PGx CN: pgx copy-number train|call (CLI-031/032, PCN-001..017, IN-050), region BED v1, demo identities (K-028)
- simurg/pgx/cn.py: per-region CN mixture, mu_c = delta + gamma*log2((c+1)/3), free lower CN-0 component, Hardy-Weinberg-constrained weights over haplotype copy numbers, EM from a grid of label starts. - pipeline/pgx_cn.py: model file (.simurg-pgxcn), batch rules (PCN-004), per-sample CN VCF + tbi, batch and sample summaries. - The GSA v4 PGx demo arrays are 30 CEPH/1000G individuals (scripts/pgx/demo36_identity.tsv); against NYGC 30x SV RD_CN 224/240 region calls agree (GSTM1, UGT2B17 30/30), in-sample (docs/validation/V2_pgx_cn.md).
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#!/usr/bin/env python3
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"""Build the PGx CN target-region BED (spec PCN-001) from UCSC ncbiRefSeq (GRCh38).
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python scripts/pgx/build_cn_regions.py ncbiRefSeq.hg38.txt.gz OUT.bed [--version N]
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Regions (0-based half-open BED: chrom start0 end region gene), from the MANE/first-listed transcript:
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CYP2D6.p5 2 kb 5' flank of CYP2D6 (upstream of exon 1; the gene is on the minus strand)
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CYP2D6.intron.2 CYP2D6 intron 2
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CYP2D6.exon.9 CYP2D6 exon 9
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CYP2A6.exon.1 CYP2A6 exon 1 (± 500 bp)
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CYP2A6.intron.7 CYP2A6 intron 7
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GSTM1, GSTT1 (on chr22_KI270879v1_alt, IN-062), UGT2B17, SULT1A1, CYP2E1: whole gene.
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Exons and introns are numbered in transcript order (strand-aware). [DB: NCBI RefSeq via UCSC ncbiRefSeq]
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"""
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from __future__ import annotations
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import argparse
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import gzip
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from pathlib import Path
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TRANSCRIPT = {"CYP2D6": "NM_000106", "CYP2A6": "NM_000762", "GSTM1": "NM_000561", "GSTT1": "NM_000853",
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"UGT2B17": "NM_001077", "SULT1A1": "NM_001055", "CYP2E1": "NM_000773"} # fmt: skip
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FLANK = 2000
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PAD = 500
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def main() -> int:
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ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("refseq", type=Path)
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ap.add_argument("out", type=Path)
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ap.add_argument("--version", type=int, default=1)
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a = ap.parse_args()
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tx: dict[str, tuple[str, str, list[int], list[int]]] = {}
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with gzip.open(a.refseq, "rt") as fh:
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for line in fh:
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f = line.rstrip("\n").split("\t")
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name, chrom, strand, gene = f[1], f[2], f[3], f[12]
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if gene not in TRANSCRIPT or not name.startswith(TRANSCRIPT[gene] + "."):
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continue
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if "_" in chrom and not (gene == "GSTT1" and chrom == "chr22_KI270879v1_alt"):
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continue # primary assembly, except GSTT1 which is only on the alt contig
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starts = [int(x) for x in f[9].rstrip(",").split(",")]
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ends = [int(x) for x in f[10].rstrip(",").split(",")]
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tx.setdefault(gene, (chrom, strand, starts, ends))
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missing = set(TRANSCRIPT) - set(tx)
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if missing:
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raise SystemExit(f"transcripts not found: {sorted(missing)}")
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rows: list[tuple[str, int, int, str, str]] = []
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def exon(gene: str, k: int) -> tuple[int, int]:
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chrom, strand, s, e = tx[gene]
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i = k - 1 if strand == "+" else len(s) - k
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return s[i], e[i]
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def intron(gene: str, k: int) -> tuple[int, int]:
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chrom, strand, s, e = tx[gene]
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a_, b_ = exon(gene, k), exon(gene, k + 1)
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return (a_[1], b_[0]) if strand == "+" else (b_[1], a_[0])
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for gene in ("CYP2D6", "CYP2A6"):
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chrom, strand, s, e = tx[gene]
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if gene == "CYP2D6":
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p5 = (e[-1], e[-1] + FLANK) if strand == "-" else (s[0] - FLANK, s[0])
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rows += [(chrom, *p5, "CYP2D6.p5", gene), (chrom, *intron(gene, 2), "CYP2D6.intron.2", gene),
