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.
rlibrary(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$pip top_variants <- order(-pip)[1:10] cat('\nTop 10 variants by PIP:\n') for (i in top_variants) { cat(sprintf(' Variant %d: PIP = %.4f\n', i, pip[i])) }
SuSiE with Summary Statistics (susie_rss)
Goal: Fine-map a GWAS locus using summary statistics and an LD reference matrix (no individual-level data needed).
Approach: Compute Z-scores from beta/SE, provide a matched-ancestry LD correlation matrix, and run susie_rss to identify credible sets and PIPs.
rlibrary(susieR) # --- Most common usage: GWAS summary statistics + LD matrix --- # z: Z-scores (beta / se) for each variant # R: LD correlation matrix (from matched ancestry reference) # n: Sample size # L: Max causal variants z_scores <- gwas_df$BETA / gwas_df$SE ld_matrix <- as.matrix(read.table('ld_matrix.ld')) # Ensure SNP order matches between z-scores and LD matrix stopifnot(nrow(ld_matrix) == length(z_scores)) fit <- susie_rss(z = z_scores, R = ld_matrix, n = 50000, L = 10) # Credible sets cs <- fit$sets$cs for (i in seq_along(cs)) { cat(sprintf('Credible set %d: %d variants, purity = %.3f\n', i, length(cs[[i]]), fit$sets$purity[i, 1])) cat(' Variants:', paste(gwas_df$SNP[cs[[i]]], collapse = ', '), '\n') } # PIPs gwas_df$PIP <- fit$pip top_pip <- gwas_df[order(-gwas_df$PIP), ][1:20, c('SNP', 'PIP', 'P')] print(top_pip)
Choosing L (Number of Causal Variants)
r# L = max number of causal signals SuSiE will search for # Too low: Misses real signals # Too high: Increases computation but rarely hurts results (SuSiE prunes excess) # # Guidelines: # L = 1: Single causal variant expected # L = 5: Most GWAS loci # L = 10: Default, works well for most cases # L = 20: Very complex loci (e.g., HLA region) # Compare fits with different L fit_l5 <- susie_rss(z = z_scores, R = ld_matrix, n = 50000, L = 5) fit_l10 <- susie_rss(z = z_scores, R = ld_matrix, n = 50000, L = 10) cat('L=5 credible sets:', length(fit_l5$sets$cs), '\n') cat('L=10 credible sets:', length(fit_l10$sets$cs), '\n')
LD Reference Panel
bash# Generate LD matrix from 1000 Genomes with plink # Must match ancestry of GWAS sample # Extract region plink --bfile 1000G_EUR \ --chr 6 --from-bp 30000000 --to-bp 31000000 \ --make-bed --out locus_ref # Compute correlation matrix plink --bfile locus_ref \ --r square --out ld_matrix # Filter to GWAS SNPs only plink --bfile locus_ref \ --extract gwas_snps.txt \ --r square --out ld_matrix_filtered
r# Read LD matrix into R ld <- as.matrix(read.table('ld_matrix.ld')) # Ensure positive semi-definite (numerical issues can violate this) # Add small ridge to diagonal if needed eigenvalues <- eigen(ld, only.values = TRUE)$values if (any(eigenvalues < 0)) { ld <- ld + diag(abs(min(eigenvalues)) + 1e-6, nrow(ld)) }
FINEMAP
Goal: Fine-map a locus using an alternative shotgun stochastic search algorithm.
Approach: Prepare .z (summary stats), .ld (LD matrix), and .master (config) files, then run FINEMAP to compute per-variant PIPs and causal configurations.
bash# FINEMAP: alternative fine-mapping tool using shotgun stochastic search # Download from http://www.christianbenner.com/ # Required input files: # 1. .z file: SNP, chromosome, position, allele1, allele2, MAF, beta, se # 2. .ld file: LD matrix (space-separated, no header) # 3. .master file: configuration # Create master file cat > master.txt << 'EOF' z;ld;snp;config;cred;log;n_samples locus.z;locus.ld;locus.snp;locus.config;locus.cred;locus.log;50000 EOF # Run FINEMAP finemap --sss --in-files master.txt --n-causal-snps 5
r# Parse FINEMAP output finemap_snp <- read.table('locus.snp', header = TRUE) finemap_snp <- finemap_snp[order(-finemap_snp$prob), ] cat('Top variants by PIP (FINEMAP):\n') print(head(finemap_snp[, c('rsid', 'prob', 'log10bf')], 10)) # Credible sets from .cred file finemap_cred <- read.table('locus.cred', header = TRUE)
Functional Annotation with PolyFun
r# PolyFun integrates functional annotations to improve fine-mapping # Uses LD-score regression to estimate per-SNP heritability # Provides functionally-informed priors for SuSiE # After running PolyFun (Python), read prior variances polyfun_priors <- read.table('polyfun_output.txt', header = TRUE) # Use priors in SuSiE fit_informed <- susie_rss( z = z_scores, R = ld_matrix, n = 50000, L = 10, prior_variance = polyfun_priors$prior_var )
Visualization
rlibrary(ggplot2) plot_pip <- function(gwas_df, credible_sets = NULL) { p <- ggplot(gwas_df, aes(x = POS / 1e6, y = PIP)) + geom_point(alpha = 0.5, size = 1.5) + geom_hline(yintercept = 0.5, linetype = 'dashed', color = 'orange', alpha = 0.5) + geom_hline(yintercept = 0.95, linetype = 'dashed', color = 'red', alpha = 0.5) + labs(x = 'Position (Mb)', y = 'Posterior Inclusion Probability', title = 'Fine-Mapping Results') + theme_minimal() if (!is.null(credible_sets)) { cs_snps <- unlist(credible_sets) gwas_df$in_cs <- seq_len(nrow(gwas_df)) %in% cs_snps p <- ggplot(gwas_df, aes(x = POS / 1e6, y = PIP, color = in_cs)) + geom_point(alpha = 0.6, size = 1.5) + scale_color_manual(values = c('grey60', 'red'), labels = c('No', 'Yes'), name = 'In credible set') + geom_hline(yintercept = 0.5, linetype = 'dashed', alpha = 0.3) + labs(x = 'Position (Mb)', y = 'PIP', title = 'Fine-Mapping with Credible Sets') + theme_minimal() } p } # Combined GWAS + PIP plot plot_gwas_pip <- function(gwas_df) { library(patchwork) p_gwas <- ggplot(gwas_df, aes(x = POS / 1e6, y = -log10(P))) + geom_point(alpha = 0.4, size = 1) + geom_hline(yintercept = -log10(5e-8), linetype = 'dashed', color = 'red', alpha = 0.3) + labs(x = NULL, y = '-log10(P)', title = 'GWAS') + theme_minimal() + theme(axis.text.x = element_blank()) p_pip <- ggplot(gwas_df, aes(x = POS / 1e6, y = PIP)) + geom_point(alpha = 0.4, size = 1, color = 'steelblue') + labs(x = 'Position (Mb)', y = 'PIP', title = 'Fine-Mapping') + theme_minimal() p_gwas / p_pip }
Related Skills
- colocalization-analysis - SuSiE-coloc uses fine-mapping credible sets
- mendelian-randomization - Fine-map instrument loci for causal variants
- population-genetics/linkage-disequilibrium - LD matrices for fine-mapping
- variant-calling/variant-annotation - Annotate fine-mapped variants
