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).

docs/known_unknowns.md

@@ -4,6 +4,7 @@ Plan §20 lists the open questions; this file records what the implementation ha
 
 | ID | Date | Topic | Finding | Status |
 |---|---|---|---|---|
+| K-028 | 2026-09-29 | PGx CN (v2) | Deviations from PCN-009: the per-probe reference is the training cohort's median stored in the model (a calling-batch median is not CN 2 where deletions are common, e.g. GSTM1 null ≈ 50 %); no GC-correction step; the CN mixture is ours: µ_c = δ + γ·log2((c+1)/3) per region with a free lower CN-0 component and Hardy–Weinberg-constrained weights (probe compression differs by region: γ ≈ 0.19 CYP2D6.p5 … 0.56 GSTT1). Interim validation on the GSA v4 PGx demo — whose 36 arrays turned out to be 30 CEPH/1000G individuals — against NYGC 30x SV RD_CN: 224/240 region calls (GSTM1, UGT2B17 30/30), but in-sample (trained on the same arrays, < 96 samples, priors chosen with this truth in view). | Open: validate on an independent GSA-PGx cohort; CYP2D6 via GeT-RM star alleles (`docs/validation/V2_pgx_cn.md`). |
 | K-027 | 2026-09-29 | CN/LOH (v2) | CYTO-014 noise gate: the summary `logr_dev` also measures cluster-file mismatch (GSE165845: 0.246, all samples `SampleQuality`) while the LRR used after the batch reference has SD ≈ 0.1; the gate now uses the SD of the normalised LRR (the JSON still reports the summary value). Spec: built-in mask/cytobands from UCSC; the annotation bundle uses UCSC ncbiRefSeq instead of RefSeq GFF + Ensembl GTF + HGNC. Real-data result: constitutional DEL/DUP calls 74–77 % in NYGC truth, mosaic calls mostly false positives. Pi tuning: mosaic prior / switch distance barely matter; CYTO-041 (noise-normalised BAF split, complete-loss promotion) cuts PASS mosaic calls by ~30 % without losing implanted ≥ 15 % mosaics. Two limits remain: (1) without phasing, < 20 % mosaic losses are not separable from regional BAF noise; (2) common CNVs (carrier frequency > 20 %) are almost never detected (1/236 DEL) because the median-LRR batch reference absorbs them; rare DEL (< 5 %) 38 %. | Open: rare-event sensitivity as primary metric; phasing-based mosaic BAF (`docs/validation/V2_cn.md`). |
 | K-026 | 2026-09-29 | Methylation QC (v2) | Spec deviations: vendor EPIC manifests put a 5th field on some control lines (≥ 4 fields accepted); detection p follows sesame's pOOBAH (1 − max(F(M), F(U))) rather than the ambiguous "1 − ECDF(M + U)"; minfi-style `Sentrix_ID`/`Sentrix_Position` sheet keys accepted. Against sesame 1.28.1 on minfiDataEPIC: β r = 0.99998 (max \|Δβ\| 0.011), detection calls 99.1 % identical, noob signals within 0.5 % (median) / 5 % (max). The systematic remainder is unexplained; it is not the negative controls (sesame did not attach the supplied control table). | Open; `docs/validation/V2_methylation.md`. Next step: dump sesame's background vectors and noob parameters to find the difference. |
 | K-025 | 2026-09-28 | Scaling limits (P9) | With the Rust kernels a Graviton3 core genotypes 78–80 GSA samples/min. At > 8 workers a 125 MB/s gp3 volume caps the run at ~620 samples/min (GTC ≈ 13 MB each); on a RAM disk efficiency is 0.77 at 16 and 0.48 at 32 workers for 360 samples, explained by a fixed start-up cost (≈ 2.5 s + 0.2 s per worker). Linear scaling for large batches is inferred from that model, not measured. | `docs/validation/P9.md`; measure a ≥ 5000-sample batch when one is at hand. |