/bio-gatk-variant-calling

name: bio-gatk-variant-calling
description: Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.
tool_type: cli
primary_tool: gatk

Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

GATK Variant Calling

GATK HaplotypeCaller is the gold standard for germline variant calling. This skill covers the GATK Best Practices workflow.

Prerequisites

BAM files should be preprocessed: 1. Mark duplicates 2. Base quality score recalibration (BQSR) - optional but recommended

Single-Sample Calling

**Goal:** Call germline SNPs and indels from a single sample using HaplotypeCaller.

**Approach:** Run local de novo assembly of haplotypes in active regions to detect variants with optional annotation enrichment.

**"Call variants from my BAM file using GATK"** → Perform local haplotype assembly and genotyping on aligned reads using HaplotypeCaller.

Basic HaplotypeCaller

gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz

With Standard Annotations

gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz \
    -A Coverage \
    -A QualByDepth \
    -A FisherStrand \
    -A StrandOddsRatio \
    -A MappingQualityRankSumTest \
    -A ReadPosRankSumTest

Target Intervals (Exome/Panel)

gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -L targets.interval_list \
    -O sample.vcf.gz

Adjust Calling Confidence

gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz \
    --standard-min-confidence-threshold-for-calling 20

GVCF Workflow (Recommended for Cohorts)

**Goal:** Enable joint genotyping across a cohort by generating per-sample genomic VCFs.

**Approach:** Call each sample in GVCF mode (-ERC GVCF), combine into a GenomicsDB or merged GVCF, then jointly genotype.

The GVCF workflow enables joint genotyping across samples for better variant calls.

Step 1: Generate GVCFs per Sample

gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.g.vcf.gz \
    -ERC GVCF

Step 2: Combine GVCFs (GenomicsDBImport)

# Create sample map file
# sample_map.txt:
# sample1    /path/to/sample1.g.vcf.gz
# sample2    /path/to/sample2.g.vcf.gz

gatk GenomicsDBImport \
    --genomicsdb-workspace-path genomicsdb \
    --sample-name-map sample_map.txt \
    -L intervals.interval_list

Alternative: CombineGVCFs (smaller cohorts)

gatk CombineGVCFs \
    -R reference.fa \
    -V sample1.g.vcf.gz \
    -V sample2.g.vcf.gz \
    -V sample3.g.vcf.gz \
    -O cohort.g.vcf.gz

Step 3: Joint Genotyping

# From GenomicsDB
gatk GenotypeGVCFs \
    -R reference.fa \
    -V gendb://genomicsdb \
    -O cohort.vcf.gz

# From combined GVCF
gatk GenotypeGVCFs \
    -R reference.fa \
    -V cohort.g.vcf.gz \
    -O cohort.vcf.gz

Variant Quality Score Recalibration (VQSR)

**Goal:** Apply machine learning-based variant filtering using known truth/training sets.

**Approach:** Build a Gaussian mixture model from annotation values at known sites, then apply a sensitivity threshold to classify variants.

Machine learning-based filtering using known variant sites. Requires many variants (WGS preferred).

SNP Recalibration

# Build SNP model
gatk VariantRecalibrator \
    -R reference.fa \
    -V cohort.vcf.gz \
    --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf.gz \
    --resource:omni,known=false,training=true,truth=false,prior=12.0 omni.vcf.gz \
    --resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G.vcf.gz \
    --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.vcf.gz \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP \
    -O snp.recal \
    --tranches-file snp.tranches

# Apply SNP filter
gatk ApplyVQSR \
    -R reference.fa \
    -V cohort.vcf.gz \
    -O cohort.snp_recal.vcf.gz \
    --recal-file snp.recal \
    --tranches-file snp.tranches \
    --truth-sensitivity-filter-level 99.5 \
    -mode SNP

Indel Recalibration

# Build Indel model
gatk VariantRecalibrator \
    -R reference.fa \
    -V cohort.snp_recal.vcf.gz \
    --resource:mills,known=false,training=true,truth=true,prior=12.0 Mills.vcf.gz \
    --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.vcf.gz \
    -an QD -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode INDEL \
    --max-gaussians 4 \
    -O indel.recal \
    --tranches-file indel.tranches

# Apply Indel filter
gatk ApplyVQSR \
    -R reference.fa \
    -V cohort.snp_recal.vcf.gz \
    -O cohort.vqsr.vcf.gz \
    --recal-file indel.recal \
    --tranches-file indel.tranches \
    --truth-sensitivity-filter-level 99.0 \
    -mode INDEL

Hard Filtering (When VQSR Not Suitable)

**Goal:** Apply fixed-threshold filters when the dataset is too small for VQSR.

**Approach:** Separate SNPs and indels, apply GATK-recommended annotation thresholds, then merge results.

For small datasets, exomes, or single samples where VQSR fails.

Extract SNPs and Indels

gatk SelectVariants \
    -R reference.fa \
    -V cohort.vcf.gz \
    --select-type-to-include SNP \
    -O snps.vcf.gz

gatk SelectVariants \
    -R reference.fa \
    -V cohort.vcf.gz \
    --select-type-to-include INDEL \
    -O indels.vcf.gz

Apply Hard Filters

# Fi