bio-variant-calling-clinical-interpretation

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Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.

mdbabumiamssm By mdbabumiamssm schedule Updated 2/4/2026
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name: bio-variant-calling-clinical-interpretation description: Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants. tool_type: mixed primary_tool: InterVar measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Clinical Variant Interpretation

Prioritize and interpret variants for clinical significance using databases and ACMG/AMP guidelines.

Core Capabilities

  • Ground variant narratives in ClinVar, gnomAD, ACMG/AMP criteria, and disease databases; cite evidence for each clinical claim; detect conflicts between database records; and keep pathogenicity evidence separate from clearly labeled model speculation.
  • Apply precision grounding for genetic variant summarization: retrieve database-backed evidence from ClinVar, gnomAD, ACMG/AMP criteria, disease-gene validity resources, and literature provenance; cite each claim; separate known evidence from model inference; label uncertainty explicitly; keep pathogenicity classification separate from explanatory prose; flag conflicting evidence or stale assertions.
  • Require precision-grounded genetic variant summaries to retrieve evidence from ClinVar, gnomAD, ACMG/AMP criteria, and disease databases before synthesis, with provenance-linked statements, explicit uncertainty, and conflict reporting.
  • Generate Precision Grounding-style LLM variant summaries by separating database facts from model synthesis, citing ClinVar variation/accession records, gnomAD population evidence, and ACMG/AMP criteria used for each statement, then running hallucination checks against the retrieved evidence before including content in genetic variant reports.
  • Produce precision-grounded variant summaries with explicit provenance for ClinVar assertions, gnomAD population frequency, COSMIC somatic evidence, and literature findings; surface conflicts across sources for manual review, and do not make or upgrade pathogenicity claims unless directly supported by retrieved evidence and applicable ACMG/AMP criteria.
  • Before drafting genetic variant summaries, retrieve and reconcile evidence from ClinVar, gnomAD, dbSNP, COSMIC, ACMG/AMP criteria, and disease databases; include only citation-backed claims, detect conflicts across sources, and label uncertainty when evidence is incomplete or discordant.
  • For LLM-assisted variant summarization, link each interpretive statement to retrieved ClinVar, gnomAD, dbSNP, COSMIC, or ACMG/AMP evidence; run hallucination checks against those records and remove unsupported claims before reporting.
  • For evidence-grounded genetic variant summaries, cross-check ClinVar assertions, gnomAD population frequencies, and dbSNP identifiers before synthesis; attach citations or database accession links to each assertion, report source conflicts explicitly, and audit the final text against retrieved records to remove hallucinated or unsupported content.
  • Use precision-grounded review checkpoints before clinical reporting: reconcile ClinVar clinical assertions, gnomAD frequency evidence, and OMIM-style disease-gene context; tag every summary claim with source provenance; label unsupported, discordant, or incomplete evidence as uncertain; and require manual review of conflicts before final report language is released.
  • Create auditable precision-grounded variant-summary outputs by retrieving database-backed ClinVar, gnomAD, and literature evidence; mapping each ACMG/AMP evidence code to supporting provenance; documenting contradictions between sources; running hallucination checks against retrieved records; and preserving source-linked evidence tables with the final summary.

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

References

Install via CLI
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill bio-variant-calling-clinical-interpretation
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