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]))
}

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.

library(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)

# 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

# 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

# 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.

# 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

# 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

# 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

library(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