Personal-Genome-Pipeline

Step 7: Pharmacogenomics (PharmCAT)

What This Does

Pharmacogenomic analysis — determines how you metabolize drugs based on your DNA using PharmCAT, a widely used research tool developed by CPIC/PharmGKB. Note: PharmCAT is a research tool, not a clinically validated diagnostic. Results should be confirmed by a certified laboratory before making prescribing decisions.

Why

Identifies which drugs work well, which need dose adjustments, and which to avoid entirely. Covers 23 pharmacogenes affecting hundreds of medications.

Tool

• PharmCAT v3.2.0 (Pharmacogenomics Clinical Annotation Tool, CPIC/PharmGKB)
• Upgraded from 2.15.5 to 3.2.0 in v0.3.0. See docs/lessons-learned.md for migration notes (preprocessor rename, reporter flags, JSON property changes).

Docker Image

pgkb/pharmcat:3.2.0

Command

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

# Step 1: preprocess the VCF against the GRCh38 reference
docker run --rm \
  --cpus 2 --memory 4g \
  -v ${GENOME_DIR}/${SAMPLE}/vcf:/data \
  -v ${GENOME_DIR}/reference:/ref \
  pgkb/pharmcat:3.2.0 \
  python3 /pharmcat/pharmcat_vcf_preprocessor \
    -vcf /data/${SAMPLE}.vcf.gz \
    -refFna /ref/Homo_sapiens_assembly38.fasta \
    -o /data/ \
    -bf ${SAMPLE}

# Step 2: run PharmCAT on the preprocessed VCF
docker run --rm \
  --cpus 2 --memory 4g \
  -v ${GENOME_DIR}/${SAMPLE}/vcf:/data \
  pgkb/pharmcat:3.2.0 \
  java -jar /pharmcat/pharmcat.jar \
    -vcf /data/${SAMPLE}.preprocessed.vcf.bgz \
    -o /data/ \
    -bf ${SAMPLE} \
    -reporterJson \
    -reporterHtml

Output

• HTML report with drug recommendations per gene
• JSON report used by step 27 (${SAMPLE}.report.json)
• Preprocessed VCF (${SAMPLE}.preprocessed.vcf.bgz) generated as an intermediate
• Covers CYP2C19, CYP2D6, CYP2B6, CYP3A4/5, UGT1A1, DPYD, NAT2, TPMT, etc.
• Star allele calls with metabolizer status (Poor/Intermediate/Normal/Rapid/Ultra-rapid)

Key Genes

| Gene | Drugs Affected | Example | |—|—|—| | CYP2C19 | SSRIs, PPIs, clopidogrel | 1/17 = rapid → SSRIs fail faster | | CYP2D6 | 25% of all drugs, opioids, tamoxifen | Complex — may need BAM-based calling | | UGT1A1 | Irinotecan, bilirubin clearance | 28/28 = Gilbert’s syndrome | | DPYD | 5-FU, capecitabine (chemo) | Poor = lethal toxicity | | NAT2 | Isoniazid, hydralazine | Slow acetylator = increased toxicity |

Limitations

• CYP2D6 often returns Not called — gene has pseudogene homology that confounds VCF-based calling. Use Cyrius or StellarPGx (BAM-based) if CYP2D6 is critical.
• PharmCAT may disagree with lab reports on complex haplotypes (e.g., NAT2). Discrepancies can arise from different genome builds (hg19 vs hg38), different star allele definitions, or different variant calling pipelines. When a discrepancy matters clinically, compare both sets of raw variant calls and consult the PharmVar database for the current allele definitions — do not blindly trust either source.
• PharmCAT output structure changes across releases. If you upgrade PharmCAT, re-test step 27 (27-cpic-lookup.sh) because it parses the JSON output directly.

Maintenance

• The pipeline is pinned to pgkb/pharmcat:3.2.0 for reproducibility. Upgraded from 2.15.5 in v0.3.0 (Apr 2026). Breaking changes in the 3.x series are documented in docs/lessons-learned.md.
• Treat step 7 and step 27 as one upgrade unit. If you bump PharmCAT, rerun both on a known sample and diff diplotypes, phenotypes, JSON structure, and CPIC recommendation text before merging.
• Recheck CPIC / ClinPGx guidance at least quarterly, or sooner if a drug-gene pair you expose in step 27 gets a meaningful update upstream.

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