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). Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv
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"""``simurg pgx star-allele call`` — star alleles from SNV VCFs, PGx DB and PGx CN VCFs (spec PGX-011 … PGX-021,
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CLI-033). I/O and orchestration; the scoring lives in ``simurg.pgx.star``.
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Scope of this version: genes whose result basis is ``star_allele`` in the DB. Named-haplotype (G6PD), variant-based
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and tagging genes, sub-allele collapsing and hybrid CYP2D6 structures follow (K-029).
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"""
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from __future__ import annotations
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import gzip
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import json
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import math
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import sqlite3
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from collections import defaultdict
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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import numpy as np
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from simurg import __version__
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from simurg.core.errors import ExitCode, SimurgError
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from simurg.core.log import Reporter
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from simurg.pgx.star import CALLED, MASKED, NO_CALL, NOT_COVERED, GeneModel, Observations, Solution, call_gene
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CN_REGION = {"CYP2D6": "CYP2D6.p5"} # PGX-013: total gene copies from the 5′ region
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DELETION_ALLELE = {"CYP2D6": "*5"}
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@dataclass
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class StarOptions:
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vcfs: list[Path]
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db: Path
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out: Path
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population: str = "global"
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max_candidates: int = 10
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feasible_ratio: float = 0.5
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low_gs: float = 0.15
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no_cn: bool = False
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allow_foreign_vcf: bool = False
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genes: list[str] = field(default_factory=list)
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force: bool = False
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def load_models(db: Path, population: str, genes: list[str] | None = None) -> dict[str, GeneModel]:
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con = sqlite3.connect(db)
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q = "SELECT gene FROM genes WHERE result_basis = 'star_allele' ORDER BY gene"
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out = {}
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for (gene,) in con.execute(q).fetchall():
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if genes and gene not in genes:
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continue
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alle = con.execute("SELECT allele, kind FROM alleles WHERE gene = ? AND defined = 1 AND structural = 0 "
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"ORDER BY allele", (gene,)).fetchall() # fmt: skip
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names = [a for a, _ in alle]
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ref = next((a for a, k in alle if k == "reference"), names[0] if names else "")
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rows = con.execute("SELECT allele, chrom, pos, ref, alt, hgvs, rsid, location_id FROM allele_variants "
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"WHERE gene = ?", (gene,)).fetchall() # fmt: skip
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keys = sorted({(r[1], r[2], r[3], r[4]) for r in rows}, key=lambda k: (k[0], k[1], k[2], k[3]))
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kidx = {k: i for i, k in enumerate(keys)}
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ids: list[tuple[str, str | None]] = [("", None)] * len(keys)
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loc_keys: dict[int, list[int]] = defaultdict(list)
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carries = np.zeros((len(names), len(keys)), dtype=np.int8)
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hidx = {n: i for i, n in enumerate(names)}
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for allele, chrom, pos, r_, a_, hgvs, rsid, lid in rows:
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k = kidx[(chrom, pos, r_, a_)]
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ids[k] = (hgvs, rsid)
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loc_keys[lid].append(k)
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if allele in hidx:
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carries[hidx[allele], k] = 1
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for allele, lid in con.execute("SELECT allele, location_id FROM allele_wildcards WHERE gene = ?", (gene,)):
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if allele in hidx:
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for k in loc_keys.get(lid, []):
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carries[hidx[allele], k] = -1
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freq = np.full(len(names), np.nan)
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fr: dict[str, list[float]] = defaultdict(list)
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for allele, pop, f in con.execute("SELECT allele, population, freq FROM frequencies WHERE gene = ?", (gene,)):
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if population in ("global", pop):
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fr[allele].append(float(f))
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for i, n in enumerate(names):
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if fr.get(n):
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freq[i] = float(np.mean(fr[n]))
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freq = np.nan_to_num(freq, nan=0.0)
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out[gene] = GeneModel(gene, keys, ids, names, carries, freq, DELETION_ALLELE.get(gene), ref)
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con.close()
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return out
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def read_vcf_sites(
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path: Path, wanted: dict[str, set[int]]
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) -> tuple[dict[tuple[str, int], list[dict[str, Any]]], dict[str, str]]:
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"""Records at the wanted positions, and the ``##`` header meta (``##key=value``)."""
