kemalandClaude Opus 5.5
d91c8ea576
- simurg/pgx/star.py: solver-free diplotype enumeration incl. CN forms (deletion, xN), wildcards, array-equivalence classes, raw_score/score with CPIC frequency priors; calibrated error model (spec PGX-012 amended). - pipeline/pgx_star.py: DB gene models, SNV + PGx CN VCF evidence, masks, diplotype_summary.tsv + pgx_calls.json. - scripts/pgx: derive_masks.py (array vs 1000G 30x over HTTPS, GS calibration), getrm_truth.py, validate_star_getrm.py. - GSA v4 PGx demo vs GeT-RM (16 CEPH): 100/109 gene calls; CYP2D6/2C19/2C9 41/45 (docs/validation/V2_pgx_star.md). Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
59 lines
3.1 KiB
Markdown
59 lines
3.1 KiB
Markdown
# V2 — PGx DB and star-allele calling (PGX-001 … PGX-021)
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Status: **implemented for CPIC star-allele genes, interim validation** (2026-09-29).
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* `scripts/pgx/fetch_sources.sh` → CPIC API tables (CC0, data version 2026-08-03).
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* `simurg pgx db build` → deterministic SQLite: 22 genes, 1354 alleles, 1897 normalised variant rows; GSA v4 PGx
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probe map 5002 links; two builds byte-identical.
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* `simurg pgx star-allele call` → `diplotype_summary.tsv`, `pgx_calls.json` (11 star-allele genes).
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* Unit tests: `tests/unit/test_pgx_db.py`, `tests/unit/test_pgx_star.py`.
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## Data and truth
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* Arrays: the GSA v4 PGx demo (36 arrays = 30 CEPH/1000G individuals, `scripts/pgx/demo36_identity.tsv`), SNV VCFs
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and PGx CN VCFs from SimurgArray.
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* Star-allele truth: GeT-RM consolidated PGx/HLA table (CDC, 2025 V4) — 16 of the 30 individuals
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(`scripts/pgx/getrm_truth.py`).
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* Probe mask (PGX-010): array vs 1000G 30x (NYGC phased panel, fetched per gene over HTTPS) for the **other 14
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individuals** (`scripts/pgx/derive_masks.py`), so the mask is independent of the GeT-RM validation set: 8 of 658
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assessed records (23 probe names) below 95 % concordance — e.g. CYP2C19 rs367543002 and CYP2D6 rs1135836 called
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homozygous ALT in every sample, the UGT1A1 `C>CATAT` TA-repeat probe. Regenerate with the command in the script
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header (the mask lists vendor probe names and is not committed).
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## Result (16 individuals; replicate arrays counted once)
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| Gene | Top solution = GeT-RM | Truth among array-equivalent feasible solutions |
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|---|---|---|
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| CYP2C9 | **15/15** | 15/15 |
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| CYP2C19 | **14/15** | 15/15 |
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| CYP2D6 | **12/15** | 14/15 |
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| CYP3A5 | 15/15 | 15/15 |
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| TPMT | 15/16 | 15/16 |
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| CYP2B6 | 12/14 | 12/14 |
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| CYP4F2 | 8/9 | 8/9 |
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| NAT2 | 8/9 | 8/9 |
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| NUDT15 | 1/1 | 1/1 |
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| **total** | **100/109 (91.7 %)** | **103/109 (94.5 %)** |
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PGX-032 target (≥ 90 % for CYP2D6/CYP2C19/CYP2C9 against truth): **41/45 = 91 %** (top solution).
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Details and every discordant call: `V2_pgx_star_getrm16.json`.
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Not in the total (GeT-RM uses pre-2023 names the CPIC definitions no longer carry): UGT1A1 (GeT-RM `*60`;
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0/9 top) and SLCO1B1 (`*1B/*17/*21` vs CPIC `*37/*15/*20`; 9/15 top).
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Remaining discordances: CYP2D6 `*3/(*68)+*4` (NA12878; *68 hybrids not modelled, *3 frameshift probe), CYP2D6 *2 vs
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*1 where the array cannot separate them (prior picks *1), CYP2C19 *8, TPMT *21, CYP2B6 *27, CYP4F2 *2 — rare
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alleles whose defining variants are absent or masked on this array.
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## Error model (K-029)
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After masking, 0 of 9 740 array calls at DB variants disagreed with 1000G 30x in any GenCall-score bin (0.15–1.0),
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so the per-call error is 1e-3 (GS ≥ 0.15) and errors are handled by the probe mask. The first model,
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ε = 0.5·(1 − GS)², let correct low-GS calls lose to the prior (CYP2C9 7/15 → 15/15 after the change).
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## Caveats
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* 16 individuals, one array type, European (CEPH) only; masks from 14 individuals (spec: ≥ 100 samples).
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* Not yet: named-haplotype (G6PD), variant-based and tagging genes; CYP2D6 hybrids (*13, *36, *68); PharmVar-only
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genes (CYP2A6, CYP3A4, CYP1A2, …); phenotype annotation (PGX-025) and the JSON schema (PGX-028).
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