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

• Published in: Nature Genetics
• Main Authors: Korte, Arthur, Vilhjálmsson, Bjarni J, Segura, Vincent, Platt, Alexander, Long, Quan, Nordborg, Magnus
• Other Authors: Austrian Academy of Sciences (OeAW), Gregor Mendel Institute of Molecular Plant Biology (GMI), Department of Molecular and Computational Biology, University of Southern California (USC), Unité de recherche Amélioration, Génétique et Physiologie Forestières (AGPF), Institut National de la Recherche Agronomique (INRA)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2012
• Subjects: [SDV]Life Sciences [q-bio]; Northern Finland
• Online Access: https://hal.archives-ouvertes.fr/hal-01267803; https://hal.archives-ouvertes.fr/hal-01267803/document; https://hal.archives-ouvertes.fr/hal-01267803/file/2012_Korte_poster_NTMM_2.pdf; https://doi.org/10.1038/ng.2376

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 [i]Arabidopsis thaliana[/i] data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.