How do I install the Bioresearch Causal Evidence skill?

Run npx skillmds@latest add alim430/bioresearch-causal-evidence in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Bioresearch Causal Evidence free to use?

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Who published Bioresearch Causal Evidence?

Alim430 (@alim430) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bioresearch Causal Evidence by alim430

Name: Bioresearch Causal Evidence
Author: Alim430

Bioresearch Causal Evidence

Run the causal-evidence chain (GWAS → eQTL → colocalization → TWAS → fine-mapping → MR) via the BioResearch Agent causal-evidence workflow. Use when the user wants to test whether a GWAS risk locus acts through a candidate gene's expression, identify the credible set, and check MR consistency. Synthetic loci by default; methodology validation, not a real etiological claim.

Alim430 Updated Sep 17, 2026

File contents

BioResearch Agent — Causal Evidence Chain Skill

Capability

Runs the full causal-evidence chain for a set of loci:

GWAS → eQTL — per-SNP association in both trait and expression.
Colocalization — full 5-hypothesis coloc (Giambartolomei 2014), reporting PP.H4 (shared causal variant) vs PP.H3 (distinct variants). Numerically stable (log-space ABF).
TWAS — S-PrediXcan-style expression-trait association (Z, p).
Fine-mapping — Bayesian credible set via per-SNP posterior inclusion probability (PIP), normalized over the locus.
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

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 modules. By default uses synthetic loci with ground-truth labels to validate the engine — real-data AD reference loci (TREM2 / BIN1 / APOE / CLU / PICALM) are listed as the target for a real-data version. Evidence grade is C (methodology validation).

---name: bioresearch-causal-evidencedescription: Run the causal-evidence chain (GWAS → eQTL → colocalization → TWAS → fine-mapping → MR) via the BioResearch Agent causal-evidence workflow. Use when the user wants to test whether a GWAS risk locus acts through a candidate gene's expression, identify the credible set, and check MR consistency. Synthetic loci by default; methodology validation, not a real etiological claim.---# BioResearch Agent — Causal Evidence Chain Skill## CapabilityRuns 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```bashbioresearch 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)## NoteThis 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 modules. By default uses**synthetic loci with ground-truth labels** to validate the engine — real-data AD reference loci(TREM2 / BIN1 / APOE / CLU / PICALM) are listed as the target for a real-data version. Evidencegrade is **C** (methodology validation).

Frequently asked questions