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(chrom, *exon(gene, 9), "CYP2D6.exon.9", gene)] # fmt: skip
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else:
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x1 = exon(gene, 1)
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rows += [(chrom, x1[0] - PAD, x1[1] + PAD, "CYP2A6.exon.1", gene),
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(chrom, *intron(gene, 7), "CYP2A6.intron.7", gene)] # fmt: skip
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for gene in ("GSTM1", "GSTT1", "UGT2B17", "SULT1A1", "CYP2E1"):
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chrom, strand, s, e = tx[gene]
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rows.append((chrom, s[0], e[-1], gene, gene))
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order = {f"chr{i}": i for i in range(1, 23)}
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rows.sort(key=lambda r: (order.get(r[0].split("_")[0], 99), r[0], r[1]))
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with a.out.open("w") as fh:
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fh.write(f"# simurg PGx CN regions v{a.version}, GRCh38, from UCSC ncbiRefSeq (PCN-001)\n")
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for r in rows:
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fh.write("\t".join(map(str, r)) + "\n")
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print(a.out.read_text())
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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# GSA v4 PGx demo (idats_Demo_36) sample identities: best match among the 3202 1000 Genomes 30x individuals
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# by genotype concordance on 4647 chr22 SNPs with 1000G AF 0.05-0.95 (next best <= 0.91: relatives)
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sample_id individual concordance second_best second_concordance
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207895980019_R03C01 NA12875 0.9920 NA12865 0.7038
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207895980019_R03C02 NA12156 0.9933 NA10831 0.7021
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207895980019_R04C01 NA12156 0.9931 NA10831 0.7027
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207895980019_R05C01 NA12044 0.9931 NA10857 0.6969
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207895980019_R06C01 NA11992 0.9931 NA10860 0.6848
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207895980019_R07C01 NA06993 0.9940 NA06991 0.7209
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207895980019_R07C03 NA07019 0.9953 NA07056 0.7051
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207895980019_R08C01 NA12239 0.9938 NA10847 0.7022
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207895980019_R09C01 NA12716 0.9944 NA12707 0.6896
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207895980019_R09C03 NA06991 0.9931 NA06993 0.7215
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207895980019_R10C01 NA12878 0.9933 NA12892 0.7022
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207895980019_R11C01 NA12760 0.9948 NA12752 0.7171
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207895980019_R11C02 NA06985 0.9948 NA06991 0.6996
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207895980019_R12C01 NA11993 0.9961 NA10860 0.7168
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207895980019_R12C02 NA12003 0.9935 NA10838 0.6998
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207895980019_R13C03 NA06994 0.9948 NA07029 0.7114
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207895980019_R14C03 NA11993 0.9959 NA10860 0.7163
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207895980019_R15C02 NA12003 0.9931 NA10838 0.6993
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207895980035_R03C03 NA07029 0.9952 NA07000 0.7237
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207895980035_R04C03 NA12892 0.9957 NA12878 0.7005
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207895980035_R05C03 NA12248 0.9950 NA10835 0.7143
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207895980035_R06C03 NA10846 0.9920 NA12145 0.7056
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207895980035_R07C01 NA07000 0.9957 NA07029 0.7231
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207895980035_R07C03 NA12155 0.9935 NA10831 0.6903
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207895980035_R08C01 NA11832 0.9942 NA10855 0.7252
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207895980035_R08C03 NA06993 0.9940 NA06991 0.7205
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207895980035_R09C03 NA10838 0.9948 NA12004 0.7007
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207895980035_R10C01 NA12813 0.9937 NA07045 0.9103
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207895980035_R10C03 NA12249 0.9938 NA10835 0.6858
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207895980035_R11C03 NA12812 0.9942 NA12801 0.7061
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207895980035_R12C03 NA11881 0.9935 NA10859 0.7138
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207895980035_R13C01 NA11995 0.9953 NA10861 0.6857
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207895980035_R13C03 NA12248 0.9950 NA10835 0.7145
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207895980035_R14C01 NA12891 0.9942 NA12878 0.6998
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207895980035_R15C03 NA12043 0.9946 NA10857 0.7162
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207895980042_R15C02 NA11832 0.9942 NA10855 0.7253
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#!/usr/bin/env python3
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"""Validate `simurg pgx copy-number call` against the NYGC 30x SV callset (1KGP_3202 freeze V3) — PCN-017.
