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A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Nature Genetics volume 44, pages 1066–1071 (2012)Cite this article

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Abstract

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

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Figure 1: Simulation results.
Figure 2: GWAS of LDL and triglycerides.
Figure 3: Venn diagrams summarizing the GWAS of A. thaliana flowering data32.
Figure 4: Summary of FRS6 results.

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Acknowledgements

We thank 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 investigators of the NFBC1966 Study and does not necessarily reflect the opinions or views of the NFBC1966 Study Investigators, the Broad Institute, UCLA, the University of Oulu, the National Institute for Health and Welfare in Finland or the NHLBI. We furthermore thank N.B. Freimer and S.K. Service for their help in preprocessing the NFBC1966 data. We would also like to thank P. Forai for excellent IT and cluster support at the Gregor Mendel Institute, the INRA MIGALE bioinformatics platform for further computational resources and J. Dekkers, P. Donnelly, E. Eskin, C. Niango and A. Price for comments on the manuscript and/or helpful discussions. This work was supported by grants to M.N. from the US National Institutes of Health (P50 HG002790) and the European Union Framework Programme 7 (TransPLANT, grant agreement 283496), as well as by grants from the Deutsche Forschungsgemeinschaft (DFG) (A.K., KO4184/1-1) and the Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA) department of INRA (V.S.).

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Author notes

  1. Arthur Korte, Bjarni J Vilhjálmsson and Vincent Segura: These authors contributed equally to this work.

Authors and Affiliations

  1. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna, Austria

    Arthur Korte, Bjarni J Vilhjálmsson, Vincent Segura, Alexander Platt, Quan Long & Magnus Nordborg

  2. Department of Molecular and Computational Biology, University of Southern California, Los Angeles, California, USA

    Bjarni J Vilhjálmsson, Alexander Platt & Magnus Nordborg

  3. Institut National de la Recherche Agronomique (INRA), UR0588, Orléans, France

    Vincent Segura

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  1. Arthur Korte
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Contributions

All authors helped design the study. A.K., B.J.V. and V.S. developed the theory and implemented the simulations. A.K., B.J.V. and M.N. wrote the paper with input from V.S., A.P. and Q.L.

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Correspondence to Magnus Nordborg.

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Korte, A., Vilhjálmsson, B., Segura, V. et al. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet 44, 1066–1071 (2012). https://doi.org/10.1038/ng.2376

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