Bio Causal Genomics Fine Mapping
Identify likely causal variants within GWAS loci using SuSiE for sum of single effects...
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-fine-mapping -g -a claude-code -y- Repository
- FreedomIntelligence/OpenClaw-Medical-Skills3.1k
- Category
- Engineering
- Updated
- 4mo ago
- License
- See repository
- Context cost
- Mediumabout 2,190 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto 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 --sssfor 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 signals at the locus
SuSiE (Sum of Single Effects)
Goal: Fine-map a GWAS locus to identify likely causal variants and credible sets from individual-level data.
Approach: Fit SuSiE's sum-of-single-effects model on the genotype matrix, then extract 95% credible sets (each containing the causal variant) and per-variant posterior inclusion probabilities.
library(susieR)
# --- From individual-level data ---
# X: genotype matrix (n x p), standardized
# Y: phenotype vector (n x 1)
# L: max number of causal variants (10 is a reasonable default)
fit <- susie(X, Y, L = 10)
