bio-causal-genomics-fine-mapping
Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.
npx skills add BioTender-max/awesome-bio-agent-skills --skill fine-mapping --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: susieR 0.12.27+, coloc 5.2.3+, FINEMAP 1.4.2+, PolyFun (head of `omerwe/polyfun` 2024), PAINTOR V3.0, SuSiEx (head of `getian107/SuSiEx`), DAP-G (head of `xqwen/dap`), pyfocus 0.8+, R 4.3+, PLINK 1.9 / 2.0. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('susieR')` then `?susie_rss` to confirm argument names (e.g., `prior_weights` vs `prior_variance` semantics) - CLI: `finemap --help`, `SuSiEx --help`, `PAINTOR --help`, `dap-g --help` to confirm flags - Python: `polyfun.py --help` If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying. # Fine-Mapping **"Narrow my GWAS locus to the variants likely to be causal"** -> Fit a sparse Bayesian regression that propagates LD into posterior inclusion probabilities (PIPs) and credible sets, then validate that credible sets correspond to physically reasonable haplotypes given the LD reference. - R (summary statistics + LD): `susieR::susie_rss(z, R, n, L=10)` + `estimate_s_rss` LD diagnostic - R (individual-level genotypes): `susieR::susie(X, y, L=10)` - CLI (
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Critical LD Diagnostic Block (susierss)
- Per-Tool Failure Modes
- LD reference mismatch (most common)
- Non-sparse architecture (biobank scale)
- L too small
- priorweights vs priorvariance confusion (PolyFun integration)
- Credible-set misinterpretation
- Cross-ancestry with single-ancestry LD
- Case-control GWAS passing Ntotal instead of Neff
- Allele Harmonization with the LD Reference
- Reconciliation: When Methods Disagree
Stage 1: compute per-SNP h2 contributions genome-wide (one-time) polyfun.py --compute-h2-L2 \ Stage 2: extract per-SNP prior for one locus polyfun.py --compute-h2-bins \ SuSiEx \ cat > locus.master <<'EOF' finemap --sss \ locus.snp -> per-variant prob (PIP), log10bf locus.cred -> credible sets at increasing causal counts locus.config -> top configurations
What does the bio-causal-genomics-fine-mapping skill do?
Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill fine-mapping --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.