PGx star alleles: pgx star-allele call (PGX-011..021), probe masks vs 1000G, GeT-RM validation (K-029)

- 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).
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# 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 UGT1A1 `C>CATAT` TA-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).
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{
"individuals": 30,
"genes": {
"CYP2B6": {
"n": 14,
"top": 12,
"equivalent": 12,
"not_comparable": 1
},
"CYP2C19": {
"n": 15,
"top": 14,
"equivalent": 15
},
"CYP2C9": {
"n": 15,
"top": 15,
"equivalent": 15
},
"CYP2D6": {
"n": 15,
"top": 12,
"equivalent": 14
},
"CYP3A5": {
"n": 15,
"top": 15,
"equivalent": 15
},
"CYP4F2": {
"n": 9,
"top": 8,
"equivalent": 8
},
"NAT2": {
"n": 9,
"top": 8,
"equivalent": 8
},
"NUDT15": {
"n": 1,
"top": 1,
"equivalent": 1
},
"SLCO1B1": {
"n": 15,
"top": 9,
"equivalent": 9
},
"TPMT": {
"n": 16,
"top": 15,
"equivalent": 15
},
"UGT1A1": {
"n": 9,
"top": 0,
"equivalent": 1
}
},
"legacy_nomenclature_genes": [
"SLCO1B1",
"UGT1A1"
],
"total": {
"n": 109,
"top": 100,
"equivalent": 103
},
"discordant": [
{
"individual": "NA12156",
"gene": "CYP4F2",
"truth": "(*2)/*3",
"call": "*1/*3",
"within_equivalents": false
},
{
"individual": "NA12156",
"gene": "SLCO1B1",
"truth": "*1/*21",
"call": "*1/*37",
"within_equivalents": false
},
{
"individual": "NA12156",
"gene": "UGT1A1",
"truth": "*60/*28",
"call": "*28/*80",
"within_equivalents": false
},
{
"individual": "NA12044",
"gene": "TPMT",
"truth": "*1/*21",
"call": "*1/*1",
"within_equivalents": false
},
{
"individual": "NA07019",
"gene": "UGT1A1",
"truth": "*1/*60",
"call": "*1/*1",
"within_equivalents": false
},
{
"individual": "NA06991",
"gene": "UGT1A1",
"truth": "*60/*60",
"call": "*1/*1",
"within_equivalents": false
},
{
"individual": "NA12878",
"gene": "CYP2D6",
"truth": "*3/(*68)+*4",
"call": "*4x2/*1;*4x2/*82",
"within_equivalents": false
},
{
"individual": "NA12878",
"gene": "UGT1A1",
"truth": "*60/*28",
"call": "*28/*80",
"within_equivalents": false
},
{
"individual": "NA11993",
"gene": "SLCO1B1",
"truth": "*1/*17",
"call": "*1/*15",
"within_equivalents": false
},
{
"individual": "NA11993",
"gene": "UGT1A1",
"truth": "(*28 + *60)/*60",
"call": "*28/*80",
"within_equivalents": false
},
{
"individual": "NA12003",
"gene": "UGT1A1",
"truth": "*60/*28",
"call": "*28/*80",
"within_equivalents": false
},
{
"individual": "NA07029",
"gene": "CYP2B6",
"truth": "*6/(*27)",
"call": "*1/*6",
"within_equivalents": false
},
{
"individual": "NA07029",
"gene": "CYP2C19",
"truth": "*8/*17",
"call": "*1/*17",
"within_equivalents": true
},
{
"individual": "NA07029",
"gene": "UGT1A1",
"truth": "*1/*1",
"call": "*1/*28",
"within_equivalents": true
},
{
"individual": "NA12892",
"gene": "CYP2B6",
"truth": "*1/*1",
"call": "*1/*5",
"within_equivalents": false
},
{
"individual": "NA11832",
"gene": "NAT2",
"truth": "*5/(*12)",
"call": "*1/*5",
"within_equivalents": false
},
{
"individual": "NA11832",
"gene": "SLCO1B1",
"truth": "*1/*1",
"call": "*14/*20",
"within_equivalents": false
},
{
"individual": "NA11832",
"gene": "UGT1A1",
"truth": "(*28 + *60)/*60",
"call": "*28/*80",
"within_equivalents": false
},
{
"individual": "NA10838",
"gene": "CYP2D6",
"truth": "*2/*4",
"call": "*1/*4;*2/*4",
"within_equivalents": true
},
{
"individual": "NA10838",
"gene": "SLCO1B1",
"truth": "*1/*1",
"call": "*1/*14",
"within_equivalents": false
},
{
"individual": "NA12813",
"gene": "CYP2D6",
"truth": "*2/*4",
"call": "*1/*4;*2/*4",
"within_equivalents": true
},
{
"individual": "NA12813",
"gene": "SLCO1B1",
"truth": "*1/*21",
"call": "*1/*20",
"within_equivalents": false
},
{
"individual": "NA12813",
"gene": "UGT1A1",
"truth": "(*28 + *60)/(*28 + *60)",
"call": "*80/*80",
"within_equivalents": false
},
{
"individual": "NA11881",
"gene": "SLCO1B1",
"truth": "*1/(*15)",
"call": "*14/*15",
"within_equivalents": false
}
]
}