129734;SVTYPE=CNV CN:NR 2:0.965498002434641` `22 42130886 CNV:CYP2D6.p5:chr22:42130886:42131379 N 60 PASS CNVLEN=494;PROBE=172;END=42131379;SVTYPE=CNV CN:NR 2:0.970341562236357` `22_KI270879v1_alt 270316 CNV:GSTT1:chr22_KI270879v1_alt:270316:278477 N 60 PASS CNVLEN=8162;PROBE=91;END=278477;SVTYPE=CNV CN:NR 2:1.01191145130511` ## SNV VCF File The software produces one genotyping variant call file (\*.snv.vcf) file per sample, covering single nucleotide variants (SNV) and indels for the sample. It reports GenCell score (GS), B Allele Frequency (BAF), and Log R Ratio (LRR) per variant. The VCF file output follows [VCF4.1 format](https://samtools.github.io/hts-specs/VCFv4.1.pdf). Some additional details: * Genotypes are adjusted to reflect the sample ploidy. Calls are haploid for loci on Y, MT, and non-PAR chromosome X for males. * Multiple SNPs in the input manifest which are mapped to the same chromosomal coordinate (e.g. tri-allelic loci or duplicated sites) are collapsed into one VCF entry and a combined genotype generated. To produce the combined genotype, the set of all possible genotypes is enumerated based on the queried alleles. Genotypes which are not possible based on called alleles and assay design limitations (e.g. Infinium II designs cannot distinguish between A/T and C/G calls) are filtered. If only one consistent genotype remains after the filtering process, then the site is assigned this genotype. Otherwise, the genotype is ambiguous (more than 1) or inconsistent (less than 1) and a no-call is returned. * Certain SNV and indel calls can be skipped when reported in the VCF. Skipped data can include unmapped loci, intensity-only probes used for CNV identification, and indels that do not map back to the genome. See [Warning/Error Messages and Logs](#toc150786153) for messages that may be seen with DRAGEN Array Local related to the skipped data. * The BAF and LRR are oriented with Ref as A and Alt as B relative to the reference genome, while GS is agnostic to the reference genome. Users familiar with GenomeStudio may observe BAF and LRR reported in the VCF as 1 minus the value reported in GenomeStudio depending on the Ref Alt allele orientation with the reference genome. GenomeStudio reports these values based on the information in the manifest without knowledge of the reference genome. * The SNV VCF files are by default bgzipped (Block GZIP) and have the “.gz” extension. The compression saves storage space and facilitates efficient lookup when indexed with the [TBI Index File](#toc150786155). To view these files as plain text, they can be uncompressed with [bgzip](http://www.htslib.org/doc/bgzip.html) from Samtools or other third-party tools. The SNV VCF must be bgzipped and indexed to be used in downstream DRAGEN Array commands, such as star allele calling. The SNV VCF output file includes the following content. The last row shows an example of variant call. `##fileformat=VCFv4.1` `##source=dragena 1.1.0` `##genomeBuild=38` `##reference=file:///genomes/38/genome.fa` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##FORMAT=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `##contig=` `#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT 202937470021_R06C01` `1 2290399 rs878093 G A . PASS . GT:GS:BAF:LRR 0/1:0.7923:0.50724137:0.14730307` ## Genotype Call (GTC) File The genotype call algorithm produces one genotype call file (.gtc) per sample analyzed. The Genotype Call (GTC) file contains the small variant (SNV and indel) genotype for each marker specified by the product and sample quality metrics. The sample marker location is not included and must be extracted from the manifest file. Binary proprietary format can be parsed using the Illumina open-source tool [BeadArray Library File Parser](https://github.com/Illumina/BeadArrayFiles). ## BedGraph File The BedGraph file contains the log R ratios from the genotyping algorithm for use in visual tools. ## Star Allele CSV File The Star Allele CSV file is an intermediate file generated by the star-allele call command and serves as the input to the star-allele annotate command. It contains all the star allele calls for all samples in a run. Each row in the file provides either a star allele diplotype or simple variant call for a PGx-related gene. Star allele diplotype calls for a sample and a gene may span multiple lines where alternative solutions can be listed. The Star Allele CSV file also contains meta information marked by # at the top of the file for the genome build and PGx database used for the star allele calling. The star\_allele.csv file contains the following details per sample: | Field | Description | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Sample | Sentrix barcode and position of the sample. | | Rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | Gene or Variant | The gene symbol, or gene symbol plus rsID for variants. | | Type | ‘Haplotype’ (star allele) or ‘Variant’ PGx calling type. | | Solution | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | Solution Long |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | Supporting Variants |

All variants present in the array that support the star allele solution. The field has the following format: Long Solution Star Allele: (Supporting Variants).

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome, Location, Reference allele, Alternative allele, Genotype, GenCall score (GS), and B-allele frequency (BAF).

