An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

• DOI: 10.1038/ng.2314
• Publication Date: 2012-06-17T21:12:59Z
• Authors: Vincent Segura; Bjarni J Vilhjálm...; Alexander Platt; Arthur Korte; Ümit Seren; Quan Long; Magnus Nordborg
• Keywords: Genotype; Quantitative Trait Loci; Arabidopsis; polymorphisme; Molecular Dynamics Simulation; mlst; 310; Polymorphism, Single Nucleotide; Article; Linkage Disequilibrium; apparentement; 03 medical and health sciences; stratification; génétique d'association; Population Groups; [SDV.BV]Life Sciences [q-bio]/Vegetal Biology; sélection; Humans; [SDV.BV] Life Sciences [q-bio]/Vegetal Biology; modèle mixte; 0303 health sciences; Vegetal Biology; Models, Genetic; Genome, Human; qtl; Chromosome Mapping; Bayes Theorem; variation génétique; régression; Genetic Loci; étude d'association; Biologie végétale; Genome, Plant; Genome-Wide Association Study

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.