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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ftccsdartic:oai:HAL:hal-01267803v1 2023-05-15T17:42:31+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 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) 2012 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 en eng HAL CCSD Nature Publishing Group info:eu-repo/semantics/altIdentifier/doi/10.1038/ng.2376 hal-01267803 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 doi:10.1038/ng.2376 PRODINRA: 168731 WOS: 000308491200022 info:eu-repo/semantics/OpenAccess ISSN: 1061-4036 EISSN: 1546-1718 Nature Genetics https://hal.archives-ouvertes.fr/hal-01267803 Nature Genetics, 2012, 44 (9), pp.1066-1071. ⟨10.1038/ng.2376⟩ [SDV]Life Sciences [q-bio] info:eu-repo/semantics/article Journal articles 2012 ftccsdartic https://doi.org/10.1038/ng.2376 2022-12-04T01:05:00Z 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. Article in Journal/Newspaper Northern Finland Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Nature Genetics 44 9 1066 1071 |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
[SDV]Life Sciences [q-bio] |
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[SDV]Life Sciences [q-bio] 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 |
topic_facet |
[SDV]Life Sciences [q-bio] |
description |
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. |
author2 |
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 |
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 |
publisher |
HAL CCSD |
publishDate |
2012 |
url |
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 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
ISSN: 1061-4036 EISSN: 1546-1718 Nature Genetics https://hal.archives-ouvertes.fr/hal-01267803 Nature Genetics, 2012, 44 (9), pp.1066-1071. ⟨10.1038/ng.2376⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1038/ng.2376 hal-01267803 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 doi:10.1038/ng.2376 PRODINRA: 168731 WOS: 000308491200022 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1038/ng.2376 |
container_title |
Nature Genetics |
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44 |
container_issue |
9 |
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1066 |
op_container_end_page |
1071 |
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1766144385465974784 |