bio-clinical-databases-variant-prioritization

What this skill does

## Version Compatibility

Reference examples tested with: pandas 2.2+, cyvcf2 0.30+, pyhgvs 0.12+, Exomiser 14.0+ (Smedley 2015), Phen2Gene 1.2+ (Zhao 2020), DeNovoGear 1.1.1+ (Ramu 2013), WhatsHap 2.0+ (Patterson 2015), HPO 2024+ (Human Phenotype Ontology). ACMG Secondary Findings list is v3.2 (Miller 2023): 81 genes.

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`

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Phenotype-driven prioritization REQUIRES high-quality HPO terms; without rich phenotypic input Exomiser/AMELIE degrade significantly.

# Rare-Disease Variant Prioritization Pipeline

**'Prioritize candidate disease-causing variants from this trio exome'** -> Filter to rare + functional + inheritance-consistent variants; rank by phenotype concordance; flag ACMG SF v3.2 incidental findings; report tiers with classification logic deferred to `acmg-classification`.

- Python (filtering pipeline): pandas + cyvcf2 + myvariant.info aggregation
- CLI (phenotype-driven ranking): `exomiser --analysis hiPHIVE-prioritised.yml`
- Python (de novo calling): DeNovoGear / Triodenovo / PossibleDeNovo
- CLI (compound het phasing): `whatshap phase --indels` for singletons; trio-based for families
- Python (HPO concordance): Phen2Gene / AMELIE / Phenolyzer
- VCEP curations: `https://cspec.genome.network/cspec/ui/svi/all`

## Pipeline Architecture: The Standard Rare-Disease Funnel

Typical trio exome enters as 40,000-100,000 variants per individual; reaches diagnostic candidate list of 1-10 variants through cascading filters:

| Stage | Filter | Variant count (typical trio) |
|-------|--------|------------------------------|
| Raw joint-called | -- | 100k-150k |
| QC filter (PASS, depth, GQ, missingness) | GATK best practices + Hail QC | 80k-120k |
| Population frequency | gnomAD grpmax_faf95 < 0.0001 (or disease-specific Whiffin max-credible-AF) | 5k-15k |
| Functional consequence | Coding / splice / regulatory | 1k-3k |
| Inheritance pattern | de novo / AR-hom / AR-compoundhet / X-linked / mosaic | 50-500 |
| Phenotype concordance | Exomiser hiPHIVE / Phen2Gene / AMELIE score | 5-50 |
| ACMG classification | Defer to `acmg-classification` | 1-10 |
| ACMG SF v3.2 cross-check | Miller 2023 (81 genes) | Separate output |

## Inheritance-Based Filtering

| Pattern | Filter |
|---------|--------|
| **De novo (DNV)** | Variant in proband, absent in both parents; needs trio | Apply DeNovoGear / Triodenovo / GATK PossibleDeNovo; visual IGV inspection (~10-30% false-positive rate without) |
| **Autosomal recessive; homozygous** | Hom-alt in proband; het in both parents | gnomAD grpmax_faf95 < 0.005 recessive threshold (Whiffin formula) |
| **Autosomal recessive; compound het** | Two het variants in same gene on opposite alleles | Trio-phased OR read-based phasing via WhatsHap (works within ~500 bp; longer needs parents or long-read) |
| **X-linked recessive** | Male proband hemizygous; carrier mother het | chrX coords; check Klinefelter / mosaic XXY |
| **X-linked dominant** | Het in affected; consider XCI skewing in females | Report XCI status if relevant |
| **Mitochondrial heteroplasmy** | mtDNA variant present at varying heteroplasmy across tissues | Use MITOMAP + HmtVar; ACMG criteria do not apply directly |
| **Mosaic** | Sub-clonal VAF in proband; absent in inherited transmissions | VAF 5-30% suggestive; tissue-dependent (blood vs buccal vs affected tissue) |

## De Novo Calling: Trio Analysis

**Goal:** Identify variants present in proband but absent in both parents with high specificity.

**Approach:** Use specialized DNV callers; supplement with manual IGV inspection.

| Tool | Approach | Use case |
|------|----------|----------|
| **DeNovoGear** (Ramu 2013 *Nat Methods*) | Bayesian, considers parent-of-origin | Standard for trio WES |
| **Triodenovo** (Wei 2015) | Bayesian + family-aware | Alternative |
| **GATK PossibleDeNovo annotation** | Hard filter | Quick prefilter; not standalone |
| **DeNovoCNN** (2024) | Deep learning trio caller | Most accurate as of 2024-2026 |

