bio-causal-genomics-fine-mapping
Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-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: ggplot2 3.5+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Fine-Mapping **"Narrow my GWAS locus to the likely causal variant"** → Compute posterior inclusion probabilities (PIPs) for each variant and construct credible sets containing the causal variant at a specified confidence level, accounting for LD and multiple causal signals. - R: `susieR::susie_rss()` for SuSiE fine-mapping from summary statistics - CLI: `finemap --sss` for shotgun stochastic search ## Overview Fine-mapping narrows GWAS association signals to identify likely causal variants. Key outputs: - **PIP** (Posterior Inclusion Probability) - Probability each variant is causal (0-1) - **Credible set** - Minimal set of variants containing the causal variant at a given confidence level (e.g., 95%) - **L** - Number of independent causal
- Version Compatibility
- Overview
- SuSiE (Sum of Single Effects)
- SuSiE with Summary Statistics (susierss)
- Choosing L (Number of Causal Variants)
- LD Reference Panel
- FINEMAP
- Functional Annotation with PolyFun
- Visualization
- Related Skills
Generate LD matrix from 1000 Genomes with plink Must match ancestry of GWAS sample Extract region plink --bfile 1000G_EUR \ Compute correlation matrix plink --bfile locus_ref \ Filter to GWAS SNPs only Download from http://www.christianbenner.com/ Required input files: Create master file
What does the bio-causal-genomics-fine-mapping skill do?
Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.