tooluniverse-acmg-variant-classification

What this skill does

# ACMG/AMP Variant Classification

## ACMG Reasoning

Each criterion (PS, PM, PP for pathogenic; BS, BP for benign) contributes a weighted piece of evidence for or against pathogenicity. The classification is the COMBINATION of all activated criteria, not any single criterion. Do not overweight a single finding.

The hierarchy is: PVS1 (very strong) > PS (strong) > PM (moderate) > PP (supporting). On the benign side: BA1 (stand-alone) > BS (strong) > BP (supporting). A frameshift in a LOF-intolerant gene (PVS1) plus a ClinVar expert-panel pathogenic entry (PS1) is pathogenic. A single PP criterion alone is not. The combination rule is what matters.

Two common errors to avoid: (1) seeing a "Pathogenic" ClinVar entry and stopping — that is PP5 (supporting) unless it has expert-panel review, not automatic confirmation; (2) dismissing a variant because one predictor says "tolerated" — discordant predictors mean neither PP3 nor BP4 applies, which is neutral evidence, not benign evidence.

Always apply criteria conservatively. When evidence is ambiguous, leave the criterion unmet. Cite the source for every criterion you activate so clinicians can audit the reasoning.

**KEY PRINCIPLES**:
1. **Criteria-driven** — cite which criteria were activated and why
2. **Conservative** — do not upgrade a criterion when evidence is ambiguous
3. **Gene-aware** — adjust thresholds based on gene mechanism (LOF vs. gain-of-function)
4. **Population-calibrated** — use ancestry-specific gnomAD frequencies, not just global AF
5. **Transparent** — show evidence for each criterion
6. **Source-referenced** — every criterion activation must cite the database/tool source
7. **English-first queries** — always use English terms in tool calls; respond in user's language

---

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

---

## When to Use

- "Classify BRCA2 c.5946delT using ACMG criteria"
- "Is this VUS pathogenic? NM_000059.4:c.7397T>C"
- "Apply ACMG guidelines to rs28897743"
- "What is the pathogenicity of CFTR p.Arg117His?"
- "ACMG classification for TP53 R248W"

---

## Tool Parameter Reference

| Tool | Key Parameters | Notes |
|------|---------------|-------|
| `VariantValidator_validate_variant` | `variant_description`, `genome_build`, `select_transcripts` | genome_build="GRCh38" |
| `VariantValidator_gene2transcripts` | `gene_symbol` | Returns MANE Select transcript |
| `MyVariant_query_variants` | `query` | HGVS or rsID. Returns ClinVar, gnomAD, CADD, REVEL, SIFT, PolyPhen |
| `EnsemblVEP_annotate_hgvs` | `hgvs_notation` | Consequence, colocated variants, ancestry gnomAD |
| `gnomad_search_variants` | `query` | rsID to gnomAD variant ID |
| `gnomad_get_variant` | `variant_id` | Per-ancestry population frequencies |
| `gnomad_get_gene_constraints` | `gene_symbol` | pLI, LOEUF, mis_z |
| `ClinVar_search_variants` | `query` | Variable response format: list OR `{status, data}` |
| `ClinVar_get_variant_details` | `variant_id` | ClinVar numeric ID |
| `civic_get_variants_by_gene` | `gene_id` | CIViC numeric gene ID (NOT symbol). Known: BRAF=5, BRCA2=19 |
| `UniProt_get_function_by_accession` | `accession` | Returns list of strings |
| `InterPro_get_entries_for_protein` | `accession` | Domain architecture by UniProt accession |
| `alphafold_get_prediction` | `qualifier` | UniProt accession; pLDDT confidence |
| `PubMed_search_articles` | `query`, `limit` | Returns list of dicts |
| `MyGene_query_genes` | `query` | Filter by `symbol` match (first hit may not match) |

---

## Phase 0: Variant Validation and Normalization

Wrong HGVS or wrong transcript cascades errors through every downstream criterion. Validate first.

