Bio Gatk Variant Calling

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

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)

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)

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)

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

# 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

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

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

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

Parallel Processing

Scatter by Interval

# 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

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

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

Filter by CNN Score

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

#!/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

Resource Files

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