**False-DNV rate:** ~10-30% without manual IGV inspection; concentrated in:
- Tandem repeat regions (DNM rate inflated)
- Heterozygous parent with low coverage
- Mosaic parents (parental mosaicism transmitted to >1 offspring)
- Mapping errors in segmental duplications

## Phenotype-Driven Prioritization

| Tool | Approach | Performance (typical benchmark) | Fails when |
|------|----------|--------------------------------|-----------|
| **Exomiser** (Smedley 2015 *Nat Protoc*) | hiPHIVE: phenotype + interactome + sequence damage | 74% top-1; 94% top-5 (Cipriani 2020) | Sparse HPO (< 5 specific terms); novel-disease gene |
| **Phen2Gene** (Zhao 2020 *NARGAB*) | HPO-to-gene mapping; faster than Exomiser | Similar top-5 | Phenotype-only filtering insufficient |
| **AMELIE** (Birgmeier 2020 *Sci Transl Med*) | Literature-mining + phenotype | Best when literature is rich | New / rare disease without literature; specific patient HPO unmatched |
| **Phenolyzer** (Yang 2015 *Nat Methods*) | Phenotype-based gene scoring | Legacy | Modern multi-feature tools (Exomiser, AMELIE) preferred |
| **GADO** (Deelen 2019 *Nat Commun*) | Gene Network-based; HPO-free option | When HPO is sparse | Phenotype-rich cases where Exomiser hiPHIVE wins |
| **CADA** (Peng 2021) | Cross-species gene prioritization | Animal model integration | Genes without orthologs; rare-disease without animal model |

**Critical requirement:** all phenotype-driven tools degrade significantly with sparse HPO terms. Capture 5-10 specific HPO terms; avoid generic "intellectual disability" alone.

## ClinGen Gene-Disease Validity: Mandatory Gating

Strong et al. 2017 *AJHG* + ClinGen ongoing curation: **Limited / Moderate / Strong / Definitive** evidence per gene-disease pair.

| Category | When to apply |
|----------|---------------|
| **Definitive** | Strong literature evidence + functional / population genetic evidence | Apply full ACMG framework |
| **Strong** | -- | Apply full framework |
| **Moderate** | -- | Apply framework but flag |
| **Limited** | Single case report or weak segregation | Treat candidate cautiously; PP2 / BP1 should not apply |
| **Disputed** | Contradicting evidence | Do not call pathogenic without VCEP curation |
| **No Known Disease Relationship** | Gene not associated with the queried disease | Do not call |

**Many commercial panels include genes with only Limited validity.** ClinGen-curated `https://search.clinicalgenome.org/kb/gene-validity` is the authoritative directory.

## ACMG Secondary Findings v3.2 (Miller 2023 *Genet Med* 25:100866)

81 genes for opt-in/opt-out reporting on clinical exome/genome. Growth: 56 -> 59 -> 73 -> 78 -> 81. **v3.2 additions: CALM1, CALM2, CALM3** (calmodulinopathy; long QT / CPVT; high actionability via beta-blockade + ICD).

Inclusion criteria: ClinGen Strong or Definitive gene-disease validity + ClinGen ADWG actionability scoring.

```python
ACMG_SF_V3_2_GENES = [
    # Cardiomyopathies
    'ACTA2', 'ACTC1', 'COL3A1', 'DES', 'FBN1', 'FLNC', 'GLA', 'LMNA', 'MYBPC3',
    'MYH11', 'MYH7', 'MYL2', 'MYL3', 'PRKAG2', 'PKP2', 'RBM20', 'SCN5A', 'SMAD3',
    'TGFBR1', 'TGFBR2', 'TMEM43', 'TNNI3', 'TNNT2', 'TPM1', 'TTN',
    # CALM v3.2 additions (calmodulinopathies)
    'CALM1', 'CALM2', 'CALM3',
    # Arrhythmias and channelopathies
    'CACNA1S', 'KCNH2', 'KCNQ1', 'RYR1', 'RYR2',
    # Vascular
    'ACVRL1', 'ENG',
    # Cancer predisposition
    'APC', 'ATM', 'BAP1', 'BMPR1A', 'BRCA1', 'BRCA2', 'BRIP1', 'CDH1', 'CDKN2A',
    'CHEK2', 'GREM1', 'HOXB13', 'MAX', 'MEN1', 'MLH1', 'MSH2', 'MSH6', 'MUTYH',
    'NF2', 'PALB2', 'PMS2', 'PTEN', 'RAD51C', 'RAD51D', 'RB1', 'RET', 'SDHAF2',
    'SDHB', 'SDHC', 'SDHD', 'SMAD4', 'STK11', 'TMEM1