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
3.1 KiB
V2 — PGx DB and star-allele calling (PGX-001 … PGX-021)
Status: implemented for CPIC star-allele genes, interim validation (2026-09-29).
scripts/pgx/fetch_sources.sh→ CPIC API tables (CC0, data version 2026-08-03).simurg pgx db build→ deterministic SQLite: 22 genes, 1354 alleles, 1897 normalised variant rows; GSA v4 PGx probe map 5002 links; two builds byte-identical.simurg pgx star-allele call→diplotype_summary.tsv,pgx_calls.json(11 star-allele genes).- Unit tests:
tests/unit/test_pgx_db.py,tests/unit/test_pgx_star.py.
Data and truth
- Arrays: the GSA v4 PGx demo (36 arrays = 30 CEPH/1000G individuals,
scripts/pgx/demo36_identity.tsv), SNV VCFs and PGx CN VCFs from SimurgArray. - Star-allele truth: GeT-RM consolidated PGx/HLA table (CDC, 2025 V4) — 16 of the 30 individuals
(
scripts/pgx/getrm_truth.py). - Probe mask (PGX-010): array vs 1000G 30x (NYGC phased panel, fetched per gene over HTTPS) for the other 14
individuals (
scripts/pgx/derive_masks.py), so the mask is independent of the GeT-RM validation set: 8 of 658 assessed records (23 probe names) below 95 % concordance — e.g. CYP2C19 rs367543002 and CYP2D6 rs1135836 called homozygous ALT in every sample, the UGT1A1C>CATATTA-repeat probe. Regenerate with the command in the script header (the mask lists vendor probe names and is not committed).
Result (16 individuals; replicate arrays counted once)
| Gene | Top solution = GeT-RM | Truth among array-equivalent feasible solutions |
|---|---|---|
| CYP2C9 | 15/15 | 15/15 |
| CYP2C19 | 14/15 | 15/15 |
| CYP2D6 | 12/15 | 14/15 |
| CYP3A5 | 15/15 | 15/15 |
| TPMT | 15/16 | 15/16 |
| CYP2B6 | 12/14 | 12/14 |
| CYP4F2 | 8/9 | 8/9 |
| NAT2 | 8/9 | 8/9 |
| NUDT15 | 1/1 | 1/1 |
| total | 100/109 (91.7 %) | 103/109 (94.5 %) |
PGX-032 target (≥ 90 % for CYP2D6/CYP2C19/CYP2C9 against truth): 41/45 = 91 % (top solution).
Details and every discordant call: V2_pgx_star_getrm16.json.
Not in the total (GeT-RM uses pre-2023 names the CPIC definitions no longer carry): UGT1A1 (GeT-RM *60;
0/9 top) and SLCO1B1 (*1B/*17/*21 vs CPIC *37/*15/*20; 9/15 top).
Remaining discordances: CYP2D6 *3/(*68)+*4 (NA12878; *68 hybrids not modelled, *3 frameshift probe), CYP2D6 *2 vs
*1 where the array cannot separate them (prior picks *1), CYP2C19 *8, TPMT *21, CYP2B6 *27, CYP4F2 *2 — rare
alleles whose defining variants are absent or masked on this array.
Error model (K-029)
After masking, 0 of 9 740 array calls at DB variants disagreed with 1000G 30x in any GenCall-score bin (0.15–1.0), so the per-call error is 1e-3 (GS ≥ 0.15) and errors are handled by the probe mask. The first model, ε = 0.5·(1 − GS)², let correct low-GS calls lose to the prior (CYP2C9 7/15 → 15/15 after the change).
Caveats
- 16 individuals, one array type, European (CEPH) only; masks from 14 individuals (spec: ≥ 100 samples).
- Not yet: named-haplotype (G6PD), variant-based and tagging genes; CYP2D6 hybrids (*13, *36, *68); PharmVar-only genes (CYP2A6, CYP3A4, CYP1A2, …); phenotype annotation (PGX-025) and the JSON schema (PGX-028).