Variant Prioritization

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/clinical-databases/variant-prioritizationThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE),…

---
name: bio-clinical-databases-variant-prioritization
description: Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
tool_type: python
primary_tool: pandas
---

## 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** (2022) | Deep learning trio caller | Most accurate as of 2022-2026 |

**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 |
| **Phenol

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install cyvcf2 pyhgvs pandas myvariant

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Variant Prioritization

• github.com/GPTomics/bioSkills/tree/main/clinical-databases/varia — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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