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recs: dict[tuple[str, int], list[dict[str, Any]]] = defaultdict(list)
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meta: dict[str, str] = {}
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with gzip.open(path, "rt") as fh:
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for ln in fh:
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if ln.startswith("##"):
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k, _, v = ln[2:].rstrip("\n").partition("=")
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meta.setdefault(k, v)
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continue
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if ln.startswith("#"):
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continue
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c, p, _ = ln.split("\t", 2)
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ps = wanted.get(c)
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if ps is None or int(p) not in ps:
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continue
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f = ln.rstrip("\n").split("\t")
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fmt = f[8].split(":")
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val = dict(zip(fmt, f[9].split(":"), strict=False))
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recs[(c, int(p))].append({"id": f[2], "ref": f[3], "alts": f[4].split(","), "gt": val.get("GT", "./."),
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"gs": val.get("GS"), "baf": val.get("BAF")}) # fmt: skip
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return recs, meta
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def observations(
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m: GeneModel, recs: dict[tuple[str, int], list[dict[str, Any]]], masked: set[str] | None = None
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) -> tuple[Observations, int]:
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"""PGX-012 evidence per defining variant; returns the observations and the ploidy seen in the GTs. A record with
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any masked probe (PGX-010) makes the variant ``masked``."""
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v = len(m.variants)
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state = np.full(v, NOT_COVERED, dtype=np.int8)
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dosage = np.full(v, -1, dtype=np.int8)
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gs = np.full(v, np.nan)
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baf = np.full(v, np.nan)
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ploidy = 2
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for k, (c, p, r_, a_) in enumerate(m.variants):
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for rec in recs.get((c, p), []):
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if rec["ref"] != r_ or a_ not in rec["alts"]:
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continue
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ai = rec["alts"].index(a_) + 1
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if masked and any(pn in masked for pn in rec["id"].replace(";", ",").split(",")):
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state[k] = MASKED
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break
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alleles = rec["gt"].replace("|", "/").split("/")
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ploidy = len(alleles)
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if any(a in (".", "") for a in alleles):
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state[k] = NO_CALL
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break
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state[k] = CALLED
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dosage[k] = sum(int(a) == ai for a in alleles)
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gs[k] = float(rec["gs"]) if rec["gs"] not in (None, ".") else math.nan
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baf[k] = float(rec["baf"]) if rec["baf"] not in (None, ".") else math.nan
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if len(rec["alts"]) > 1 and not math.isnan(baf[k]):
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baf[k] = math.nan # BAF of a merged multi-allelic record is not per-ALT
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break
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return Observations(state, dosage, gs, baf), ploidy
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def read_cn_vcf(path: Path) -> dict[str, int | None]:
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out: dict[str, int | None] = {}
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with gzip.open(path, "rt") as fh:
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for ln in fh:
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if ln.startswith("#"):
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continue
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f = ln.rstrip("\n").split("\t")
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region = f[7].split("REGION=")[1].split(";")[0]
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cn = f[9].split(":")[0]
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out[region] = None if cn == "." or "NoCall" in f[6] else int(cn)
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return out
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def _discover(dirs: list[Path]) -> tuple[dict[str, Path], dict[str, Path]]:
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snv, cn = {}, {}
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for d in dirs:
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for p in sorted(d.rglob("*.vcf.gz")):
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if p.name.endswith(".pgx_cn.vcf.gz"):
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cn[p.name[: -len(".pgx_cn.vcf.gz")]] = p
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elif p.name.endswith(".snv.vcf.gz"):
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snv[p.name[: -len(".snv.vcf.gz")]] = p
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return snv, cn
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def run_star_call(o: StarOptions, rep: Reporter) -> int:
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"""``simurg pgx star-allele call`` (CLI-033)."""
