A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits

TLDR

Genome‑wide association studies have identified thousands of genomic regions influencing complex diseases. The authors aim to develop HyPrColoc, a fast deterministic Bayesian algorithm that can detect shared genetic risk across many traits simultaneously. HyPrColoc performs rapid multi‑trait colocalization using GWAS summary statistics, enabling joint analysis of up to 100 traits in about one second and integrating eQTL and pQTL data to pinpoint candidate causal genes. Applying the method to coronary heart disease and fourteen related traits revealed 43 colocalized loci, including five novel CHD loci, and linked these regions to candidate causal genes through integrated expression data.

Abstract

Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.