| | Missing/Masked Core Variants | All variants not present in the array or not called in the SNV VCF file for the star allele. The field has the following format: Long Solution Star-Allele: (Missing Variants). | | All Missing Variants in Array | All core definition variants that are not on the array or are not called in the SNV VCF along with the associated star alleles that are impacted. The field has the following format: Missing Variant: (List of impacted star alleles). | | Collapsed Star-Alleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | Score | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Raw Score: | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | Copy Number Solution | Estimated copy number for each gene region. The field has the following format: Gene Region: Copy Number. | Below is an example of the first 4 columns from a star allele CSV file: `Sample,Rank,Gene or Variant,Type,Solution` `204650490282_R02C01,1,CYP2C9,Haplotype,*9/*11` `204650490282_R02C01,1,CYP2C19,Haplotype,*2/*10` ## Genotype Summary Files The software produces genotype summary files (gt\_sample\_summary.csv and gt\_sample\_summary.json) that contains the following details per sample: * Sample ID * Sample Name * Sample Folder * Autosomal Call Rate * Call Rate * Log R Ratio Std Dev * Sex Estimate * TGA\_Ctrl\_5716 Norm R The TGA\_Ctrl\_5716 Norm R field is specific to PGx products (e.g., Global Diversity Array with enhanced PGx). The field value is the Normalized R value of one probe and is meant as an assay control where < 1 indicates the sample failed in the TGA (Targeted Gene Amplification) process. If the product does not have this probe, it is not included in the gt\_sample\_summary. ## Final Report DRAGEN Array Cloud produces a Final Report (gtc\_final\_report.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus per sample: | Field | Description | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | SNP Name | SNP identifier. | | SNP | SNP alleles as reported by assay probes. Alleles on the Design strand (the ILMN strand) are listed in order of Allele A/B. | | Sample ID | Sample identifier. | | Allele 1 – Top | Allele 1 corresponds to Allele A and are reported on the Top strand. | | Allele 2 – Top | Allele 2 corresponds to Allele B and are reported on the Top strand. | | Allele 1 – Forward | Allele 1 corresponds to Allele A and are reported on the Forward strand. | | Allele 2 – Forward | Allele 2 corresponds to Allele B and are reported on the Forward strand. | | Allele 1 – Plus | Allele 1 corresponds to Allele A and are reported on the Plus strand. | | Allele 2 – Plus | Allele 2 corresponds to Allele B and are reported on the Plus strand. | | GC Score | Quality metric calculated for each genotype (data point), and ranges from 0 to 1. | | GT Score | The SNP cluster quality. Score for a SNP from the GenTrain clustering algorithm. | | Log R Ratio | Base-2 log of the normalized R value over the expected R value for the theta value (interpolated from the R-values of the clusters). For loci categorized as intensity only; the value is adjusted so that the expected R value is the mean of the cluster. | | B Allele Freq | B allele frequency for this sample as interpolated from known B allele frequencies of 3 canonical clusters: 0, 0.5 and 1 if it is equal to or greater than the theta mean of the BB cluster. B Allele Freq is between 0 and 1, or set to NaN for loci categorized as intensity only. | | Chr | Chromosome containing the SNP. | | Position | SNP chromosomal position. | *Note: Analyses on products with large numbers of loci (>1 Million) and large numbers of samples (>100) yield a large (50+ Gigabyte) Final Report that are difficult to download and review. It’s recommended to create analysis configurations that do not produce this report if large batches are desired.* For more information on interpreting DNA strand and allele information, see Illumina Knowledge article [How to interpret DNA strand and allele information for Infinium genotyping array data](https://knowledge.illumina.com/microarray/general/microarray-general-reference_material-list/000001489). ## Locus Summary DRAGEN Array Cloud produces a Locus Summary (locus\_summary.csv) per analysis batch similar to the one available in GenomeStudio. It contains the following details per locus: | Field | Description | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Locus\_Name | Locus name from the manifest file. | | Illumicode\_Name | Locus ID from the manifest file. | | #No\_Calls | Number of loci with GenCall scores below the call region threshold. | | #Calls | Number of loci with GenCall scores above the call region threshold. | | Call\_Freq | Call frequency or call rate calculated as follows: #Calls/(#No\_Calls + #Calls) | | A/A\_Freq | Frequency of homozygote allele A calls. | | A/B\_Freq | Frequency of heterozygote calls. | | B/B\_Freq | Frequency of homozygote allele B calls. | | Minor\_Freq | Frequency of the minor allele. | | Gentrain\_Score | Quality score for samples clustered for this locus. | | 50%\_GC\_Score | 50th percentile GenCall score for all samples. | | 10%\_GC\_Score | 10th percentile GenCall score for all samples. | | Het\_Excess\_Freq | Heterozygote excess frequency, calculated as (Observed -Expected)/Expected for the heterozygote class. If $f\_{ab}$ is the heterozygote frequency observed at a locus, and p and q are the major and minor allele frequencies, then het excess calculation is the following: $(f\_{ab} - 2pq)/(2pq + \varepsilon)$ | | ChiTest\_P100 | Hardy-Weinberg p-value estimate calculated using genotype frequency. The value is calculated with 1 degree of freedom and is normalized to 100 individuals. | | Cluster\_Sep | Cluster separation score. | | AA\_T\_Mean | Normalized theta angles mean for the AA genotype. | | AA\_T\_Std | Normalized theta angles standard deviation for the AA genotype. | | AB\_T\_Mean | Normalized theta angles mean for the AB genotype. | | AB\_T\_Std | Standard deviation of the normalized theta angles for the AB genotype. | | BB\_T\_Mean | Normalized theta angles mean for the BB genotypes. | | BB\_T\_Std | Standard deviation of the normalized theta angles for the BB genotypes. | | AA\_R\_Mean | Normalized R value mean for the AA genotypes. | | AA\_R\_Std | Standard deviation of the normalized R value for the AA genotypes. | | AB\_R\_Mean | Normalized R value mean for the AB genotypes. | | AB\_R\_Std | Standard deviation of the normalized R value for the AB genotypes. | | BB\_R\_Mean | Normalized R value mean for the BB genotypes. | | BB\_R\_Std | Standard deviation of the normalized R value for the BB genotypes. | | Plus/Minus Strand | Designated "+" or "-" with respect to the reference genome strand. "U" designates unknown. | ## CN Summary File The sample summary contains per sample key stats for each sample in a batch that contains the following details per sample: * Sample ID * Sample Name * Sample Folder ## Copy Number Batch File The copy number batch summary file (cn\_batch\_summary.csv) shows the total copy number gain, loss, and neutral (CN=2) values for each target region across all the samples in the analysis. Example copy number batch summary file content: `Target Region,Total CN gain,Total CN loss,Total CN neutral` `CYP2A6.exon.1,0,1,47` `CYP2A6.intron.7,0,1,47` `CYP2D6.exon.9,2,4,42` `CYP2D6.intron.2,7,2,39` `CYP2D6.p5,13,2,33` `CYP2E1,2,0,46` `GSTM1,0,42,6` `GSTT1,0,33,15` `SULT1A1,0,0,48` `UGT2B17,0,34,14` `All Target Regions,24,119,337` ## Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Manifest file used to generate GTC is not the same as the manifest file used to generate the CN model. * FASTA files and FASTA index files do not match. For the following scenarios, the software reports messages to the terminal output (as either a warning or an error): * Indel processing for GTC to VCF conversion failed. * The input folder does not contain the required input files. * An input file is corrupt. Examples of such notifications can include the following: | Error | Type | Cause | | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Failed to normalize and gencall sample: {sample\_id}, it will be skipped. Error: The given key '{loci\_id}' was not present in the dictionary. | Warning | This generally occurs because of a mismatch between the manifest (bpm) and cluster file (egt) (i.e., the cluster file was generated via a different manifest). To remedy the issue, use the manifest and cluster files intended for use together. | | Reference allele is not queried for locus: {identifier} | Warning | True reference allele does not match any alleles in the manifest. The error is common for MNVs and will be addressed in future versions of the software. | | Skipping non-mapped locus: {identifier} | Warning | Locus has no chromosome position (usually 0) These loci may be used for quality purposes or CNV calling only. | | Skipping intensity only locus: {identifier} | Warning | Similar to non-mapped loci, intensity only probes have applications outside creating variants for SNV VCFs such as CNV calling. | | Skipping indel: {identifier} | Warning | Indel context (deletion/insertion) could not be determined. | | Failed to process entry for record: {identifier} | Warning | Unable to determine reference allele for indel. | | Incomplete match of source sequence to genome for indel: {identifier} | Warning | Indel not properly mapped to the reference genome. | | Failed to combine genotypes due to ambiguity - exm1068284 (InfiniumII): TT, ilmnseq\_rs1131690890\_mnv (InfiniumII): AA, rs1131690890\_mnv (InfiniumII): AA | Warning | Detailed information about a NoCall ("./.”) in the VCF as a result of combining multiple probes that assay the same variant with conflicting results. The example here is two probes with homozygous REF genotypes (AA) and one probe with homozygous ALT probe (TT) | | Cluster file ({GTC.egt}) is not the same as CN Model Cluster file ({CN\_Model.egt}). | Warning | Cluster file used to generated GTCs used for copy number calling is not the same as was used for the GTCs used during copy number training that created the input CN model. Though CNV model is robust to minor cluster file updates, CNV training should be considered when there are significant updates in the cluster file. To remove the warning, copy number training needs to be re-run with the new GTCs generated via the new cluster file during genotyping, a different CN model with the expected cluster file needs to be used, or different GTCs should be used for copy number calling that were generated using the same cluster file as was used during the generation of the input CN model. | |

