Clinical Variant Interpreter

Systematic variant interpretation using ToolUniverse - from raw variant calls to ACMG-classified clinical recommendations with structural impact analysis.

Triggers

Use this skill when users:

• Ask about variant interpretation, classification, or pathogenicity
• Have VCF data needing clinical annotation
• Need ACMG classification for variants
• Want structural impact analysis for missense variants

Key Principles

1. ACMG-Guided - Follow ACMG/AMP 2015 guidelines with explicit evidence codes
2. Structural Evidence - Use AlphaFold2 for novel structural impact analysis
3. Population Context - gnomAD frequencies with ancestry-specific data
4. Actionable Output - Clear recommendations, not just classifications
5. English-first queries - Always use English terms in tool calls; respond in user's language

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LOOK UP, DON'T GUESS

When asked about a variant's significance, query ClinVar/gnomAD/CIViC FIRST. Never classify a variant without checking databases. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.

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Workflow Overview

Phase 1: VARIANT IDENTITY        → Normalize HGVS, map gene/transcript/consequence
Phase 2: CLINICAL DATABASES       → ClinVar, gnomAD, OMIM, ClinGen, COSMIC, SpliceAI
Phase 2.5: REGULATORY CONTEXT     → ChIPAtlas, ENCODE (non-coding variants only)
Phase 3: COMPUTATIONAL PREDICTIONS → CADD, AlphaMissense, EVE, SIFT/PolyPhen
Phase 4: STRUCTURAL ANALYSIS      → PDB/AlphaFold2, domains, functional sites (VUS/novel)
Phase 4.5: EXPRESSION CONTEXT     → CELLxGENE, GTEx tissue expression
Phase 5: LITERATURE EVIDENCE      → PubMed, EuropePMC, BioRxiv, MedRxiv
Phase 6: ACMG CLASSIFICATION      → Evidence codes, classification, recommendations

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Phase 1: Variant Identity

Tools: MyVariant_query_variants, EnsemblVar_get_variant_consequences, NCBIGene_search, VariantValidator_gene2transcripts, VariantValidator_validate_variant

VariantValidator_gene2transcripts: Look up MANE Select and MANE Plus Clinical transcripts for a gene. Use this to identify the correct canonical transcript before variant annotation.

• Parameters: gene_symbol (e.g. "TP53"), transcript_set ("mane" | "refseq" | "ensembl" | "all"), genome_build ("GRCh38" default)
• Returns: Array of {current_symbol, transcripts: [{reference, annotations: {mane_select, mane_plus_clinical}}]}
• Aliases: gene and gene_name also accepted for gene_symbol

VariantValidator_validate_variant: Validate HGVS variant descriptions and get normalized notation with genomic/transcript/protein consequences.

• Parameters: genome_build ("GRCh37" | "GRCh38"), variant_description (HGVS, e.g. "NM_007294.4:c.5266dup"), select_transcripts (transcript or "all")
• Returns: Validated HGVS, protein consequence, genomic coordinates, gene IDs

Capture: HGVS notation (c. and p.), gene symbol, canonical transcript (MANE Select via VariantValidator), consequence type, amino acid change, exon/intron location.

Phase 2: Clinical Databases

Tools: ClinVar_search_variants, gnomad_search_variants, gnomad_get_variant, OMIM_search, OMIM_get_entry, ClinGen_search_gene_validity, ClinGen_search_dosage_sensitivity, ClinGen_search_actionability, COSMIC_search_mutations, COSMIC_get_mutations_by_gene, DisGeNET_search_gene, DisGeNET_get_vda, SpliceAI_predict_splice, SpliceAI_get_max_delta, civic_get_variants_by_gene, civic_search_evidence_items, civic_search_assertions

gnomAD two-step workflow: gnomad_search_variants only accepts rsIDs or variant IDs (not gene names). Search by rsID first, then use the returned variant_id with gnomad_get_variant to get population allele frequencies.

CIViC: Use civic_search_genes(query="<gene_symbol>") to find the CIViC gene ID dynamically (do NOT rely on a hardcoded lookup table). Then use civic_get_variants_by_gene(gene_id=<id>) and civic_search_evidence_items for actionability details. If civic_search_genes returns no results, the gene may not be curated in CIViC — note this gap.

