Abstract
Genome-wide association studies (GWAS) have traditionally been undertaken in homogeneous populations from the same ancestry group. However, with the increasing availability of GWAS in large-scale multi-ethnic cohorts, we have evaluated a framework for detecting association of genetic variants with complex traits, allowing for population structure, and developed a powerful test of heterogeneity in allelic effects between ancestry groups. We have applied the methodology to identify and characterise loci associated with susceptibility to type 2 diabetes (T2D) using GWAS data from the Resource for Genetic Epidemiology on Adult Health and Aging, a large multi-ethnic population-based cohort, created for investigating the genetic and environmental basis of age-related diseases. We identified a novel locus for T2D susceptibility at genome-wide significance (P<5 × 10−8) that maps to TOMM40-APOE, a region previously implicated in lipid metabolism and Alzheimer's disease. We have also confirmed previous reports that single-nucleotide polymorphisms at the TCF7L2 locus demonstrate the greatest extent of heterogeneity in allelic effects between ethnic groups, with the lowest risk observed in populations of East Asian ancestry.
Highlights
Genome-wide association studies (GWAS) of complex human traits have traditionally been undertaken in homogeneous populations from the same ancestry group because: (i) geographical confounding between the trait and genetic variation can inflate type I error rates, if not accounted for in the association analysis;[1] and (ii) there may be reduced power to detect association due to heterogeneity in allelic effects on the trait between ethnicities.[2]
We have evaluated a framework for detecting association of genetic variants with a complex trait in multi-ethnic cohorts, allowing for population structure, and developed a powerful test of heterogeneity in allelic effects between ancestry groups
We have demonstrated that adjustment for axes of genetic variation (AGV) as covariates in a generalised linear regression modelling framework can control type I error rates for association testing in multi-ethnic cohorts
Summary
Genome-wide association studies (GWAS) of complex human traits have traditionally been undertaken in homogeneous populations from the same ancestry group because: (i) geographical confounding between the trait and genetic variation can inflate type I error rates, if not accounted for in the association analysis;[1] and (ii) there may be reduced power to detect association due to heterogeneity in allelic effects on the trait between ethnicities.[2] more recent GWAS have been performed in large multi-ethnic cohorts,[3,4] where assignment of individuals to homogeneous population groups becomes increasingly difficult because of ancestral diversity and admixture
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