{numPassingSamples} sample(s) passed QC.

Requires at least {minPassingSamples} samples to proceed.

| Error | CNV calling is batch dependent and requires a certain number of samples with high-quality to make accurate calls. More high-quality samples need to be added to analysis batch to resolve error. | | Invalid manifest file path {manifestPath} | Error | Application could not find manifest file provided or user error. | | Failed to load cluster file: {e.Message} | Error | Corrupted file or unsupported version. | ## Star allele JSON File The star allele JSON file is produced per sample. It contains the fields present in the [star allele CSV file](#star_allele_csv) as well as additional meta data and annotations. Fields included in the star allele JSON header are described below. | Field | Description | | ------------------------- | ------------------------------------------------------------------------------------------------------------- | | softwareVersion | DRAGEN Array software version, e.g. dragena 1.0.0. | | genomeBuild | Genome build, e.g hg38. | | starAlleleDatabaseSources | Public databases with versions used as the sources of the star allele definitions and population frequencies. | | phenotypeDatabaseSources | Public databases with versions used as the sources of the star allele phenotypes. | | mappingFile | The PGx database file used for the star allele calling. | | pgxGuideline | The PGx guidelines used for metabolizer status/phenotype annotations, e.g. CPIC or DPWG | | sampleId | Sentrix barcode and position of the sample. | | locusAnnotations | The star allele call information. | Fields included in the star allele call (locusAnnotations) information are described below. | Field | Description | | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | gene | The gene symbol. | | callType | ‘Star Allele’ or ‘Variant’ PGx calling type. | | genotype | Most likely star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotypeDatabaseAnnotation | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), the variant ID (id), and the associated star allele IDs (alleleIds).

| | candidateSolutions | The set of alternative star allele calling solutions, this is only relevant for genes of the ‘Star Allele’ call type. | | missingVariantSites | All core variants that are not available (e.g. not on the array, or no calls in the SNV VCF) for star allele calling for this gene. For star alleles, the field provides an array (list) of variant "id" and impacted "alleleIds" pairs | | allelesTested | Alleles that are covered by the star allele caller. The capability to call star alleles is also dependent on array content coverage and data quality. This field is defined by the array's content and will be the same across all samples. | Fields included in the candidateSolution section, only available for star allele call type, are described below. | Field | Description | | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | rank | Rank of a single star allele solution for a gene. The top solution based on quality score is ranked as 1 with the alternative solutions ranked lower. | | genotype | Star allele or variant solution. If diploid, variant solutions have the format of Allele1/Allele2. | | activityScore | Activity score annotation of the determined genotype of the gene determined based on public PGx guidelines CPIC or DPWG. | | phenotype | Metabolizer status and function annotations of the determined genotype of the gene based on lookup into public PGx guidelines CPIC or DPWG per user choice. | | qualityScore | Quality score of the solution including the population frequency of PGx alleles. The score ranges from 0 to 1. | | rawScore | Raw quality score of the solution without including the population frequency of PGx alleles. The score ranges from 0 to 1. | | alleles | The composite alleles of the candidate genotype solution. | | solutionLong |

Long format solution for star alleles. The field has the following format: Structural Variant Type: Underlying Star allele.

An example of a long solution is: Complete: CYP2D64, Complete: CYP2D610, CYP2D668: CYP2D64 where there are two complete alleles that have CYP2D64 and CYP2D610 haplotypes and one CYP2D668 structural variant that has a CYP2D64 haplotype configuration.

| | supportingVariants |

All variants present in the array that support the star allele solution. The field provides an array (list) of supporting Variants.

Each supporting variant is listed with essential information extracted from the SNV VCF to assist with troubleshooting, including Chromosome (chrom), Location (pos), Reference allele (ref), Alternative allele (alt), Genotype (gt), GenCall score (gs), B-allele frequency (baf), and the variant ID (id).

| | missingVariantSites | All variants not present in the array or not called in the SNV VCF file for the star allele solution. The field provides an array (list) of missing variants. | | collapsedAlleles |

Star alleles that cannot be distinguished from the solution star allele given the input array’s content. The field has the following format: Long Solution Star-Allele: (List of collapsed star alleles).

