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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ftpubmed:oai:pubmedcentral.nih.gov:3432668 2023-05-15T17:42:30+02:00 A mixed-model approach for genome-wide association studies of correlated traits in structured populations Korte, Arthur Vilhjálmsson, Bjarni J. Segura, Vincent Platt, Alexander Long, Quan Nordborg, Magnus 2012-08-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432668 http://www.ncbi.nlm.nih.gov/pubmed/22902788 https://doi.org/10.1038/ng.2376 en eng http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432668 http://www.ncbi.nlm.nih.gov/pubmed/22902788 http://dx.doi.org/10.1038/ng.2376 Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms Article Text 2012 ftpubmed https://doi.org/10.1038/ng.2376 2013-09-04T12:20:38Z 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. Text Northern Finland PubMed Central (PMC) Nature Genetics 44 9 1066 1071 |
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Article Korte, Arthur Vilhjálmsson, Bjarni J. Segura, Vincent Platt, Alexander Long, Quan Nordborg, Magnus A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
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Article |
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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. |
format |
Text |
author |
Korte, Arthur Vilhjálmsson, Bjarni J. Segura, Vincent Platt, Alexander Long, Quan Nordborg, Magnus |
author_facet |
Korte, Arthur Vilhjálmsson, Bjarni J. Segura, Vincent Platt, Alexander Long, Quan Nordborg, Magnus |
author_sort |
Korte, Arthur |
title |
A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
title_short |
A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
title_full |
A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
title_fullStr |
A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
title_full_unstemmed |
A mixed-model approach for genome-wide association studies of correlated traits in structured populations |
title_sort |
mixed-model approach for genome-wide association studies of correlated traits in structured populations |
publishDate |
2012 |
url |
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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Northern Finland |
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Northern Finland |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432668 http://www.ncbi.nlm.nih.gov/pubmed/22902788 http://dx.doi.org/10.1038/ng.2376 |
op_rights |
Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
op_doi |
https://doi.org/10.1038/ng.2376 |
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Nature Genetics |
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44 |
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9 |
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1066 |
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1071 |
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1766144363861114880 |