bio-causal-genomics-colocalization-analysis

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

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Colocalization Analysis

"Test whether my GWAS signal and eQTL share the same causal variant" → Compute Bayesian posterior probabilities for five colocalization hypotheses (no association, trait-1-only, trait-2-only, distinct causal variants, shared causal variant) to distinguish true causal overlap from LD-driven coincidence.

• R: coloc::coloc.abf() for approximate Bayes factor colocalization

Overview

Colocalization tests whether two association signals at the same locus are driven by the same causal variant. This distinguishes shared causality from coincidental overlap due to LD.

Five hypotheses tested by coloc:

• H0: No association with either trait
• H1: Association with trait 1 only
• H2: Association with trait 2 only
• H3: Both associated, different causal variants
• H4: Both associated, shared causal variant

coloc.abf Analysis

Goal: Test whether two traits share a causal variant at a GWAS locus using Bayesian colocalization.

Approach: Format summary statistics for each trait as named lists, run coloc.abf to compute posterior probabilities for five hypotheses (H0-H4), and interpret PP.H4 as evidence for a shared causal variant.

library(coloc)

# --- Input format: named list with GWAS summary stats ---
# Required fields: beta, varbeta, snp, position, type, N
# type = 'quant' (continuous) or 'cc' (case-control)

gwas_data <- list(
  beta = gwas_df$BETA,
  varbeta = gwas_df$SE^2,
  snp = gwas_df$SNP,
  position = gwas_df$POS,
  type = 'cc',           # Case-control study
  s = 0.3,               # Proportion of cases (required for cc)
  N = 50000              # Total sample size
)

eqtl_data <- list(
  beta = eqtl_df$BETA,
  varbeta = eqtl_df$SE^2,
  snp = eqtl_df$SNP,
  position = eqtl_df$POS,
  type = 'quant',        # Quantitative trait (expression)
  N = 500,               # eQTL sample size
  sdY = 1                # SD of trait (1 if already normalized)
)

# --- Run colocalization ---
result <- coloc.abf(dataset1 = gwas_data, dataset2 = eqtl_data)

# Posterior probabilities
# PP.H4 > 0.8: Strong evidence for colocalization (shared variant)
# PP.H3 > 0.8: Distinct causal variants at the locus
# PP.H4 between 0.5-0.8: Suggestive but inconclusive
print(result$summary)