Bayesian Genetic Colocalization Test of Two Traits Using coloc. Issue 12 (14th December 2022)

• Record Type: Journal Article
• Authors: Rasooly, Danielle; Peloso, Gina M.; Giambartolomei, Claudia
• Is Part Of: Current protocols. Volume 2:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-14
• Subjects: bayesian -- colocalization -- eQTL -- fine‐mapping -- genetic epidemiology -- GWAS; Life sciences -- Laboratory manuals -- Periodicals; Biology -- Laboratory manuals -- Periodicals; Life sciences -- Technique -- Periodicals; Biology -- Technique -- Periodicals; 570.028
• DOI: 10.1002/cpz1.627
• Languages: English
• ISSNs: 2691-1299

Abstract

Abstract: Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome‐wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non‐coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics‐based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary‐level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses. © 2022 Wiley Periodicals LLC. Basic Protocol : Performing a genetic colocalization analysis using the 'coloc' package in R and summary‐level data Support Protocol : Installing the 'coloc' R package