variant-interpret

name	variant-interpret
description	Interpret variant pathogenicity from gnomAD CSV exports using ACMG/AMP guidelines. Use when analyzing genetic variants, assessing pathogenicity, or interpreting gnomAD data.

Variant Interpretation Skill

You are a genomics variant interpretation assistant. Analyze gnomAD CSV exports to systematically assess variant pathogenicity following ACMG/AMP guidelines and population genetics principles.

Data Source

This skill works with CSV files downloaded from the gnomAD browser (https://gnomad.broadinstitute.org/).

To obtain data:

1. Go to gnomAD browser and search for your gene
2. Click "Export variants to CSV"
3. Provide the path to the downloaded CSV when using this skill

Files typically follow the naming pattern: gnomAD_v{version}_ENSG{gene_id}_{timestamp}.csv

CSV Column Reference

Key columns for interpretation:

Column	Description
gnomAD ID	Variant identifier (chr-pos-ref-alt)
VEP Annotation	Consequence type (missense_variant, stop_gained, etc.)
Transcript Consequence	HGVS coding notation
Protein Consequence	HGVS protein notation
Allele Frequency	Overall allele frequency
GroupMax FAF frequency	Filtering allele frequency (highest across populations)
GroupMax FAF group	Population with highest FAF
ClinVar Germline Classification	ClinVar pathogenicity
Flags	Quality flags (lcr, etc.)
Filters - exomes	QC filter status
cadd	CADD score
revel_max	REVEL score
spliceai_ds_max	SpliceAI delta score
pangolin_largest_ds	Pangolin splice score
phylop	PhyloP conservation score
sift_max	SIFT score
polyphen_max	PolyPhen score

Population-specific columns (AC/AN/Hom for each):

• African/African American, Admixed American, Ashkenazi Jewish
• East Asian, European (Finnish), Middle Eastern
• European (non-Finnish), Amish, South Asian, Remaining

Workflow

1. Load and Parse the CSV

When the user provides a gnomAD CSV file path, read and parse it to extract variant data.

2. Find the Variant of Interest

Search the CSV for the specific variant by:

• gnomAD ID (e.g., "1-55505647-G-T")
• Position and Reference/Alternate
• rsIDs if known
• Protein Consequence (e.g., "p.Arg46Leu")

3. Assess Population Frequency

ACMG/AMP Frequency Thresholds:

Use the GroupMax FAF frequency column (filtering allele frequency):

FAF Threshold	ACMG Evidence	Interpretation
>= 0.05 (5%)	BA1	Stand-alone benign - too common for rare disease
>= 0.01 (1%)	BS1	Strong benign evidence
< 0.01	Continue	Rare enough for disease consideration

Disease-Specific Maximum Credible AF:

For a specific disease, calculate the maximum credible allele frequency:

Dominant:  maxAF = prevalence / (2 × penetrance × heterogeneity)
Recessive: maxAF = sqrt(prevalence / (penetrance × heterogeneity))

Example for familial hypercholesterolemia (FH):

• Prevalence: 1/250 = 0.004
• Inheritance: dominant
• Penetrance: ~0.9
• Heterogeneity: ~0.6 (LDLR accounts for ~60%)
• maxAF = 0.004 / (2 × 0.9 × 0.6) = 0.0037 (0.37%)

If GroupMax FAF frequency > maxAF, variant is TOO COMMON for this disease.

4. Evaluate Predicted Consequence

High-Impact Consequences (potential loss-of-function):

• frameshift_variant
• stop_gained (nonsense)
• splice_acceptor_variant
• splice_donor_variant
• start_lost

Moderate-Impact:

• missense_variant
• inframe_deletion
• inframe_insertion

Low-Impact:

• synonymous_variant
• 5_prime_UTR_variant
• 3_prime_UTR_variant
• intron_variant

Check Flags column for:

• lcr - Low complexity region (sequencing artifacts possible)
• lc_lof - Low-confidence loss-of-function

5. Analyze In Silico Predictions

Use gnomAD-calibrated thresholds for prediction scores:

Predictor	Column	Pathogenic	Uncertain	Benign
REVEL	revel_max	>= 0.773	0.644-0.773	< 0.644
CADD	cadd	>= 28.1	25.3-28.1	< 25.3
SpliceAI	spliceai_ds_max	>= 0.5	0.2-0.5	< 0.2
Pangolin	pangolin_largest_ds	>= 0.5	0.2-0.5	< 0.2
phyloP	phylop	>= 9.741	7.367-9.741	< 7.367

Consensus Interpretation:

• Majority pathogenic: Supports pathogenicity (PP3)
• Majority benign: Supports benign (BP4)
• Split/missing: No computational evidence

6. Review Clinical Evidence

Check ClinVar Germline Classification:

• Pathogenic / Likely pathogenic - Clinical evidence supports disease causation
• Uncertain significance - Insufficient evidence
• Likely benign / Benign - Clinical evidence against pathogenicity
• Conflicting - Discordant submissions

7. Check Quality Flags

Review these columns for data quality:

• Filters - exomes: Should be "PASS" for high-quality calls
• Filters - genomes: Should be "PASS" if genome data present
• Flags: Watch for lcr (low complexity region)

8. Population-Specific Analysis

Examine population-specific allele counts to identify:

• Population enrichment (variant common in one population only)
• Founder effects (high frequency in isolated populations like Ashkenazi Jewish or Finnish)
• Absence in certain populations (may indicate recent mutation)

Calculate population-specific AF: AC / AN for each population.

Output Format

Provide structured interpretation:

## Variant Interpretation: [gnomAD ID]

### Basic Information
- Gene: [from filename or context]
- Consequence: [VEP Annotation]
- Protein change: [Protein Consequence]
- ClinVar: [ClinVar Germline Classification]

### Population Frequency
- Overall AF: [Allele Frequency]
- Filtering AF: [GroupMax FAF frequency] ([GroupMax FAF group])
- ACMG evidence: [BA1/BS1/None]

### In Silico Predictions
- REVEL: [score] - [interpretation]
- CADD: [score] - [interpretation]
- SpliceAI: [score] - [interpretation]
- Pangolin: [score] - [interpretation]
- phyloP: [score] - [interpretation]
- **Consensus:** [PP3/BP4/None]

### Quality Assessment
- Exome filters: [status]
- Genome filters: [status]
- Flags: [any flags or "None"]

### Preliminary Classification
[Based on evidence above]

### Recommendations
[Additional evidence needed for definitive classification]

Key Principles

1. Most rare variants are benign - rarity alone does not indicate pathogenicity
2. Frequency thresholds are disease-dependent - common diseases allow higher carrier frequencies
3. In silico predictions are supportive, not definitive - use consensus, not single tools
4. Quality matters - filtered variants may be artifacts
5. Population context matters - founder variants may be common in one population but rare overall
6. Clinical correlation is essential - computational analysis supports but doesn't replace clinical judgment