The most frequent star allele based on the population frequency of PGx alleles will be the star allele in the solution.

| | copyNumberRegions | Gene regions for the copy numbers listed in CopyNumberSolution. | | copyNumberSolution | Estimated copy number for each gene region listed in CopyNumberRegions | Example of JSON file content: ```json { "softwareVersion": "dragena 1.1.0+9f82ed31d8c17e42b80f67a3e2b271f1a873e1d1", "genomeBuild": "38", "starAlleleDatabaseSources": [ "PharmVar Version: 6.1", "PharmGKB Database Version: Snapshot-2024.05.16", "UGT Alleles Nomenclature: 2010.12.21", "The Human Cytochrome P450 (CYP) Allele Nomenclature Database, July 2024" ], "phenotypeDatabaseSources": [ "CPIC Database Version: 1.38.0", "DPWG Database Version: June 2023" ], "mappingFile": "DRAGENA-549-fix-annotate-sha.e56e884ed1f2d118e796cdab578ab895456bb94e.zip", "pgxGuideline": "CPIC", "sampleId": "207883050020_R08C03", "locusAnnotations": [ { "gene": "CYP2C9", "callType": "Star Allele", "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9999", "supportingVariants": [], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*1", "activityScore": "2", "phenotypeDatabaseAnnotation": "CYP2C9 Normal Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9999, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [], "missingVariantSites": [], "collapsedAlleles": "" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94938719T>G", "alleleIds": "*80" }, { "id": "NC_000010.11:g.94938788C>T", "alleleIds": "*83" }, { "id": "NC_000010.11:g.94938800G>A", "alleleIds": "*76" }, { "id": "NC_000010.11:g.94941975G>A", "alleleIds": "*77" }, { "id": "NC_000010.11:g.94942243T>G", "alleleIds": "*78" }, { "id": "NC_000010.11:g.94942306C>T", "alleleIds": "*72" }, { "id": "NC_000010.11:g.94942308C>T", "alleleIds": "*73" }, { "id": "NC_000010.11:g.94942309G>T", "alleleIds": "*27" }, { "id": "NC_000010.11:g.94947939G>T", "alleleIds": "*74" }, { "id": "NC_000010.11:g.94949145C>T", "alleleIds": "*82" }, { "id": "NC_000010.11:g.94949163del", "alleleIds": "*85" }, { "id": "NC_000010.11:g.94972183A>T", "alleleIds": "*81" }, { "id": "NC_000010.11:g.94981258C>T", "alleleIds": "*79" }, { "id": "NC_000010.11:g.94986136A>C", "alleleIds": "*75" }, { "id": "NC_000010.11:g.94986174G>C", "alleleIds": "*84" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*20,*21,*22,*23,*24,*25,*26,*27,*28,*29,*30,*31,*32,*33,*34,*35,*36,*37,*38,*39,*40,*41,*42,*43,*44,*45,*46,*47,*48,*49,*50,*51,*52,*53,*54,*55,*56,*57,*58,*59,*60,*61,*62,*63,*64,*65,*66,*67,*68,*69,*70,*71,*72,*73,*74,*75,*76,*77,*78,*79,*80,*81,*82,*83,*84,*85" }, { "gene": "CYP2C19", "callType": "Star Allele", "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": "0.9999", "rawScore": "0.9958", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G", "alleleIds": "*1" }, { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G", "alleleIds": "*2" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A", "alleleIds": "*2" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G", "alleleIds": "*2" } ], "candidateSolutions": [ { "rank": 1, "genotype": "*1/*2", "activityScore": "n/a", "phenotypeDatabaseAnnotation": "CYP2C19 Intermediate Metabolizer", "qualityScore": 0.9999, "rawScore": 0.9958, "alleles": [ { "solutionLong": "Complete: *1", "supportingVariants": [ { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": "NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "" }, { "solutionLong": "Complete: *2", "supportingVariants": [ { "chrom": "10", "pos": "94775367", "ref": "A", "alt": "G", "gt": "0/1", "gs": "0.2191", "baf": "0.4690612", "id": "NC_000010.11:g.94775367A>G" }, { "chrom": "10", "pos": "94781859", "ref": "G", "alt": "A", "gt": "0/1", "gs": "0.3351", "baf": "0.66212183", "id": " NC_000010.11:g.94781859G>A" }, { "chrom": "10", "pos": "94842866", "ref": "A", "alt": "G", "gt": "1/1", "gs": "0.2669", "baf": "1", "id": " NC_000010.11:g.94842866A>G" } ], "missingVariantSites": [], "collapsedAlleles": "*2.001" } ], "copyNumberRegions": "p5,exon.1,intron.1,exon.2,intron.2,exon.3,intron.3,exon.4,intron.4,exon.5,intron.5,exon.6,intron.6,exon.7,intron.7,exon.8,intron.8,exon.9,p3", "copyNumberSolution": "2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2" } ], "missingVariantSites": [ { "id": "NC_000010.11:g.94762715T>C", "alleleIds": "*34" } ], "allelesTested": "*1,*2,*3,*4,*5,*6,*7,*8,*9,*10,*11,*12,*13,*14,*15,*16,*17,*18,*19,*22,*23,*24,*25,*26,*28,*29,*30,*31,*32,*33,*34,*35,*38,*39" } ``` ## TBI Index File The TBI (TABIX) index file is associated with the bgzipped VCF files. It allows for data line lookup in VCF files for quick data retrieval. The format is a tab-delimited genome index file developed by Samtools as part of the HTSlib utilities. For more information, visit the [Samtools](http://www.htslib.org/doc/tabix.html) website. ## Methylation Control Probe Output File The software produces a control probe output file ({BeadChipBarcode}\_{Position}\_ctrl.tsv.gz) per sample that includes the raw methylated and unmethylated values for each control probe. Each control probe has an address, type, color channel, name, and probe ID. It also provides the raw signal for methylated green (MG), methylated red (MR), unmethylated green (UG) and unmethylated red (UR). The file can help identify which probes are available on a given BeadChip. ## Methylation CG Output File The software produces a CG output file ({BeadChipBarcode}\_{Position}\_cgs.tsv.gz) per sample that includes beta values, m-values and detection p-values for each CG site. Beta values measure methylation levels in a linear fashion for easy interpretation. Unmethylated probes are close to zero and methylated probes are close to 1. M-values are a log transformed beta value which provides a more representative measure of methylation. Detection p-values measure the likelihood that the signal is background noise. It is recommended that p-value >0.05 are excluded from analysis as they are likely background noise. see [High-throughput Infinium methylation array QC using DRAGEN Array Methylation QC](https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/dragen-array-methylation-qc-tech-note-m-gl-02644/dragen-array-methylation-qc-tech-note-m-gl-02644.pdf) software tech note for further detail on calculation of these metrics. ## Methylation Sample QC Summary Files The software produces methylation sample QC summary in .xlsx and .tsv file formats (sample\_qc\_summary.xlsx and sample\_qc\_summary.tsv) per analysis batch, which provides per sample QC data for all samples in the batch. The QC summary provides details on 21 controls metrics (see tables below), which are computed in same way as in the BeadArray Controls Reporter software from Illumina. In addition, it provides average red and green raw and normalized signals, time of scanning, proportion of probes passing, overall sample pass/fail status, and the failure codes for control metrics that did not pass. The sample pass status is defined as the passing of all 21 control metrics. The QC summary .xlsx file further highlights failing parameters for easy viewing. The QC summary files contain the following fields: | Field | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Sentrix\_ID | 12-digit BeadChip Barcode associated with the sample. | | Sentrix\_Position | Row and column on the BeadChip ie R01C01 | | Sample\_ID | Optional field that can be indicated using IDAT Sample Sheet | | User Defined Meta Data | Optional field(s) that can be indicated using IDAT Sample Sheet. Any number of fields indicated will appear in this output file. | | restoration |
  • The default threshold is 0.
  • If using the FFPE DNA Restore Kit, the restoration control identifies success of the FFPE restoration chemistry. Change the threshold from 0 to 1 if the FFPE DNA Restore Kit was used.
  • The green channel intensity is higher than Background. Therefore, the metric provided is the Green Channel Intensity/Background.
| |

