Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution

• Published in: Human molecular genetics 2019-12, Vol.28 (24), p.4161-4172
• Main Authors: Wu, Ying, Broadaway, K Alaine, Raulerson, Chelsea K, Scott, Laura J, Pan, Calvin, Ko, Arthur, He, Aiqing, Tilford, Charles, Fuchsberger, Christian, Locke, Adam E, Stringham, Heather M, Jackson, Anne U, Narisu, Narisu, Kuusisto, Johanna, Pajukanta, Päivi, Collins, Francis S, Boehnke, Michael, Laakso, Markku, Lusis, Aldons J, Civelek, Mete, Mohlke, Karen L
• Format: Article
• Language: English
• Subjects: Adipose Tissue - physiology; Adult; Bayes Theorem; Body Fat Distribution; Body Mass Index; Female; General One; Genetic Predisposition to Disease; Genome-Wide Association Study - methods; Humans; Linkage Disequilibrium; Male; Metabolic Syndrome - genetics; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Subcutaneous Fat - metabolism; Waist-Hip Ratio - methods
• ISSN: 0964-6906; 1460-2083
• DOI: 10.1093/hmg/ddz263

Abstract Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist–hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.