OncoKB note: Demo mode only supports BRAF, TP53, ROS1. For other genes, set ONCOKB_API_TOKEN environment variable.

Use SpliceAI for: intronic variants near splice sites, synonymous variants, exonic variants near splice junctions.

See CODE_PATTERNS.md for implementation details.

Phase 2.5: Regulatory Context (Non-Coding Only)

Apply for intronic (non-splice), promoter, UTR, or intergenic variants near disease genes.

Tools: ChIPAtlas_enrichment_analysis, ChIPAtlas_get_peak_data, ENCODE_search_experiments, ENCODE_get_experiment

Phase 2.9: Short-Circuit Check

Before full ACMG classification, check if the variant already has an expert panel classification in ClinVar. Use MyVariant_query_variants with the rsID or HGVS notation — the clinvar field in the response includes clinical significance, review status, and RCV records. If an expert panel has already classified the variant as Pathogenic or Benign, note this prominently and focus on confirming/contextualizing rather than de novo classification.

Phase 3: Computational Predictions

Primary approach: MyVariant_query_variants with fields=dbnsfp,clinvar,cadd,gnomad_genome retrieves 15+ predictor scores (SIFT, PolyPhen, CADD, REVEL, AlphaMissense, MetaRNN, FATHMM, GERP, PhyloP, etc.) in a single call. This is usually sufficient.

REVEL/AlphaMissense fallback: If MyVariant_query_variants returns no dbnsfp block, use the dedicated tool:

1. MyVariant_get_pathogenicity_scores (PREFERRED FALLBACK) — returns REVEL, AlphaMissense, SIFT, PolyPhen2, MetaRNN, GERP, PhyloP, and more in a single call with pre-configured dbnsfp fields. Input: variant_id (rsID or HGVS genomic).
2. CADD_get_variant_score (PHRED 0-99) — works for most variants
3. AlphaMissense_get_variant_score (0-1, needs UniProt ID) — missense only
4. EVE_get_variant_score (0-1) — missense only
5. EnsemblVEP_annotate_hgvs (VEP with colocated variants) — includes SIFT/PolyPhen
6. If REVEL is still unavailable, note this as a limitation and rely on CADD + SIFT + PolyPhen consensus. REVEL absence does not prevent classification.

Consensus: Run CADD (all variants) + AlphaMissense + EVE (missense). 2+ concordant damaging = strong PP3; 2+ concordant benign = strong BP4.

See ACMG_CLASSIFICATION.md for thresholds.

Phase 4: Structural Analysis (VUS/Novel Missense)

Tools: PDBe_get_uniprot_mappings, NvidiaNIM_alphafold2, alphafold_get_prediction (param: qualifier, e.g., UniProt accession), InterPro_get_protein_domains, UniProt_get_function_by_accession

Workflow: Get structure -> map residue -> assess domain/functional site -> predict destabilization.

AlphaFold size limitation: Very large proteins (>2,700 aa, e.g., BRCA2 at 3,418 aa) may not have AlphaFold predictions via the standard API. Fall back to published structural studies or PDBe_get_uniprot_mappings for experimental structures.

Phase 4.5: Expression Context

Tools: CELLxGENE_get_expression_data, CELLxGENE_get_cell_metadata, GTEx_get_median_gene_expression

Confirms gene expression in disease-relevant tissues. Supports PP4 if highly restricted; challenges classification if not expressed in affected tissue.

Phase 5: Literature Evidence

Tools: PubMed_search_articles, EuropePMC_search_articles, BioRxiv_list_recent_preprints, MedRxiv_get_preprint, openalex_search_works, SemanticScholar_search_papers

Always flag preprints as NOT peer-reviewed.

Phase 6: ACMG Classification

Apply all relevant evidence codes (PVS1, PS1, PS3, PM1, PM2, PM5, PP3, PP5 for pathogenic; BA1, BS1, BS3, BP4, BP7 for benign). See ACMG_CLASSIFICATION.md for the complete algorithm.