staining\_green

staining\_red

|
  • Staining controls are used to examine the efficiency of the staining step in both the red and green channels. These controls are independent of the hybridization and extension step.
  • The green channel shows a higher signal for biotin staining when compared to biotin background, whereas the red channel shows higher signal for DNP staining when compared to DNP background.
  • The metric provided for green is the (Biotin High value)/ (Biotin Bkg) and the metric provided for red is (DNP High value)/(DNP Bkg value)
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

extension\_green

extension\_red

|
  • Extension controls test the extension efficiency of A, T, C, and G nucleotides from a hairpin probe, and are therefore sample independent.
  • In the green channel, the lowest intensity for C or G is always greater than the highest intensity for A or T.
  • The metric provided is the (lowest of the C or G intensity)/ (highest of A or T extension) for a single sample.
  • The default threshold is 5. This threshold can be increased on some scanners.
| |

hybridization\_high\_medium

hybridization\_medium\_low

|
  • Hybridization controls test the overall performance of the Infinium Assay using synthetic targets instead of amplified DNA. These synthetic targets complement the sequence on the array, allowing the probe to extend on the synthetic target as a template. Synthetic targets are present in the Hybridization Buffer at 3 levels, monitoring the response from high-concentration (5 pM), medium concentration (1 pM), and low concentration (0.2 pM) targets. All bead type IDs result in signals with various intensities, corresponding to the concentrations of the initial synthetic targets.
  • The value for high concentration is always higher than medium and the value for medium concentration is always higher than low.
  • The metric provided is the value of high/medium and the value of medium/low.
  • The default thresholds are 1. Do not change the default threshold.
| |

target\_removal1

target\_removal2

|
  • Target removal controls test the efficiency of the stripping step after the extension reaction. In contrast to allele-specific extension, the control oligos are extended using the probe sequence as a template. This process generates labeled targets. The probe sequences are designed such that extension from the probe does not occur. All target removal controls result in low signal compared to the hybridization controls, indicating that the targets were removed efficiently after extension. Target removal controls are present in the Hybridization Buffer.
  • The Background for the same sample is close to or larger than either control.
  • The metric provided is Background/Control Intensity.
  • The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion1\_green

bisulfite\_conversion1\_background\_green

bisulfite\_conversion1\_red

bisulfite\_conversion1\_background\_red

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium I probe design and allele-specific single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "C" (Converted) probes matches the converted sequence and get extended. If the sample has unconverted DNA, the "U" (Unconverted) probes get extended. There are no underlying C bases in the primer landing sites, except for the query site itself.

  • The calculation is done in both the green and red channels separately to provide 2 unique sets of values:

    • Green Channel

      • Lowest value of C1 or C2 / Highest value of U1 or U2. The default threshold is 1. This value can be increased for some scanners.
      • Background/(U1, or U2). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.

    • Red Channel

      • Lowest value of C3, 4, or 5 / Highest value of U3, 4, or 5. The default threshold is 1. This value can be increased for some scanners.
      • Background /(Highest value of U4, U5, or U6). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

bisulfite\_conversion2

bisulfite\_conversion2\_background

|
  • These controls assess the efficiency of bisulfite conversion of the genomic DNA. The Infinium Methylation probes query a \[C/T] polymorphism created by bisulfite conversion of non-CpG cytosines in the genome.
  • These controls use Infinium II probe design and single base extension to monitor efficiency of bisulfite conversion. If the bisulfite conversion reaction was successful, the "A" base gets incorporated and the probe has intensity in the red channel. If the sample has unconverted DNA, the "G" base gets incorporated across the unconverted cytosine, and the probe has elevated signal in the green channel.
  • The calculation is done using both channels for 1 set of numbers returned.

  • The following metrics are provided:

    • (Lowest of red C 1, 2, 3, or 4) / (Highest of green C 1, 2, 3, or 4). The default threshold is 1. This value can be increased for some scanners.
    • Background/(Highest C1, C2, C3, or C4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

specificity1\_green

specificity1\_red

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor allele-specific extension for Infinium I probes. The methylation status of a particular cytosine is carried out following bisulfite treatment of DNA by using query probes for unmethylated and methylated state of each CpG locus. In assay oligo design, the A/T match corresponds to the unmethylated status of the interrogated C, and G/C match corresponds to the methylated status of C. G/T mismatch controls check for nonspecific detection of methylation signal over unmethylated background. PM controls correspond to A/T perfect match and give high signal. MM controls correspond to G/T mismatch and give low signal.
  • The metrics provided are the ratio of the lowest PM/highest MM in each channel.
  • The default threshold is 1. Do not change the default threshold.
| |

specificity2

specificity2\_background

|
  • Specificity controls are designed to monitor potential nonspecific primer extension for Infinium I and Infinium II assay probes. Specificity controls are designed against nonpolymorphic T sites.
  • These controls are designed to monitor extension specificity for Infinium II probes and check for potential nonspecific detection of methylation signal over unmethylated background. Specificity II probes incorporate the "A" base across the nonpolymorphic T and have intensity in the Red channel. If there was nonspecific incorporation of the "G" base, the probe has elevated signal in the Green channel.

