Skip to content
bio-variant-calling-clinical-interpretation logo

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

CLNSIGMeaningAction
PathogenicDisease-causingReport
Likely_pathogenicProbably disease-causingReport with caveat
Uncertain_significanceVUSMay report, needs follow-up
Likely_benignProbably not disease-causingUsually exclude
BenignNot disease-causingExclude
ConflictingMultiple interpretationsManual review

ClinVar Review Status

CLNREVSTATStarsMeaning
practice_guideline4Expert panel reviewed
reviewed_by_expert_panel3ClinGen expert reviewed
criteria_provided,_multiple_submitters2Consistent assertions
criteria_provided,_single_submitter1One submitter with criteria
no_assertion_criteria0No 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

CodeTypeDescription
PVS1Very StrongNull variant in gene where LOF is disease mechanism
PS1-4StrongSame AA change, functional studies, etc.
PM1-6ModerateHot spot, absent from controls, etc.
PP1-5SupportingCo-segregation, computational evidence

Benign Criteria

CodeTypeDescription
BA1Stand-aloneAF >5% in gnomAD
BS1-4StrongAF greater than expected, functional studies
BP1-7SupportingMissense 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

ResourceContentUse
ClinVarClinical assertionsPrimary lookup
OMIMGene-disease relationshipsGene prioritization
HGMDPublished mutationsLiterature evidence
gnomADPopulation frequenciesRarity filtering
ClinGenGene validity/​dosageLOF 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

More Other skills

Delegate independent tasks to specialized agents working concurrently with isolated context.

295.4K 206.3K
View

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

179.7K 100.1K
View

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

179.7K 89.5K
View

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

179.7K 76.1K
View

Fundamental design principles for writing maintainable, testable Python code.

40.2K 21.9K
View

Modern responsive layouts using container queries, fluid typography, CSS Grid, and mobile-first strategies.

40.2K 20K
View

Generate Playwright tests from scenarios using interactive browser exploration and validation.

39.7K 18.2K
View

Modern React state management with Redux Toolkit, Zustand, Jotai, and React Query for every state category.

40.2K 14.7K
View

GDPR-compliant data handling with consent management, data subject rights, and privacy controls.

40.2K 14.3K
View

Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.

40.2K 13.5K
View

Detailed multi-file refactor planning with safety checks and rollback guidance.

39.7K 13K
View