Open AccessA mixed-model approach for genome-wide association studies of correlated traits in structured populations
Author(s) -
Arthur Korte,
Bjarni J. Vilhjálmsson,
Vincent Segura,
Alexander Platt,
Quan Long,
Magnus Nordborg
Publication year - 2012
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/ng.2376
Subject(s) - genome wide association study , biology , quantitative trait locus , trait , genetic association , genetics , computational biology , population , association mapping , evolutionary biology , gene , single nucleotide polymorphism , computer science , genotype , demography , sociology , programming language
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.