  • The following metrics are provided:

    • (Lowest intensity of S1, S2, or S3 red) / (Highest intensity of S1, S2, or S3 green). The default threshold is 1. Do not change the default threshold.
    • Background/(Highest intensity S1, S2, S3, or S4 green). The default threshold is 1. Do not change the default threshold; however, the offset correction can be changed.
| |

nonpolymorphic\_green

nonpolymorphic\_red

|
  • Nonpolymorphic controls test the overall performance of the assay, from amplification to detection, by querying a particular base in a nonpolymorphic region of the genome. They let you compare assay performance across different samples. One nonpolymorphic control has been designed for each of the 4 nucleotides (A, T, C, and G).
  • In the green channel, the lowest intensity of C or G is always greater than the highest intensity of A or T.
  • The metric provided is the (lowest intensity for C or G) /(highest intensity for A or T) for a single sample.
  • The default threshold is 5. This value can be increased for some scanners.
| |

avg\_green\_raw

avg\_red\_raw

|
  • Average green and red raw signal for the given sample.
| |

avg\_green\_norm

avg\_red\_norm

|
  • Average green and red signal after dye bias correction and noob normalization for the given sample.
| | ScanTime |
  • The date (MM/DD/YY) and time (HH:MM) that the sample was scanned by the iScan system.
| | NProbes |
  • Number of probes on the BeadChip, including SNP and CG probes
| | NPassDetection |
  • Number of probes on the BeadChip that passed detection p-value at the threshold defined.
| | prop\_probes\_passing |
  • The proportion of probes passing defined as the number of probes passing detection p-value divided by the total number of probes on the BeadChip.
| | passQC |
  • 1 = sample passed all QC metrics for the thresholds defined
  • 0 = sample did not pass all QC metrics for the thresholds defined
| | failCodes |
  • The list of parameters that failed QC for the thresholds defined.
| The control metrics in the QC summary files are calculated as following. The default value for background correction offset (x) of 3,000 can be modified and applies to all background calculations indicated with (bkg + x). Note that the table uses default thresholds for EPIC arrays as example, the default thresholds changes with the methylation arrays. See section [Threshold Adjustment](/dragen-array-v1.1/product-guides/dragen-array-cloud-analysis#threshold-adjustment) for additional details.
ControlCalculationAdditional Information
Restoration Green > bkg(Green/(bkg+x))> 0
  • If using the FFPE Restore kit, change the default threshold from 0 to 1.
  • bkg = Extension Green highest A or T intensity

Staining Green

Biotin High > Biotin Bkg

(High/Biotin Bkg) > 5

Staining Red

DNP High > DNP Bkg

(High/DNP Bkg) > 5
Extension Green Lowest CG/Highest AT(C or G/A or T) > 5Green channel—Lowest C or G intensity is used; highest A or T intensity is used.

Extension Red

Lowest AT/Highest CG

(A or T/C or G) > 5Red channel—Lowest A or T intensity is used; highest C or G intensity is used.
Hybridization Green High > Medium > Low(High/Med) > 1
(Med/Low) > 1
Target Removal Green ctrl 1 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity
Target Removal Green ctrl 2 ≤ bkg((bkg + x)/ctrl) > 1bkg = Extension Green highest A or T intensity

Bisulfite Conversion I Green

C1, 2 > U1, 2

(C/U) > 1
  • Lowest C intensity is used. Highest U intensity is used.

Bisulfite Conversion I Green

U ≤ bkg

((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Green channel—bkg = Extension Green highest AT
Bisulfite Conversion I Red C3, 4, 5 > U3, 4, 5(C/U) >1
  • Lowest C intensity is used. Highest U intensity is used.
Bisulfite Conversion I Red U ≤ bkg((bkg + x)/U) > 1
  • For MSA arrays, the default is 0.5
  • Highest U intensity is used.
  • Red Channel—bkg = Extension Red highest CG
Bisulfite Conversion II C Red > C Green(C Red/ C Green) > 1
  • For MSA arrays, the default is 0.5
  • Lowest C Red intensity is used. Highest C Green intensity is used.
Bisulfite Conversion II C green ≤ bkg((bkg + x)/C Green) > 1
  • For MSA arrays, the default is 0.5
  • Highest C Green intensity is used.
  • Green channel—bkg = Extension Green highest AT
Specificity I Green PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used
Specificity I Red PM > MM(PM/MM) > 1
  • Lowest PM intensity is used. Highest MM intensity is used

Specificity II

S Red > S Green

(S Red/ S Green) > 1
  • Lowest S Red intensity is used. Highest S Green intensity is used.

