bio-causal-genomics-fine-mapping

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 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)

# Extract credible sets
# 95% credible sets: each set contains the causal variant with >= 95% probability
# coverage: minimum posterior mass for the credible set (default 0.95)
# min_abs_corr: minimum purity (correlation among variants in set; > 0.5 is good)
cs <- fit$sets$cs
cat('Number of credible sets:', length(cs), '\n')

# Credible set purity (minimum absolute correlation within set)
# Purity > 0.5: Well-resolved signal
# Purity < 0.5: Signal may be confounded by LD
purity <- fit$sets$purity
print(purity)

# PIPs for all variants
pip <- fit$pip
top_variants <- order(-pip)[1:10]
cat('\nTop 10 variants by PIP:\n')
for (i in top_variants) {
  cat(sprintf('  Variant %d: PIP = %.4f\n', i, pip[i]))
}