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

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 in 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 second). We performed a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits. HyPrColoc identified 43 regions in which CHD colocalized with [≥]1 trait, including 5 potentially new CHD loci. Across the 43 loci, we further integrated gene and protein expression quantitative trait loci to identify candidate causal genes.