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bio-variant-calling-joint-calling Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.
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name bio-variant-calling-joint-calling description Joint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples. 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.
Joint Calling
"Joint genotype my cohort samples" → Combine per-sample gVCFs into a single cohort callset with consistent genotyping across all sites, enabling VQSR and population-level analysis.
CLI: gatk HaplotypeCaller -ERC GVCF → gatk GenomicsDBImport → gatk GenotypeGVCFs
Why Joint Calling?
Improved sensitivity - Leverage information across samplesConsistent genotyping - Same sites called across all samplesVQSR eligible - Requires cohort for machine learning filtering
Population analysis - Allele frequencies across cohort
Workflow Overview Sample BAMs
│
├── HaplotypeCaller (per-sample, -ERC GVCF)
│ └── sample1.g.vcf.gz, sample2.g.vcf.gz, ...
│
├── CombineGVCFs or GenomicsDBImport
│ └── Combine into cohort database
│
├── GenotypeGVCFs
│ └── Joint genotyping
│
└── VQSR or Hard Filtering
└── Final VCF
Step 1: Per-Sample gVCF Generation
gatk HaplotypeCaller \
-R reference.fa \
-I sample1.bam \
-O sample1.g.vcf.gz \
-ERC GVCF
gatk HaplotypeCaller \
-R reference.fa \
-I sample1.bam \
-O sample1.g.vcf.gz \
-ERC GVCF \
-L intervals.bed
Batch Processing
for bam in *.bam; do
sample=$(basename $bam .bam)
gatk HaplotypeCaller \
-R reference.fa \
-I $bam \
-O ${sample} .g.vcf.gz \
-ERC GVCF &
done
wait
Step 2a: CombineGVCFs (Small Cohorts) gatk CombineGVCFs \
-R reference.fa \
-V sample1.g.vcf.gz \
-V sample2.g.vcf.gz \
-V sample3.g.vcf.gz \
-O cohort.g.vcf.gz
From Sample Map
ls *.g.vcf.gz | while read f; do
echo -e "$(basename $f .g.vcf.gz) \t$f "
done > sample_map.txt
gatk CombineGVCFs \
-R reference.fa \
$(cat sample_map.txt | cut -f2 | sed 's/^/-V /' ) \
-O cohort.g.vcf.gz
Step 2b: GenomicsDBImport (Large Cohorts) For >100 samples, use GenomicsDB:
ls *.g.vcf.gz | while read f; do
echo -e "$(basename $f .g.vcf.gz) \t$f "
done > sample_map.txt
gatk GenomicsDBImport \
--sample-name-map sample_map.txt \
--genomicsdb-workspace-path genomicsdb_chr1 \
-L chr1 \
--reader-threads 4
for chr in {1..22} X Y; do
gatk GenomicsDBImport \
--sample-name-map sample_map.txt \
--genomicsdb-workspace-path genomicsdb_chr${chr} \
-L chr${chr} &
done
wait
Update GenomicsDB with New Samples gatk GenomicsDBImport \
--genomicsdb-update-workspace-path genomicsdb_chr1 \
--sample-name-map new_samples.txt \
-L chr1
Step 3: GenotypeGVCFs
From Combined gVCF gatk GenotypeGVCFs \
-R reference.fa \
-V cohort.g.vcf.gz \
-O cohort.vcf.gz
From GenomicsDB gatk GenotypeGVCFs \
-R reference.fa \
-V gendb://genomicsdb_chr1 \
-O chr1.vcf.gz
for chr in {1..22} X Y; do
gatk GenotypeGVCFs \
-R reference.fa \
-V gendb://genomicsdb_chr${chr} \
-O chr${chr} .vcf.gz &
done
wait
bcftools concat chr{1..22}.vcf.gz chrX.vcf.gz chrY.vcf.gz \
