Journal
NATURE GENETICS
Volume 44, Issue 7, Pages 825-U144Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/ng.2314
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Funding
- National Heart, Lung, and Blood Institute (NHLBI)
- Ecologie des Forets, Prairies et milieux Aquatiques (EFPA) department of INRA
- Deutsche Forschungsgemeinschaft (DFG)
- US National Institutes of Health [P50 HG002790]
- European Union [283496]
- Austrian Academy of Sciences through GMI
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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.
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