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

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...

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Bibliographic Details
Published in:Nature Genetics
Main Authors: Korte, Arthur, Vilhjálmsson, Bjarni J., Segura, Vincent, Platt, Alexander, Long, Quan, Nordborg, Magnus
Format: Text
Language:English
Published: 2012
Subjects:
Article
Northern Finland
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432668
http://www.ncbi.nlm.nih.gov/pubmed/22902788
https://doi.org/10.1038/ng.2376
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Summary: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 human cohort data for correlated blood lipid traits from the Northern Finland Birth Cohort 1966, and demonstrate greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this to an Arabidopsis dataset for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

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