Agent skill

gatk-variant-calling

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/variant-interpretation-acmg/bioSkills/gatk-variant-calling

SKILL.md

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 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

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

Basic HaplotypeCaller

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

With Standard Annotations

bash
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)

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

Adjust Calling Confidence

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

GVCF Workflow (Recommended for Cohorts)

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

Step 1: Generate GVCFs per Sample

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

Step 2: Combine GVCFs (GenomicsDBImport)

bash
# 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)

bash
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

bash
# 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)

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

SNP Recalibration

bash
# 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

bash
# 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)

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

Extract SNPs and Indels

bash
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

bash
# Filter SNPs
gatk VariantFiltration \
    -R reference.fa \
    -V snps.vcf.gz \
    -O snps.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 60.0" --filter-name "FS60" \
    --filter-expression "MQ < 40.0" --filter-name "MQ40" \
    --filter-expression "MQRankSum < -12.5" --filter-name "MQRankSum-12.5" \
    --filter-expression "ReadPosRankSum < -8.0" --filter-name "ReadPosRankSum-8" \
    --filter-expression "SOR > 3.0" --filter-name "SOR3"

# Filter Indels
gatk VariantFiltration \
    -R reference.fa \
    -V indels.vcf.gz \
    -O indels.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 200.0" --filter-name "FS200" \
    --filter-expression "ReadPosRankSum < -20.0" --filter-name "ReadPosRankSum-20" \
    --filter-expression "SOR > 10.0" --filter-name "SOR10"

Merge Filtered Variants

bash
gatk MergeVcfs \
    -I snps.filtered.vcf.gz \
    -I indels.filtered.vcf.gz \
    -O cohort.filtered.vcf.gz

Base Quality Score Recalibration (BQSR)

Preprocessing step to correct systematic errors in base quality scores.

Step 1: BaseRecalibrator

bash
gatk BaseRecalibrator \
    -R reference.fa \
    -I sample.bam \
    --known-sites dbsnp.vcf.gz \
    --known-sites known_indels.vcf.gz \
    -O recal_data.table

Step 2: ApplyBQSR

bash
gatk ApplyBQSR \
    -R reference.fa \
    -I sample.bam \
    --bqsr-recal-file recal_data.table \
    -O sample.recal.bam

Parallel Processing

Scatter by Interval

bash
# Split calling across intervals
for interval in chr{1..22} chrX chrY; do
    gatk HaplotypeCaller \
        -R reference.fa \
        -I sample.bam \
        -L $interval \
        -O sample.${interval}.g.vcf.gz \
        -ERC GVCF &
done
wait

# Gather GVCFs
gatk GatherVcfs \
    -I sample.chr1.g.vcf.gz \
    -I sample.chr2.g.vcf.gz \
    ... \
    -O sample.g.vcf.gz

Native Pairwise Parallelism

bash
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample.bam \
    -O sample.vcf.gz \
    --native-pair-hmm-threads 4

CNN Score Variant Filter (Deep Learning)

Alternative to VQSR using convolutional neural network.

Score Variants

bash
gatk CNNScoreVariants \
    -R reference.fa \
    -V cohort.vcf.gz \
    -O cohort.cnn_scored.vcf.gz \
    --tensor-type reference

Filter by CNN Score

bash
gatk FilterVariantTranches \
    -V cohort.cnn_scored.vcf.gz \
    -O cohort.cnn_filtered.vcf.gz \
    --resource hapmap.vcf.gz \
    --resource mills.vcf.gz \
    --info-key CNN_1D \
    --snp-tranche 99.95 \
    --indel-tranche 99.4

Complete Single-Sample Pipeline

bash
#!/bin/bash
SAMPLE=$1
REF=reference.fa
DBSNP=dbsnp.vcf.gz
KNOWN_INDELS=known_indels.vcf.gz

# BQSR
gatk BaseRecalibrator -R $REF -I ${SAMPLE}.bam \
    --known-sites $DBSNP --known-sites $KNOWN_INDELS \
    -O ${SAMPLE}.recal.table

gatk ApplyBQSR -R $REF -I ${SAMPLE}.bam \
    --bqsr-recal-file ${SAMPLE}.recal.table \
    -O ${SAMPLE}.recal.bam

# Call variants
gatk HaplotypeCaller -R $REF -I ${SAMPLE}.recal.bam \
    -O ${SAMPLE}.g.vcf.gz -ERC GVCF

# Single-sample genotyping
gatk GenotypeGVCFs -R $REF -V ${SAMPLE}.g.vcf.gz \
    -O ${SAMPLE}.vcf.gz

# Hard filter
gatk VariantFiltration -R $REF -V ${SAMPLE}.vcf.gz \
    -O ${SAMPLE}.filtered.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "LowQD" \
    --filter-expression "FS > 60.0" --filter-name "HighFS" \
    --filter-expression "MQ < 40.0" --filter-name "LowMQ"

Key Annotations

Annotation Description Good Values
QD Quality by Depth > 2.0
FS Fisher Strand < 60 (SNP), < 200 (Indel)
SOR Strand Odds Ratio < 3 (SNP), < 10 (Indel)
MQ Mapping Quality > 40
MQRankSum MQ Rank Sum Test > -12.5
ReadPosRankSum Read Position Rank Sum > -8.0 (SNP), > -20.0 (Indel)

Resource Files

Resource Use
dbSNP Known variants (prior=2.0)
HapMap Training/truth SNPs (prior=15.0)
Omni Training SNPs (prior=12.0)
1000G SNPs Training SNPs (prior=10.0)
Mills Indels Training/truth indels (prior=12.0)

Related Skills

  • variant-calling - bcftools alternative
  • alignment-files - BAM preprocessing
  • filtering-best-practices - Post-calling filtering
  • variant-normalization - Normalize before annotation
  • vep-snpeff-annotation - Annotate final calls

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