-Oz -o cohort.vcf.gz
With Allele-Specific Annotations gatk GenotypeGVCFs \
-R reference.fa \
-V gendb://genomicsdb \
-O cohort.vcf.gz \
-G StandardAnnotation \
-G AS_StandardAnnotation
Step 4: Filtering
VQSR (Recommended for >30 Samples)
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 snps.recal \
--tranches-file snps.tranches
gatk ApplyVQSR \
-R reference.fa \
-V cohort.vcf.gz \
--recal-file snps.recal \
--tranches-file snps.tranches \
-mode SNP \
--truth-sensitivity-filter-level 99.5 \
-O cohort.snps.vcf.gz
gatk VariantRecalibrator \
-R reference.fa \
-V cohort.snps.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 \
-O indels.recal \
--tranches-file indels.tranches
gatk ApplyVQSR \
-R reference.fa \
-V cohort.snps.vcf.gz \
--recal-file indels.recal \
--tranches-file indels.tranches \
-mode INDEL \
--truth-sensitivity-filter-level 99.0 \
-O cohort.filtered.vcf.gz
Hard Filtering (Small Cohorts)
gatk VariantFiltration \
-R reference.fa \
-V cohort.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" \
-O cohort.filtered.vcf.gz
Complete Pipeline Script Goal: Run the full joint calling workflow from BAMs to filtered cohort VCF.
Approach: Generate per-sample gVCFs, import into GenomicsDB, joint genotype, then index and compute statistics.
#!/bin/bash
set -euo pipefail
REFERENCE=$1
OUTPUT_DIR=$2
THREADS=16
mkdir -p $OUTPUT_DIR /{gvcfs,genomicsdb,vcfs}
echo "=== Step 1: Generate gVCFs ==="
for bam in data/*.bam; do
sample=$(basename $bam .bam)
gatk HaplotypeCaller \
-R $REFERENCE \
-I $bam \
-O $OUTPUT_DIR /gvcfs/${sample} .g.vcf.gz \
-ERC GVCF &
while [ $(jobs -r | wc -l) -ge $THREADS ]; do sleep 1; done
done
wait
echo "=== Step 2: Create sample map ==="
ls $OUTPUT_DIR /gvcfs/*.g.vcf.gz | while read f; do
echo -e "$(basename $f .g.vcf.gz) \t$(realpath $f) "
done > $OUTPUT_DIR /sample_map.txt
echo "=== Step 3: GenomicsDBImport ==="
gatk GenomicsDBImport \
--sample-name-map $OUTPUT_DIR /sample_map.txt \
--genomicsdb-workspace-path $OUTPUT_DIR /genomicsdb \
-L intervals.bed \
--reader-threads 4
echo "=== Step 4: Joint genotyping ==="
gatk GenotypeGVCFs \
-R $REFERENCE \
-V gendb://$OUTPUT_DIR /genomicsdb \
-O $OUTPUT_DIR /vcfs/cohort.vcf.gz
echo "=== Step 5: Index ==="
bcftools index -t $OUTPUT_DIR /vcfs/cohort.vcf.gz
echo "=== Statistics ==="
bcftools stats $OUTPUT_DIR /vcfs/cohort.vcf.gz > $OUTPUT_DIR /vcfs/cohort_stats.txt
echo "=== Complete ==="
echo "Joint VCF: $OUTPUT_DIR /vcfs/cohort.vcf.gz"
Tips
Memory for Large Cohorts
gatk --java-options "-Xmx64g" GenotypeGVCFs ...
gatk GenomicsDBImport --batch-size 50 ...
Incremental Updates
gatk GenomicsDBImport \
--genomicsdb-update-workspace-path existing_db \
--sample-name-map new_samples.txt
Related Skills
variant-calling/gatk-variant-calling - Single-sample calling
variant-calling/filtering-best-practices - VQSR and hard filtering
population-genetics/plink-basics - Population analysis of joint calls
workflows/fastq-to-variants - End-to-end germline pipeline
