Joint CallingSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

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# COPYRIGHT NOTICE
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# Copyright (c) 2026 MD BABU MIA, PhD <[email protected]>
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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
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

# Joint Calling

Call variants jointly across multiple samples for improved accuracy and consistent genotyping.

## Why Joint Calling?

- **Improved sensitivity** - Leverage information across samples
- **Consistent genotyping** - Same sites called across all samples
- **VQSR 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

```bash
# Generate gVCF for each sample
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample1.bam \
    -O sample1.g.vcf.gz \
    -ERC GVCF

# With intervals (faster)
gatk HaplotypeCaller \
    -R reference.fa \
    -I sample1.bam \
    -O sample1.g.vcf.gz \
    -ERC GVCF \
    -L intervals.bed
```

### Batch Processing

```bash
# Process all samples
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)

For <100 samples:

```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
```

### From Sample Map

```bash
# Create sample map file
# sample1    /path/to/sample1.g.vcf.gz
# sample2    /path/to/sample2.g.vcf.gz

ls *.g.vcf.gz | while read f; do
    echo -e "$(basename $f .g.vcf.gz)\t$f"
done > sample_map.txt

# Combine with -V for each
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:

```bash
# Create sample map
ls *.g.vcf.gz | while read f; do
    echo -e "$(basename $f .g.vcf.gz)\t$f"
done > sample_map.txt

# Import to GenomicsDB (per chromosome for parallelism)
gatk GenomicsDBImport \
    --sample-name-map sample_map.txt \
    --genomicsdb-workspace-path genomicsdb_chr1 \
    -L chr1 \
    --reader-threads 4

# Or all chromosomes
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

```bash
gatk GenomicsDBImport \
    --genomicsdb-update-workspace-path genomicsdb_chr1 \
    --sample-name-map new_samples.txt \
    -L chr1
```

## Step 3: GenotypeGVCFs

### From Combined gVCF

```bash
gatk GenotypeGVCFs \
    -R reference.fa \
    -V cohort.g.vcf.gz \
    -O cohort.vcf.gz
```

### From GenomicsDB

```bash
gatk GenotypeGVCFs \
    -R reference.fa \
    -V gendb://genomicsdb_chr1 \
    -O chr1.vcf.gz

# All chromosomes
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

# Merge chromosomes
bcftools concat chr{1..22}.vcf.gz chrX.vcf.gz chrY.vcf.gz \
    -Oz -o cohort.vcf.gz
```

### With Allele-Specific Annotations

```bash
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)

```bash
# SNPs
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

# Indels
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)

```bash
# See filtering-best-practices skill
gatk VariantFiltration \
    -R reference.fa \
    -V cohort.vcf.gz \
    --filter-expression "QD < 2.0" --filte

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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Gates applied: no_behavioural_pass.