Delegate independent tasks to specialized agents working concurrently with isolated context.
Clinical Variant Interpretation
bio-variant-calling-clinical-interpretation
Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.
SKILL.md
Full skill instructions
Clinical Variant Interpretation
Prioritize and interpret variants for clinical significance using databases and ACMG/AMP guidelines.
Interpretation Framework
Annotated VCF
│
├── Database Lookup
│ ├── ClinVar (clinical assertions)
│ ├── OMIM (disease associations)
│ └── gnomAD (population frequency)
│
├── Computational Predictions
│ ├── SIFT, PolyPhen-2
│ ├── CADD, REVEL
│ └── SpliceAI
│
├── ACMG Classification
│ └── Pathogenic → Likely Pathogenic → VUS → Likely Benign → Benign
│
└── Prioritized Variant List
ClinVar Annotation
Download ClinVar
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi
Annotate with bcftools
bcftools annotate \
-a clinvar.vcf.gz \
-c INFO/CLNSIG,INFO/CLNDN,INFO/CLNREVSTAT \
input.vcf.gz -Oz -o with_clinvar.vcf.gz
Filter Pathogenic Variants
# Pathogenic or Likely pathogenic
bcftools view -i 'INFO/CLNSIG~"Pathogenic" || INFO/CLNSIG~"Likely_pathogenic"' \
with_clinvar.vcf.gz -Oz -o pathogenic.vcf.gz
# Exclude benign
bcftools view -e 'INFO/CLNSIG~"Benign" || INFO/CLNSIG~"Likely_benign"' \
with_clinvar.vcf.gz -Oz -o not_benign.vcf.gz
ClinVar Significance Levels
| CLNSIG | Meaning | Action |
|---|---|---|
| Pathogenic | Disease-causing | Report |
| Likely_pathogenic | Probably disease-causing | Report with caveat |
| Uncertain_significance | VUS | May report, needs follow-up |
| Likely_benign | Probably not disease-causing | Usually exclude |
| Benign | Not disease-causing | Exclude |
| Conflicting | Multiple interpretations | Manual review |
ClinVar Review Status
| CLNREVSTAT | Stars | Meaning |
|---|---|---|
| practice_guideline | 4 | Expert panel reviewed |
| reviewed_by_expert_panel | 3 | ClinGen expert reviewed |
| criteria_provided,_multiple_submitters | 2 | Consistent assertions |
| criteria_provided,_single_submitter | 1 | One submitter with criteria |
| no_assertion_criteria | 0 | No criteria provided |
# Filter for high-confidence assertions (2+ stars)
bcftools view -i 'INFO/CLNREVSTAT~"multiple_submitters" || \
INFO/CLNREVSTAT~"expert_panel" || \
INFO/CLNREVSTAT~"practice_guideline"' \
with_clinvar.vcf.gz -Oz -o high_confidence.vcf.gz
InterVar (ACMG Classification)
Automated ACMG/AMP variant classification.
Installation
git clone https://github.com/WGLab/InterVar.git
cd InterVar
# Download databases per documentation
Run InterVar
python Intervar.py \
-i input.avinput \
-o output \
-b hg38 \
-d humandb/ \
--input_type=AVinput
From VCF
# Convert VCF to ANNOVAR format
convert2annovar.pl -format vcf4 input.vcf > input.avinput
# Run InterVar
python Intervar.py -i input.avinput -o intervar_results -b hg38
ACMG/AMP Criteria
Pathogenic Criteria
| Code | Type | Description |
|---|---|---|
| PVS1 | Very Strong | Null variant in gene where LOF is disease mechanism |
| PS1-4 | Strong | Same AA change, functional studies, etc. |
| PM1-6 | Moderate | Hot spot, absent from controls, etc. |
| PP1-5 | Supporting | Co-segregation, computational evidence |
Benign Criteria
| Code | Type | Description |
|---|---|---|
| BA1 | Stand-alone | AF >5% in gnomAD |
| BS1-4 | Strong | AF greater than expected, functional studies |
| BP1-7 | Supporting | Missense in gene with truncating mechanism |
Population Frequency Filtering
# Rare variants only (gnomAD AF < 0.01)
bcftools view -i 'INFO/gnomAD_AF<0.01 || INFO/gnomAD_AF="."' \
input.vcf.gz -Oz -o rare.vcf.gz
# Ultra-rare for dominant diseases (AF < 0.0001)
bcftools view -i 'INFO/gnomAD_AF<0.0001 || INFO/gnomAD_AF="."' \
input.vcf.gz -Oz -o ultrarare.vcf.gz
Pathogenicity Score Filtering
CADD Scores
# CADD > 20 (top 1% deleterious)
bcftools view -i 'INFO/CADD_PHRED>20' input.vcf.gz -Oz -o cadd_filtered.vcf.gz
# CADD > 30 (top 0.1%)
bcftools view -i 'INFO/CADD_PHRED>30' input.vcf.gz -Oz -o highly_deleterious.vcf.gz
REVEL Scores
