Bio variant calling clinical interpretation

Version Compatibility

Reference examples tested with: Entrez Direct 21.0+, bcftools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

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

Goal: Annotate variants with ClinVar clinical significance and filter by pathogenicity.

Approach: Download the ClinVar VCF, add CLNSIG/CLNDN/CLNREVSTAT fields with bcftools annotate, then filter by significance level.

"Find pathogenic variants in my VCF" → Cross-reference variants against ClinVar clinical assertions and extract those classified as pathogenic or likely pathogenic.

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)

Goal: Classify variants according to ACMG/AMP guidelines using automated criteria evaluation.

Approach: Convert VCF to ANNOVAR format, run InterVar to evaluate 28 ACMG criteria, and output five-tier 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

Goal: Restrict to rare variants that could be disease-causing.

Approach: Filter by gnomAD allele frequency threshold appropriate for the disease model (dominant vs. recessive).

# 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

Goal: Prioritize variants using computational pathogenicity predictors.

Approach: Filter by CADD PHRED score (deleteriousness) and REVEL score (missense pathogenicity), alone or in combination with ClinVar.

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

Goal: Implement a multi-criteria variant classification pipeline in Python.

Approach: Combine ClinVar lookups, population frequency, and computational scores (CADD, REVEL) into a tiered classification function.

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

Goal: Restrict analysis to variants within a clinical gene panel.

Approach: Filter by BED coordinates or VEP gene symbol annotations to target specific genes.

# 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

Goal: Run an end-to-end clinical variant interpretation pipeline from annotation through reporting.

Approach: Chain ClinVar annotation, rare variant filtering, pathogenicity extraction, VUS review, and TSV report generation.

#!/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