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from simurg.core.atomic import check_outputs_absent
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from simurg.pipeline.qc import write_tsv
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snv, cnv = _discover(o.vcfs)
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if not snv:
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raise SimurgError("E_NO_INPUT", f"no *.snv.vcf.gz under {', '.join(map(str, o.vcfs))}")
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missing_cn = sorted(set(snv) - set(cnv))
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if missing_cn and not o.no_cn:
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raise SimurgError("E_PGX_CN_MISSING", f"{len(missing_cn)} sample(s) have no .pgx_cn.vcf.gz (e.g. "
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f"{missing_cn[0]})", hint="run pgx copy-number call, or give --no-cn") # fmt: skip
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for vp in snv.values():
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if not Path(str(vp) + ".tbi").exists() and not Path(str(vp) + ".csi").exists():
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raise SimurgError("E_PGX_VCF_UNINDEXED", f"{vp} is not indexed")
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check_outputs_absent([o.out / "diplotype_summary.tsv"], force=o.force)
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models = load_models(o.db, o.population, o.genes or None)
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mcon = sqlite3.connect(o.db)
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masks: dict[str, set[str]] = defaultdict(set)
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for man, probe in mcon.execute("SELECT manifest, probe_name FROM masks"):
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masks[man].add(probe)
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mcon.close()
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wanted: dict[str, set[int]] = defaultdict(set)
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for m in models.values():
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for c, p, _, _ in m.variants:
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wanted[c].add(p)
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rows = []
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calls_json = []
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for sid, path in sorted(snv.items()):
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recs, meta = read_vcf_sites(path, wanted)
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opts = meta.get("simurg_options", "")
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if not opts and not o.allow_foreign_vcf:
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raise SimurgError("E_PGX_VCF_UNSUITABLE", f"{path}: not a SimurgArray VCF (no ##simurg_options)",
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hint="give --allow-foreign-vcf to use it anyway") # fmt: skip
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kv = dict(x.partition("=")[::2] for x in opts.split(";") if x)
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if (
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kv.get("unsquash_duplicates") == "true"
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or kv.get("exclude_loci", "none").lower() != "none"
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or kv.get("include_loci", "none").lower() != "none"
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): # PGX-011
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raise SimurgError("E_PGX_VCF_UNSUITABLE", f"{path}: made with unsquashed duplicates or a locus filter")
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cn = {} if o.no_cn or sid not in cnv else read_cn_vcf(cnv[sid])
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masked = masks.get(meta.get("simurg_manifest", "").split(";")[0], set())
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sample_name = sid
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genes_out = []
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for gene, m in sorted(models.items()):
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obs, ploidy = observations(m, recs, masked)
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region = CN_REGION.get(gene)
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gcn = cn.get(region) if region else None
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sols, feasible = call_gene(m, obs, cn=gcn if region else None, ploidy=ploidy,
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max_candidates=o.max_candidates, feasible_ratio=o.feasible_ratio) # fmt: skip
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flags = []
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low = [k for k in range(len(m.variants)) if obs.state[k] == CALLED and obs.gs[k] < o.low_gs]
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if low:
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flags.append("review_alternatives")
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no_calls = [k for k in range(len(m.variants)) if obs.state[k] == NO_CALL]
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genotype = ";".join(s.genotype for s in feasible)
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for rank, s in enumerate(sols, start=1):
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rows.append({"sample_id": sid, "sample_name": sample_name, "solution_id": rank, "gene": gene,
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"call_type": "star_allele", "solution": s.genotype,
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"array_equivalent_alleles_1": ";".join(sorted({mm[0] for mm in s.members})),
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"array_equivalent_alleles_2": ";".join(sorted({mm[-1] for mm in s.members})),
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"no_call_variants": ";".join(m.variant_ids[k][0] for k in no_calls),
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"alleles_impacted_by_no_call": "", "background_alleles": "", "variant_change": "",
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"score": f"{s.score:.4f}", "raw_score": f"{s.raw_score:.4f}",
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"copy_number_solution": f"{region}={gcn}" if region and gcn is not None else "",
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"flags": ";".join(flags + (["feasible"] if s in feasible else []))}) # fmt: skip
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genes_out.append({"gene": gene, "genotype": genotype, "feasible_solutions": [s.genotype for s in feasible],
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"candidates": [_sol_json(s) for s in sols], "copy_number": gcn,
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"flags": flags}) # fmt: skip
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calls_json.append({"sample_id": sid, "genes": genes_out})
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cols = ["sample_id", "sample_name", "solution_id", "gene", "call_type", "solution", "array_equivalent_alleles_1",
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"array_equivalent_alleles_2", "no_call_variants", "alleles_impacted_by_no_call", "background_alleles",
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"variant_change", "score", "raw_score", "copy_number_solution", "flags"] # fmt: skip
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o.out.mkdir(parents=True, exist_ok=True)
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write_tsv(o.out / "diplotype_summary.tsv", cols, rows)
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(o.out / "pgx_calls.json").write_text(json.dumps({"software": f"SimurgArray {__version__}", "db": o.db.name,
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"samples": calls_json}, indent=1)) # fmt: skip
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rep.info(f"star alleles: {len(calls_json)} sample(s), {len(models)} gene(s)")
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return ExitCode.OK
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def _sol_json(s: Solution) -> dict[str, Any]:
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return {"genotype": s.genotype, "score": round(s.score, 4), "raw_score": round(s.raw_score, 4),
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"equivalents": ["/".join(m) for m in s.members][:20], "copy_number": s.copy_number} # fmt: skip
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