bioresearch-causal-evidencelisted

# BioResearch Agent — Causal Evidence Chain Skill ## Capability Runs the full causal-evidence chain for a set of loci: 1. **GWAS → eQTL** — per-SNP association in both trait and expression. 2. **Colocalization** — full 5-hypothesis coloc (Giambartolomei 2014), reporting `PP.H4` (shared causal variant) vs `PP.H3` (distinct variants). Numerically stable (log-space ABF). 3. **TWAS** — S-PrediXcan-style expression-trait association (Z, p). 4. **Fine-mapping** — Bayesian credible set via per-SNP posterior inclusion probability (PIP), normalized over the locus. 5. **MR** — Wald-ratio causal estimate for the colocalized gene's lead SNP. Returns a per-gene evidence table + credible-set CSV + locus heatmap, not a biological claim. ## Run ```bash bioresearch run causal-evidence --seed 42 --output-dir outputs/causal-evidence ``` ## Outputs (in `--output-dir`) - `CE_per_gene_results.csv` — per-gene: truth class, `PP.H4`, TWAS Z/p, MR β/p, n credible - `CE_credible_sets.csv` — per-SNP PIP + credible-set membership - `CE_locus_heatmap.png` — GWAS / eQTL / coloc association heatmap - `CE_recovery_benchmark.csv` — ground-truth recovery across effect sizes - `CE_summary_report.txt` — human-readable summary - `CE_evidence_package.json` — reproducible Evidence Package (provenance + benchmark + grade) ## Note This skill dispatches to the framework's `causal-evidence` workflow / `demo_causal_evidence.py`. It adds **no analysis of its own**; all statistics run in the workflow modul