Specificity II

S Green ≤ bkg

((bkg + x)/ S green) > 1
  • bkg = Extension Green highest A or T intensity
  • Highest S Green intensity is used.
Nonpolymorphic Green Lowest CG/ Highest AT(C or G/ A or T) > 5
  • Lowest C or G intensity is used; highest A or T intensity is used
  • For MSA arrays, the default threshold is 2.5
Nonpolymorphic Red Lowest AT/ Highest CG(A or T/ C or G) >5
  • Lowest A or T intensity is used; highest C or G intensity is used
  • For MSA arrays, the default threshold is 3
## Methylation Sample QC Summary Plots The software produces methylation sample QC summary plots (sample\_qc\_summary.pdf) per analysis batch which provides visual depictions of two QC summary plots for quick visual review. The file contains the following control plots: | Control Plot | Description | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Proportion of Probes Passing Threshold | Histogram of the proportion of probes passing the p-value detection threshold. Samples passing QC are shown in one color, and samples failing QC are shown in another color. | | Principal Component Analysis (PCA) | Uses beta values for all analytical probes to compare samples. Principal component analysis (PCA) is applied to the beta values to reduce the dimensionality of the data to two “principal components” that reflect the most variation across samples. If more than 100 samples are used in the analysis, a random subset of 10,000 probes are used for the PCA analysis to reduce computational burden. PCA control plot assigns unique colors to each sample group defined by the IDAT Sample Sheet. If no groups were assigned, all samples will appear the same color. Sample groups may cluster together and can be used to explain some of the variation. Coordinates used to plot each sample in the PCA control plot are provided in the pcs.tsv.gz output file (see below). | ## Methylation Principal Component Summary The software produces a methylation principal component summary file (pcs.tsv.gz) per analysis batch which provides principal component data for each sample within the batch. This can be used to identify the specific samples associated with points on the PCA control plot within the Methylation Sample QC Control Plots output file. The files contain the following fields: | Field | Description | | --------------------- | -------------------------------------------------------------------------------------------------------------------- | | blank | BeadChip Barcode and Position ie 123456789101\_R01C01 | | principal component 1 | The variable of the first axis for the Principal Component Analysis | | principal component 2 | The variable of the second axis for the Principal Component Analysis | | Sample\_Group | Sample group defined by the user in the IDAT Sample Sheet. If no sample group was defined, all samples will show NA. | ## Methylation Manifest Files The software produces two methylation manifest files 1. Manifest in Sesame format (probes.csv) 2. Additional information for control probes (controls.csv) The probes.csv file has the following columns: | Field | Description | | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | | U | This is corresponds to the AddressA\_ID column in the standard Illumina manifest format. | | M | This corresponds to the AddressB\_ID column in the standard Illumina manifest format. | | col | This is the color channel for Infinium I probes (R/G). For Infinium I probes, this column will be NA. | The controls.csv file has the following columns: | Field | Description | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Address | The address of the probe | | Type | The control probe type | | Color\_Channel | A color used to denote certain control probes in legacy software | | Name | A human readable identifier for certain control probes | | Probe\_ID | This is a unique identifier for each probe. It corresponds to the IlmnID column in the standard Illumina manifest format or ctl\_\[AddressA\_ID] for control probes. | ## Methylation Warning/Error Messages and Logs The following scenarios result in a warning or error message: * Missing IDATs or manifest * Incorrect sample sheet formatting * Duplicate BeadChip Barcode and Position within the sample sheet * Missing control or assay probes * Missing required columns in the manifest * Unable to compute certain metrics Examples of such notifications can include the following: | **Log** | **Error** | **Type** | **Cause** | | ----------------------------------- | ---------------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------- | | write\_samplesheet.log | No IDATs found | Error | No IDATs provided for analysis | | format\_samplesheet.log | No samples in sample sheet | Error | No samples in user’s sample sheet input | | format\_samplesheet.log | Sample sheet not correctly formatted | Error | Sample sheet is not in CSV format or header lines do not start with “<” | | format\_samplesheet.log | beadChipName and sampleSectionName columns are required for the sample sheet. | Error | Sample sheet does not contain required columns: beadChipName and sampleSectionName. | | format\_samplesheet.log | Warning: \ samples have duplicate Sample\_ID | Warning | X lines in the sample sheet have duplicate \\_\. Duplicates are dropped from analysis. | | convert\_manifest\_ilmn\_sesame.log | Missing control probes in manifest | Error | Missing “\[Controls]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Probe section not found | Error | Missing “\[Assay]” line in CSV manifest | | convert\_manifest\_ilmn\_sesame.log | Missing required columns: IlmnID, AddressA\_ID, AddressB\_ID, Color\_Channel | Error | Missing one of required columns in Assay section of manifest | | convert\_manifest\_ilmn\_sesame.log | Controls not formatted correctly. Must have 4 columns (Address,Type,Color\_Channel,Name) | Error | Missing one of required columns in Control section of manifest | | run\_sesame\_gs.log | Missing sample: \ | Error | Missing idats for a particular sample | | run\_sesame\_gs.log | No scan time available | Warning | No scan time in idat | | run\_sesame\_gs.log | Prep failed | Error | Dye bias correction or noob failure for sample | | run\_sesame\_gs.log |

Warning: missing control probe types

\

| Warning | Missing control probe types to compute a BACR metric. Metric will be set to NA. | | run\_sesame\_gs.log |

