An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.
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.
Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna, Austria.
Vincent
V
Segura
Bjarni J
BJ
Vilhjálmsson
Alexander
A
Platt
Arthur
A
Korte
Ümit
Ü
Seren
Quan
Q
Long
Magnus
M
Nordborg
D017360:Arabidopsis / Q000235:genetics; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods; D056426:Genetic Loci; D015894:Genome, Human; D018745:Genome, Plant; D055106:Genome-Wide Association Study / Q000379:methods; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D056004:Molecular Dynamics Simulation; D020641:Polymorphism, Single Nucleotide; D044382:Population Groups / Q000235:genetics*; D040641:Quantitative Trait Loci