Joint Genotyping Pipeline

The Azure Databricks joint genotyping pipeline is a GATK best practices compliant pipeline for joint genotyping using GenotypeGVCFs.

Beta

The Azure Databricks joint genotyping pipeline requires Databricks Runtime HLS, which is in Beta. Interfaces and pricing are subject to change before general availability.

Walkthrough

The pipeline typically consists of the following steps:

  1. Ingest variants into Delta Lake.
  2. Joint-call the cohort with GenotypeGVCFs.

During variant ingest, single-sample gVCFs are processed in batches and the rows are stored in Delta Lake to provide fault tolerance, fast querying, and incremental joint genotyping. In the joint genotyping step, the gVCF rows are ingested from Delta Lake, split into bins, and distributed to partitions. For each variant site, the relevant gVCF rows per sample are identified and used for regenotyping.

Setup

The pipeline is run as an Azure Databricks job. Most likely an Azure Databricks solutions architect will work with you to set up the initial job. The necessary details are:

  • The cluster configuration should use Databricks Runtime HLS.
  • The task should be the joint genotyping pipeline notebook found at the bottom of this page.
  • For best performance, use the storage-optimized VMs. We recommend Standard_L32s_v2.
  • To reduce costs, enable autoscaling with a minimum of 1 worker and a maximum of 10-50 depending on latency requirements.

Parameters

The pipeline accepts parameters that control its behavior. The most important and commonly changed parameters are documented here. To view all available parameters and their usage information, run the first cell of the pipeline notebook. New parameters are added regularly. Parameters can be set for all runs or per-run.

Parameter Default Description
manifest n/a The path of the manifest file describing the input.
output n/a The path where pipeline output is written.
replayMode skip

One of:

  • skip: stages are skipped if output already exists.
  • overwrite: existing output is deleted.
exportVCF false If true, the pipeline writes results in VCF as well as Delta Lake.
targetedRegions n/a Path to files containing regions to call. If omitted, calls all regions.
genotypeGivenAlleles false If true, regenotypes variant sites based on the alleles in the input gVCFs.
emitAllSites false If true, retain low quality sites in the output.
gvcfDeltaOutput n/a If specified, gVCFs are ingested to a Delta Lake table before genotyping. You should specify this parameter only if you expect to joint call the same gVCFs many times.
performValidation false If true, the system verifies that each record contains the necessary information for joint genotyping. In particular, it checks that the correct number of genotype probabilities are present.
validationStringency STRICT

How to handle malformed records, both during loading and validation.

  • STRICT: fail the job
  • LENIENT: log a warning and drop the record
  • SILENT: drop the record without a warning

Tip

To keep rare variants, set genotypeGivenAlleles and emitAllSites to true. This is equivalent to changing the GATK settings genotyping_mode from DISCOVERY (choose the most probable alleles) to GENOTYPE_GIVEN_ALLELES (use the alleles present in the input gVCFs), and output_mode from EMIT_VARIANTS_ONLY (produces calls only at variant sites) to EMIT_ALL_SITES (produces calls at any callable site regardless of confidence).

Output

The regenotyped variants are all written out to Delta Lake tables inside the provided output directory. In addition, if you configured the pipeline to export VCFs, they’ll appear under the output directory as well.

output
|---genotypes
    |---Delta files
|---genotypes.vcf
    |---VCF files

Reference genomes

You must configure the reference genome using environment variables. To use GRCh37, set the environment variable:

refGenomeId=grch37

To use GRCh38, change grch37 to grch38.

Manifest format

The manifest is a file describing where to find the input GVCF files, with each path on a new row.

Tip

Each row may be an absolute path or a path relative to the manifest. You can include globs (*) to match many files.

Troubleshooting

Job fails with an ArrayIndexOutOfBoundsException
This error usually indicates that an input record has an incorrect number of genotype probabilities. Try setting the performValidation option to true and the validationStringency option to LENIENT or SILENT.

Additional usage info

The joint genotyping pipeline shares many operational details with the other Azure Databricks pipelines. For more detailed usage information, such as output format structure, tips for running programmatically, and steps for setting up custom reference genomes, see DNASeq Pipeline.

Joint Genotyping Pipeline