Gene-Specific Population Frequency Thresholds

BS1 (allele frequency too high for disorder) requires gene-specific calibration, not a universal cutoff:

• High-penetrance genes (BRCA1, TP53): BS1 threshold ~0.0001
• Moderate-penetrance genes (PALB2, ATM, CHEK2): BS1 threshold ~0.001
• Low-penetrance/common disease genes: BS1 threshold higher, depends on disease prevalence
• Formula: BS1 threshold = (disease prevalence × max allelic contribution × max genetic contribution) / penetrance
• When in doubt, compare the variant's AF to the highest AF of any known pathogenic variant in the same gene — if it exceeds that, BS1 is likely applicable.

Handling Conflicting Evidence: Functional vs Epidemiological

This is one of the most challenging scenarios in variant interpretation. When a biochemical assay shows damage but population/epidemiological data shows no disease association:

1. Epidemiological data generally trumps in-vitro assays for clinical classification. A variant found at ~0.1% frequency with no disease association in 40K+ cases is unlikely to be clinically significant, even if it reduces protein function in a tube.
2. Apply PS3/BS3 carefully: ClinGen's SVI recommends that PS3 (functional evidence for pathogenicity) requires the assay to be validated against known pathogenic AND known benign controls. A single biochemical study without such validation is PS3_Supporting at best.
3. Hypomorphic variants: Some variants genuinely reduce protein function (detectable in sensitive assays) but not enough to cause disease. This is biologically real and does not make them pathogenic.
4. Document the conflict explicitly in the report. State: "Biochemical assay X shows [result], but case-control study Y with N cases found no significant disease association. Per ACMG guidelines, the epidemiological evidence is weighted more heavily for clinical classification."

Bayesian ACMG Point System (Tavtigian et al. 2018)

Modern clinical labs use a point-based system instead of the original rule-counting approach:

Evidence Level	Pathogenic Points	Benign Points
Very Strong (PVS1)	+8	--
Strong (PS1-PS4)	+4 each	-4 each (BS1-BS4)
Moderate (PM1-PM6)	+2 each	--
Supporting (PP1-PP5)	+1 each	-1 each (BP1-BP7)
Stand-alone (BA1)	--	-8

Classification by total points:

• Pathogenic: >= 10 points
• Likely Pathogenic: 6-9 points
• VUS: -5 to 5 points
• Likely Benign: -6 to -9 points
• Benign: <= -10 points

This system handles conflicting evidence naturally — a variant with PS3 (+4) and BS1 (-4) and BP4 (-1) nets -1, which is VUS. The original rule-based approach struggles with this scenario.

Computational procedure: ACMG Bayesian classification

# Automated ACMG point calculation
# Input: dict of evidence codes with their applied strength

def classify_acmg(evidence: dict) -> dict:
    """
    Classify a variant using the Bayesian ACMG point system.

    Args:
        evidence: dict mapping ACMG codes to strength levels.
            Pathogenic codes: 'very_strong', 'strong', 'moderate', 'supporting'
            Benign codes: 'stand_alone', 'strong', 'supporting'

    Example:
        evidence = {
            'BS1': 'strong',       # AF too high
            'BS3': 'supporting',   # Epidemiological evidence against pathogenicity
            'BP6': 'supporting',   # ClinVar benign consensus
            'PP3': 'supporting',   # Computational predictors say damaging
        }
    """
    pathogenic_points = {
        'very_strong': 8, 'strong': 4, 'moderate': 2, 'supporting': 1
    }
    benign_points = {
        'stand_alone': -8, 'strong': -4, 'supporting': -1
    }

    total = 0
    details = []
    for code, strength in evidence.items():
        if code.startswith(('PVS', 'PS', 'PM', 'PP')):
            pts = pathogenic_points.get(strength, 0)
        elif code.startswith(('BA', 'BS', 'BP')):
            pts = benign_points.get(strength, 0)
        else:
            pts = 0
        total += pts
        details.append(f"{code} ({strength}): {pts:+d}")

    if total >= 10:
        classification = "Pathogenic"
    elif 6 <= total <= 9:
        classification = "Likely Pathogenic"
    elif -5 <= total <= 5:
        classification = "VUS"
    elif -9 <= total <= -6:
        classification = "Likely Benign"
    else:
        classification = "Benign"

    return {
        'classification': classification,
        'total_points': total,
        'evidence_breakdown': details
    }

# Example: PALB2 c.2816T>G (from test case)
result = classify_acmg({