1. **Get MANE Select transcript**: `VariantValidator_gene2transcripts(gene_symbol="BRCA2")`
2. **Validate variant**: `VariantValidator_validate_variant(variant_description="NM_000059.4:c.5946delT", genome_build="GRCh38", select_transcripts="mane_select")`
3. **Resolve gene IDs**: `MyGene_query_genes(query="BRCA2")` — extract Ensembl ID and UniProt accession. Filter results by `symbol == 'BRCA2'` (first hit may not match).
4. **Record**: HGVS coding, HGVS protein, genomic coordinates, variant type (frameshift/missense/nonsense/splice/synonymous/in-frame indel).

Accepted inputs: HGVS coding (NM_000059.4:c.5946delT), HGVS protein (BRCA2 p.Val600Glu), rsID (rs28897743), gene+change (BRCA1 c.68_69del), genomic coordinates.

---

## Phase 1: Population Frequency (BA1, BS1, BS2, PM2)

Population AF is among the strongest evidence in either direction. A variant at >5% in any population is almost certainly benign (BA1 — stand-alone, no further analysis needed). Absent from gnomAD supports pathogenicity (PM2, now usually applied as PM2_Supporting per ClinGen guidance).

Use ancestry-specific AF, not just global. A variant at 8% in East Asian populations but rare globally is benign in that ancestry context. For BS1, the threshold depends on disease prevalence and inheritance — the default is 1% for common diseases, 0.1% for rare.

```python
gnomad_search_variants(query="rs28897743")          # get gnomAD variant ID
gnomad_get_variant(variant_id="...")                 # per-ancestry frequencies
gnomad_get_gene_constraints(gene_symbol="BRCA2")     # pLI, LOEUF, mis_z
MyVariant_query_variants(query="rs28897743")          # fallback: gnomad_genome.af
```

If gnomAD data is unavailable, note the gap and continue — absence of data is not the same as evidence of absence.

---

## Phase 2: Computational Predictions (PP3, BP4)

No single predictor is definitive. The reasoning is: concordance across multiple independent predictors provides supporting evidence. Discordance means neither PP3 nor BP4 applies — it is neutral, not benign.

PP3 (supporting pathogenic) applies when the majority of predictors agree damaging, or when REVEL >= 0.7 alone (sufficient per ClinGen guidance). BP4 (supporting benign) requires ALL predictors to agree benign, or REVEL < 0.15 or CADD < 15. These criteria apply only to missense variants.

```python
MyVariant_query_variants(query="...")        # REVEL, CADD PHRED, AlphaMissense, SIFT, PolyPhen
EnsemblVEP_annotate_hgvs(hgvs_notation="...") # consequence, SpliceAI deltas
```

For non-missense variants, skip PP3/BP4 and focus on SpliceAI scores in Phase 5.

**Mechanism complement for VUS-resolution narratives**: PP3/BP4 is a vote count of predictor scores — it tells you *whether* the variant is damaging, not *how*. For a VUS resolution report where the verdict needs a mechanistic explanation (clinician asks "why is this damaging?"), add `ESM_explain_variant_mechanism(sequence=..., position=..., ref_aa=..., alt_aa=...)` — returns lost/gained ESMC-6B SAE feature categories (catalytic / ligand-binding / ptm / structural-stability / domain / etc.) with a one-line summary. This does not change PP3 strength but turns "PP3 satisfied (REVEL 0.78, AlphaMissense 0.85)" into "PP3 satisfied — and SAE shows catalytic-feature loss at position X, consistent with active-site disruption." Requires `ESM_API_KEY`; missense only.

---

## Phase 3: Clinical Database Evidence (PS1, PM5, PP5, BP6)

ClinVar aggregates clinical lab classifications. The reasoning: if the same amino acid change (different nucleotide) is established pathogenic, that is strong evidence (PS1) because the mechanism is the amino acid change. If a different pathogenic missense occurs at the same residue, that is moderate evidence (PM5) — the residue is functionally important.

PP5 applies when ClinVar shows Pathogenic with >= 2-star review (criteria provided, multiple submitters). Weight by the number of concordant submitters. Conflicting ClinVar interpretations mean neither PP5 nor BP6 should be applied. ClinGen has proposed downweighting PP5/BP6 — treat them as supporting, not strong.

```python
ClinVar_search_variants(