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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.

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Figure 1: A GWAS for a simulated trait with two causal SNPs randomly chosen from a real A. thaliana SNP data set.
Figure 2: Power and FDR in 100-locus model simulations for four different mapping methods: LM, SWLM, MM and MLMM.
Figure 3: GWAS for LDL levels in the NFBC1966 data set.
Figure 4: GWAS for sodium accumulation in A. thaliana.
Figure 5: An example of Bayesian MLMM for the analysis of FLC expression in A. thaliana.


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We acknowledge the NFBC1966 Study investigators for allowing us to use their phenotype and genotype data in our study. The NFBC1966 Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the Broad Institute, the University of California, Los Angeles (UCLA), the University of Oulu and the National Institute for Health and Welfare in Finland. This manuscript was not prepared in collaboration with the investigators from the NFBC1966 Study and does not necessarily reflect the opinions or views of these investigators or those at the collaborating institutes. We thank N.B. Freimer and S.K. Service for their help in pre-processing the NFBC1966 data. We would also like to thank P. Forai for excellent information technology and cluster support at GMI, the INRA MIGALE bioinformatics platform for additional computational resources and D.V. Conti, D.J. Balding and S. Srivastava for useful discussions on the topic. Finally, we would like to thank the anonymous reviewers for their helpful comments on the manuscript. This work was supported by grants from the Ecologie des Forts, Prairies et milieux Aquatiques (EFPA) department of INRA to V.S. and Deutsche Forschungsgemeinschaft (DFG) to A.K. and by grants from the US National Institutes of Health (P50 HG002790) and the European Union Framework Programme 7 (TransPLANT, grant agreement 283496) to M.N., as well as by the Austrian Academy of Sciences through GMI.

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All authors contributed to designing the study. V.S. and B.J.V. ran the simulations and analyzed the data. V.S., B.J.V. and M.N. wrote the manuscript with input from A.P., A.K., Ü.S. and Q.L.

Corresponding author

Correspondence to Magnus Nordborg.

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The authors declare no competing financial interests.

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Supplementary Table 1, Supplementary Figures 1–11 and Supplementary Note (PDF 1167 kb)

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Segura, V., Vilhjálmsson, B., Platt, A. et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44, 825–830 (2012).

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