Warning: missing control probe names

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| Warning | Missing control probes to compute a BACR metric. Metric will be set to NA. | | qc.log | No features, skipping PCA plot | Warning | No common betas found in all samples. This may occur if a sample has no signal intensity in the IDAT files. | [^1]: If using the FFPE Restore kit, change the default threshold from 0 to 1. [^2]: For MSA arrays, the default is 0.5. [^3]: For MSA arrays, the default is 2.5. [^4]: For MSA arrays, the default is 3. # Support and Additional Resources ## Technical Support For support, questions, and feedback on DRAGEN Array, please contact Illumina Tech Support at . ## Additional Resources | Resource | Description | | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | [DRAGEN Array Webpage](https://www.illumina.com/products/by-type/informatics-products/dragen-array-secondary-analysis.html) | Product features and benefits and allows product ordering. | | [DRAGEN Array Support Site](https://support.illumina.com/array/array_software/dragen-array-secondary-analysis.html) | Support site for DRAGEN Array which includes installers and product documentation. | | [DRAGEN Array Methylation QC analysis](https://developer.illumina.com/news-updates/dragen-array-1-0-now-supporting-methylation-qc-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 Methylation QC. | | [DRAGEN Array PGx Analysis](https://developer.illumina.com/news-updates/introducing-dragen-array-1-0-for-infinium-array-based-pharmacogenomics-analysis) | Illumina Software Resources article with technical details on DRAGEN Array v1.0 PGx analysis. | | [Infinium Lab Setup and Best Practices](http://support-docs.illumina.com/ARR/infinium-labsetup.htm) | Lab setup and maintenance information for Infinium assays. | | [Infinium Assay Consumables & Equipment List](http://support-docs.illumina.com/ARR/infinium-consumables.htm) | List of consumables and equipment used in Infinium assays. | | [iScan System Product Documentation](http://support-docs.illumina.com/ARR/iscan.htm) | Instructions for operating and maintaining the iScan System. | | [Polygenic Risk Score – Predict](https://support-docs.illumina.com/ARR/PRS/Content/ARR/PRS/PRS.htm) | Instructions for using the Polygenic Risk Score – Predict Module. | | [Illumina Connected Analytics](https://support.illumina.com/sequencing/sequencing_software/illumina-connected-analytics.html) | Instructions for using the hosted environment Illumina Connected Analytics. | | [BaseSpace Sequence Hub](https://help.basespace.illumina.com/) | Instructions for using the hosted environment BaseSpace Sequence Hub. | ### # Frequently Asked Questions 1. **Is DRAGEN Array analysis a local (on-premises) or cloud solution?**\ DRAGEN Array analysis is available locally (on-premises) and cloud. DRAGEN Array Local Analysis utilizes a command-line interface for power users to have granular control and flexibility to support large scale microarray genomic studies. Deployed on Windows or Linux operating systems, the local package is CPU-based and does not require a specialized server or hardware. DRAGEN Array Cloud Analysis utilizes the user-friendly, graphical interface of BaseSpace Sequence Hub to simplify analysis setup and kickoff. 2. **Which Infinium arrays is DRAGEN Array compatible with?**\ Refer to the Product and Analysis Compatibility table in the [Applications](/dragen-array-v1.1/overview/our-features) section. 3. **How many samples are needed per analysis?**\ **Genotyping:** As few as one sample can be used for genotyping. Multiple analysis batches can be kicked off and run in parallel. **Pharmacogenomics:** A minimum of 24 samples is required for PGx CNV calling with 22 passing QC. Passing QC is defined as Log R Dev < 0.2. 96 samples are recommended for the most accurate CNV results. Multiple analysis batches can be kicked off and run in parallel. 4. **Which PGx CNVs and star alleles are available?**\ Please refer to the DRAGEN Array [release notes](/dragen-array-v1.1/reference/release-notes). 5. **Where can I find demo data?**\ Demo data is available in BaseSpace under the “Demo Data” section. All array data starts with “iScan:” and includes the name of the type of analysis. Supported types of analysis can be found in the [Applications](/dragen-array-v1.1/overview/our-features) section. # Release Notes The following versions of DRAGEN Array have been released: * [DRAGEN Array v1.1.0 Release Notes](/dragen-array-v1.1/reference/release-notes/dragen-array-v1.1.0-release-notes) * [DRAGEN Array v1.0.0 Release Notes](/dragen-array-v1.1/reference/release-notes/dragen-array-v1.0.0-release-notes) * [DRAGEN Array Genotyping Cloud v1.0.0 Release Notes](/dragen-array-v1.1/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-genotype-release-notes) * [DRAGEN Array Methylation QC Cloud v1.0.0 Release Notes](/dragen-array-v1.1/reference/release-notes/dragen-array-v1.0.0-release-notes/dragen-array-v1.0.0-cloud-methylqc-release-notes) # DRAGEN Array v1.1.0 Release Notes ## **RELEASE DATE** September 2024 ## **RELEASE HIGHLIGHTS** * New EX PGx beadchips enabled for PGx analysis * Increased coverage of high priority PGx genes * Custom optimized .egt files accepted in PGx analysis * Up-to-date database reflecting latest versions of public PGx resources * DPWG guidelines now available for metabolizer status calling on cloud analysis ## **NEW FEATURES IN DETAIL** * DRAGEN Array supports multiple PGx products * Two new EX PGx beadchips enabled through genotyping, PGx CNV calling, and star allele annotation * Infinium Global Screening Array with Enhanced PGx-48 v4.0 Kit * Infinium Global Clinical Research Array with Enhanced PGx-24 v1.0 Kit * In total 3 PGx products supported | Product | Manifest Name | Genome Build | Product Files Link | | ---------- | ----------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------- | | GDA-ePGx | GDA\_PGx-8v1-0\_20042614\_G2 | 38 | [GDA-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-diversity-pgx/product-files.html) | | GSAv4-ePGx | GSA-PGx-48v4-0\_20079540\_E2 | 38 | [GSAv4-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-screening-array-v4-pgx.html) | | GCRA-ePGx | GCRA-PGx-24v1-0\_20084467\_C2 | 38 | [GCRA-ePGx product files](https://support.illumina.com/array/array_kits/infinium-global-clinical-research-array-pgx.html) | * Increased coverage of high priority PGx genes * Star allele annotation now covers CYP2E1, CYP1A2, ABCG2, CYP2C8, HMGCR, UGT1A4, UGT2B15, F13A1, and HLA-B\*15:02 * CNV calling now covers SULT1A1 * Extended bi-allelic PGx variants from source databases to multi-allelic variants based on the designs in the supported PGx products. * See [PGx Star Allele Coverage](/dragen-array-v1.1/reference/pgx-star-allele-coverage) and [PGx CNV Coverage](/dragen-array-v1.1/reference/pgx-cnv-coverage) for the full coverage lists. * Allows flexibility for GTCs generated with a custom cluster file (.egt) to be used with the commercial CN model file (.dat). This alleviates the burden to retrain the CN model file. * The cluster file is a required input for the genotype call command in DRAGEN Array. The CN (Copy Number) model file is a required input to the copy-number call command to enable accurate copy number calling for pharmacogenomics. Custom cluster files and CN model files may be required for optimal genotyping and PGx performance. See section Optimizing cluster files and copy number models for additional details. * Database revision reflecting [PGx Allele Definitions and PGx Guidelines](/dragen-array-v1.1/reference/pgx-allele-definitions-and-pgx-guidelines) updates. * Standardization of star allele JSON output file * Renamed databaseSources to phenotypeDatabaseSources and starAlleleDatabaseSources * Renamed Phenotype to PhenotypeDatabaseAnnotation * Combined missingVariants and allMissingVariants to missingVariantSites * JSONized supportingVariants and missingVariants at the gene and candidate solution allele levels * Removed redundant info in the Alleles fields * Updated VCF tabix indexing, improving performance and disk usage for SNV VCF. ## **KNOWN ISSUES** * Some simple variants have REF and ALT delimited by \_ instead of > in the star\_alleles.csv and metabolizer status JSON files (e.g., "ryr1.38577931a\_c" instead of "ryr1.38577931a>c") * Some multi-nucleotide variant (MNV) designs reverse compliment the "Allele1/2 Top" fields in the Final Report * Occasional star-allele solution score discorcordance between Linux and Windows OS with concordant solution ranking. * Rare intermittent memory issues during star allele calling. Example error message: `The model has been changed since the solution was last computed.`. To workaround the issue, user should restart star allele calling or run it on a machine with more memory. ## **KNOWN LIMITATIONS** * Star allele calling does not support novel alleles but those defined in the PharmVar and PharmGKB databases. * CYP2D6 non-\*36 star alleles with exon 9 conversion, such as \*83, are reported as \*36 with \*83 as an underlying allele. * Genotyping only supports diploid organisms. Polyploid genotyping is currently not supported. * DRAGEN Array were only validated and intended to be used for commercial PGx beadchips with specified manifests (see table above). PGx star allele annotation is not backwards compatable with v1.0 manifest version, e.g., GDA\_PGx-8v1-0\_20042614\_E2 is supported in DRAGEN Array v1.0, GDA\_PGx-8v1-0\_20042614\_G2 is supported in DRAGEN Array v1.1. * Command line options `unsquash-duplicates` and `filter-loci` for `gtc-to-vcf` conversion should not be used when star allele calling is desired. In addition, VCFs must be gzipped and tabix indexed (the default for `gtc-to-vcf`) to be used in star allele calling. --- [Next Page](/llms-full.txt/1)