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python scripts/pgx/validate_cn_truth.py --calls CALL_DIR --identity scripts/pgx/demo36_identity.tsv \
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--sv 1KGP_3202.sv_freeze_V3.vcf.gz --bcftools BCFTOOLS --out summary.json
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Truth per region = RD_CN of one SV record per region (below, chosen as the record spanning the whole region; for
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GSTM1 the read-depth DUP/CNV record, whose RD_CN gives 0/1/2 in Hardy–Weinberg proportions — the DEL record there
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reports CN 1 for 27/30 individuals). CYP2D6 sub-regions share one gene-level record, so exon-9 conversions (*36) and
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2D7-2D6 hybrids (*13, *68) legitimately differ from it. GSTT1 lies on an alt contig with no SV calls: only its CN
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distribution is reported. Replicate arrays of one individual are counted once (the first array).
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"""
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from __future__ import annotations
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import argparse
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import collections
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import csv
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import json
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import subprocess
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from pathlib import Path
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TRUTH_RECORD = {"GSTM1": "HGSV_9691", "UGT2B17": "HGSV_63272", "SULT1A1": "HGSV_207805",
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"CYP2A6.intron.7": "HGSV_232737", "CYP2A6.exon.1": "HGSV_232737", "CYP2D6.exon.9": "HGSV_247317",
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"CYP2D6.intron.2": "HGSV_247317", "CYP2D6.p5": "HGSV_247317"} # fmt: skip
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def main() -> int:
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ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--calls", required=True, type=Path)
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ap.add_argument("--identity", required=True, type=Path)
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ap.add_argument("--sv", required=True)
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ap.add_argument("--bcftools", default="bcftools")
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ap.add_argument("--out", required=True, type=Path)
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a = ap.parse_args()
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ident = {}
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with a.identity.open() as fh:
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for r in csv.DictReader((ln for ln in fh if not ln.startswith("#")), delimiter="\t"):
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ident[r["sample_id"]] = r["individual"]
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calls: dict[str, dict[str, str]] = {}
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with (a.calls / "pgx_cn_sample_summary.tsv").open() as fh:
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for r in csv.DictReader(fh, delimiter="\t"):
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ind = ident.get(r["sample_id"])
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if ind and ind not in calls:
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calls[ind] = {k[3:]: v for k, v in r.items() if k.startswith("cn_")}
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ids = sorted(set(TRUTH_RECORD.values()))
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q = subprocess.run([a.bcftools, "query", "-H", "-s", ",".join(sorted(calls)), "--force-samples", "-i",
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"ID=@/dev/stdin", "-f", "%ID[\t%RD_CN]\n", a.sv], input="\n".join(ids), capture_output=True,
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text=True, check=True).stdout.splitlines() # fmt: skip
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samples = [h.split("]")[1].split(":")[0] for h in q[0].split("\t")[1:]]
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truth = {ln.split("\t")[0]: dict(zip(samples, ln.split("\t")[1:], strict=True)) for ln in q[1:]}
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res: dict[str, dict[str, object]] = {}
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tot = [0, 0]
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for region, rid in TRUTH_RECORD.items():
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pairs = [(truth[rid][ind], c[region]) for ind, c in calls.items() if ind in truth.get(rid, {})]
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pairs = [(t, c) for t, c in pairs if t not in (".", "")]
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ok = sum(t == c for t, c in pairs)
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tot[0] += ok
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tot[1] += len(pairs)
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res[region] = {"record": rid, "concordant": ok, "n": len(pairs),
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"rate": round(ok / len(pairs), 4) if pairs else None,
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"truth_to_call_mismatches": dict(collections.Counter(f"{t}->{c}" for t, c in pairs if t != c))} # fmt: skip
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summary = {"individuals": len(calls), "regions": res, "overall": {"concordant": tot[0], "n": tot[1],
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"rate": round(tot[0] / tot[1], 4) if tot[1] else None},
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"gstt1_cn_distribution": dict(sorted(collections.Counter(c.get("GSTT1", ".") for c in calls.values()).items()))} # fmt: skip
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a.out.write_text(json.dumps(summary, indent=1))
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print(json.dumps(summary, indent=1))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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