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03-variant-calling.md

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Step 3: Variant Calling (BAM to VCF)

What This Does

Identifies all positions where the sample's DNA differs from the reference genome: SNPs (single nucleotide changes) and small indels (insertions/deletions <50bp).

Why

The VCF file is the foundation for ALL downstream analyses: ClinVar screening, pharmacogenomics, PRS, ROH, etc.

Tool

  • DeepVariant v1.6.0 — Google's deep learning variant caller (state-of-the-art accuracy)

Docker Image

google/deepvariant:1.6.0

Prerequisites

  • Sorted, indexed BAM file
  • GRCh38 reference genome (FASTA + FAI)

Command

SAMPLE=your_sample
GENOME_DIR=/path/to/your/data

docker run --rm \
  --cpus 16 --memory 32g \
  -v ${GENOME_DIR}:/genome \
  google/deepvariant:1.6.0 \
  /opt/deepvariant/bin/run_deepvariant \
    --model_type=WGS \
    --ref=/genome/reference/Homo_sapiens_assembly38.fasta \
    --reads=/genome/${SAMPLE}/aligned/${SAMPLE}_sorted.bam \
    --output_vcf=/genome/${SAMPLE}/vcf/${SAMPLE}.vcf.gz \
    --num_shards=16

# For WES data, use MODEL_TYPE=WES:
# MODEL_TYPE=WES ./scripts/03-deepvariant.sh your_sample

# Output: ~93MB VCF with ~5.5M total variants (~4.6M PASS)

Resource Requirements

  • CPU: 16+ cores (scales well with --num_shards)
  • RAM: 32GB recommended
  • GPU: Optional (significantly faster with NVIDIA GPU)
  • Time: 4-8 hours on CPU, 1-2 hours with GPU

Output Interpretation

  • PASS variants: high-confidence calls (~4.6M per 30X WGS sample)
  • RefCall: site looks like reference (not a variant)
  • LowQual: low confidence
  • GQ (Genotype Quality): higher = more confident
  • DP (Read Depth): typical 25-35x for a 30X WGS sample

Notes

  • DeepVariant is the gold standard for SNPs/indels but does NOT detect:
    • Structural variants >50bp (use Manta, step 4)
    • Repeat expansions (use ExpansionHunter, step 9)
    • Copy number variants (use CNVnator or GATK gCNV)
  • bcftools can also call variants but is significantly less accurate than DeepVariant