# REVEL > 0.5 (likely pathogenic)
bcftools view -i 'INFO/REVEL>0.5' input.vcf.gz -Oz -o revel_filtered.vcf.gz
Combined Filtering
bcftools view -i '(INFO/CADD_PHRED>20 || INFO/REVEL>0.5) && \
(INFO/CLNSIG~"Pathogenic" || INFO/CLNSIG~"Likely" || INFO/CLNSIG=".")' \
input.vcf.gz -Oz -o prioritized.vcf.gz
Python: Clinical Prioritization
from cyvcf2 import VCF, Writer
def classify_variant(variant):
clnsig = variant.INFO.get('CLNSIG', '')
af = variant.INFO.get('gnomAD_AF', 0) or 0
cadd = variant.INFO.get('CADD_PHRED', 0) or 0
revel = variant.INFO.get('REVEL', 0) or 0
# Known pathogenic
if 'Pathogenic' in str(clnsig):
return 'PATHOGENIC'
if 'Likely_pathogenic' in str(clnsig):
return 'LIKELY_PATHOGENIC'
# Known benign
if 'Benign' in str(clnsig) or af > 0.05:
return 'BENIGN'
# Computational prediction
if cadd > 25 or revel > 0.7:
if af < 0.0001:
return 'LIKELY_PATHOGENIC'
elif af < 0.01:
return 'VUS_FAVOR_PATH'
if cadd < 10 and revel < 0.3:
return 'LIKELY_BENIGN'
return 'VUS'
vcf = VCF('annotated.vcf.gz')
results = []
for variant in vcf:
classification = classify_variant(variant)
if classification in ('PATHOGENIC', 'LIKELY_PATHOGENIC', 'VUS_FAVOR_PATH'):
gene = variant.INFO.get('SYMBOL', 'Unknown')
consequence = variant.INFO.get('Consequence', 'Unknown')
results.append({
'chrom': variant.CHROM,
'pos': variant.POS,
'ref': variant.REF,
'alt': variant.ALT[0],
'gene': gene,
'consequence': consequence,
'classification': classification,
'clnsig': variant.INFO.get('CLNSIG', '.'),
'cadd': variant.INFO.get('CADD_PHRED', '.'),
'af': variant.INFO.get('gnomAD_AF', '.')
})
# Output prioritized variants
for r in results:
print(f"{r['gene']}\t{r['chrom']}:{r['pos']}\t{r['consequence']}\t{r['classification']}")
Gene Panel Filtering
# Filter to gene panel
bcftools view -R gene_panel.bed input.vcf.gz -Oz -o panel_variants.vcf.gz
# Or by gene symbol (requires VEP annotation)
bcftools view -i 'INFO/CSQ~"BRCA1" || INFO/CSQ~"BRCA2"' \
input.vcf.gz -Oz -o brca_variants.vcf.gz
Disease-Specific Resources
| Resource | Content | Use |
|---|---|---|
| ClinVar | Clinical assertions | Primary lookup |
| OMIM | Gene-disease relationships | Gene prioritization |
| HGMD | Published mutations | Literature evidence |
| gnomAD | Population frequencies | Rarity filtering |
| ClinGen | Gene validity/dosage | LOF interpretation |
Reporting Template
bcftools query -f '%CHROM\t%POS\t%REF\t%ALT\t%INFO/SYMBOL\t%INFO/Consequence\t\
%INFO/CLNSIG\t%INFO/CLNDN\t%INFO/gnomAD_AF\t%INFO/CADD_PHRED\n' \
prioritized.vcf.gz > clinical_report.tsv
Complete Workflow
#!/bin/bash
set -euo pipefail
INPUT=$1
CLINVAR=$2
OUTPUT_PREFIX=$3
echo "=== Add ClinVar annotations ==="
bcftools annotate -a $CLINVAR \
-c INFO/CLNSIG,INFO/CLNDN,INFO/CLNREVSTAT,INFO/CLNVC \
$INPUT -Oz -o ${OUTPUT_PREFIX}_clinvar.vcf.gz
echo "=== Filter rare variants ==="
bcftools view -i 'INFO/gnomAD_AF<0.01 || INFO/gnomAD_AF="."' \
${OUTPUT_PREFIX}_clinvar.vcf.gz -Oz -o ${OUTPUT_PREFIX}_rare.vcf.gz
echo "=== Extract pathogenic/likely pathogenic ==="
bcftools view -i 'INFO/CLNSIG~"athogenic"' \
${OUTPUT_PREFIX}_rare.vcf.gz -Oz -o ${OUTPUT_PREFIX}_pathogenic.vcf.gz
echo "=== Extract high-impact VUS ==="
bcftools view -i 'INFO/CLNSIG~"Uncertain" && INFO/CADD_PHRED>20' \
${OUTPUT_PREFIX}_rare.vcf.gz -Oz -o ${OUTPUT_PREFIX}_vus_review.vcf.gz
echo "=== Generate report ==="
bcftools query -H -f '%CHROM\t%POS\t%REF\t%ALT\t%INFO/SYMBOL\t%INFO/Consequence\t\
%INFO/CLNSIG\t%INFO/CLNDN\t%INFO/gnomAD_AF\t%INFO/CADD_PHRED\n' \
${OUTPUT_PREFIX}_pathogenic.vcf.gz > ${OUTPUT_PREFIX}_report.tsv
echo "=== Complete ==="
echo "Pathogenic: ${OUTPUT_PREFIX}_pathogenic.vcf.gz"
echo "VUS for review: ${OUTPUT_PREFIX}_vus_review.vcf.gz"
echo "Report: ${OUTPUT_PREFIX}_report.tsv"
Related Skills
- variant-calling/variant-annotation - VEP/SnpEff annotation
- variant-calling/filtering-best-practices - Quality filtering
- database-access/entrez-fetch - Download ClinVar/OMIM data
- pathway-analysis/go-enrichment - Gene set analysis
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