Agent skill

bio-gatk-variant-calling

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.

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Install this agent skill to your Project

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

SKILL.md

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

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)

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

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)

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

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)

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

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)

Goal: Correct systematic errors in base quality scores before variant calling.

Approach: Model quality score errors at known variant sites with BaseRecalibrator, then apply corrections with ApplyBQSR.

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

Goal: Reduce wall-clock time for variant calling on large datasets.

Approach: Scatter by chromosome or interval, run HaplotypeCaller in parallel, then gather results.

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)

Goal: Filter variants using a deep learning model as an alternative to VQSR.

Approach: Score variants with CNNScoreVariants using reference context, then filter by tranche sensitivity.

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

Goal: Run the full GATK best practices workflow from BQSR through filtered variants.

Approach: Chain BaseRecalibrator, ApplyBQSR, HaplotypeCaller (GVCF mode), GenotypeGVCFs, and hard filtering.

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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