A linear mixed-model approach to study multivariate gene–environment interactions

BIOS Consortium, Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Bastiaan T. Heijmans, Peter A. C.’t Hoen, Joyce van Meurs, Aaron Isaacs, Rick Jansen, Lude Franke, Dorret I. Boomsma, René Pool, Jenny van Dongen, Jouke J. Hottenga, Marleen M. J. van Greevenbroek, Coen D. A. Stehouwer, Carla J. H. van der Kallen, Casper G. Schalkwijk, Cisca Wijmenga & 31 others Alexandra Zhernakova, Ettje F. Tigchelaar, P. Eline Slagboom, Marian Beekman, Joris Deelen, Diana van Heemst, Jan H. Veldink, Leonard H. van den Berg, Cornelia M. van Duijn, Bert A. Hofman, André G. Uitterlinden, P. Mila Jhamai, Michael Verbiest, H. Eka D. Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Hailiang Mei, Maarten van Iterson, Michiel van Galen, Jan Bot, Peter van’t Hof, Patrick Deelen, Irene Nooren, Matthijs Moed, Martijn Vermaat, Rick Jansen, Jenny van Dongen, Casper G. Schalkwijk, Leonard H. van den Berg

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
LanguageEnglish
Pages180-186
Number of pages7
JournalNature Genetics
Volume51
Issue number1
DOIs
StatePublished - 2019

Cite this

@article{bc3d6aefb18e4156b9390c5e6c101469,
title = "A linear mixed-model approach to study multivariate gene–environment interactions",
abstract = "Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.",
author = "{BIOS Consortium} and Rachel Moore and Casale, {Francesco Paolo} and {Jan Bonder}, Marc and Danilo Horta and Heijmans, {Bastiaan T.} and {C.’t Hoen}, {Peter A.} and {van Meurs}, Joyce and Aaron Isaacs and Rick Jansen and Lude Franke and Boomsma, {Dorret I.} and Ren{\'e} Pool and {van Dongen}, Jenny and Hottenga, {Jouke J.} and {van Greevenbroek}, {Marleen M. J.} and Stehouwer, {Coen D. A.} and {van der Kallen}, {Carla J. H.} and Schalkwijk, {Casper G.} and Cisca Wijmenga and Alexandra Zhernakova and Tigchelaar, {Ettje F.} and Slagboom, {P. Eline} and Marian Beekman and Joris Deelen and {van Heemst}, Diana and Veldink, {Jan H.} and {van den Berg}, {Leonard H.} and {van Duijn}, {Cornelia M.} and Hofman, {Bert A.} and Uitterlinden, {Andr{\'e} G.} and Jhamai, {P. Mila} and Michael Verbiest and Suchiman, {H. Eka D.} and Marijn Verkerk and {van der Breggen}, Ruud and {van Rooij}, Jeroen and Nico Lakenberg and Hailiang Mei and {van Iterson}, Maarten and Galen, {Michiel van} and Jan Bot and {van’t Hof}, Peter and Patrick Deelen and Irene Nooren and Matthijs Moed and Martijn Vermaat and Rick Jansen and {van Dongen}, Jenny and Schalkwijk, {Casper G.} and {van den Berg}, {Leonard H.}",
year = "2019",
doi = "10.1038/s41588-018-0271-0",
language = "English",
volume = "51",
pages = "180--186",
journal = "Nature Genetics",
issn = "1061-4036",
publisher = "Nature Publishing Group",
number = "1",

}

A linear mixed-model approach to study multivariate gene–environment interactions. / BIOS Consortium.

In: Nature Genetics, Vol. 51, No. 1, 2019, p. 180-186.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A linear mixed-model approach to study multivariate gene–environment interactions

AU - BIOS Consortium

AU - Moore,Rachel

AU - Casale,Francesco Paolo

AU - Jan Bonder,Marc

AU - Horta,Danilo

AU - Heijmans,Bastiaan T.

AU - C.’t Hoen,Peter A.

AU - van Meurs,Joyce

AU - Isaacs,Aaron

AU - Jansen,Rick

AU - Franke,Lude

AU - Boomsma,Dorret I.

AU - Pool,René

AU - van Dongen,Jenny

AU - Hottenga,Jouke J.

AU - van Greevenbroek,Marleen M. J.

AU - Stehouwer,Coen D. A.

AU - van der Kallen,Carla J. H.

AU - Schalkwijk,Casper G.

AU - Wijmenga,Cisca

AU - Zhernakova,Alexandra

AU - Tigchelaar,Ettje F.

AU - Slagboom,P. Eline

AU - Beekman,Marian

AU - Deelen,Joris

AU - van Heemst,Diana

AU - Veldink,Jan H.

AU - van den Berg,Leonard H.

AU - van Duijn,Cornelia M.

AU - Hofman,Bert A.

AU - Uitterlinden,André G.

AU - Jhamai,P. Mila

AU - Verbiest,Michael

AU - Suchiman,H. Eka D.

AU - Verkerk,Marijn

AU - van der Breggen,Ruud

AU - van Rooij,Jeroen

AU - Lakenberg,Nico

AU - Mei,Hailiang

AU - van Iterson,Maarten

AU - Galen,Michiel van

AU - Bot,Jan

AU - van’t Hof,Peter

AU - Deelen,Patrick

AU - Nooren,Irene

AU - Moed,Matthijs

AU - Vermaat,Martijn

AU - Jansen,Rick

AU - van Dongen,Jenny

AU - Schalkwijk,Casper G.

AU - van den Berg,Leonard H.

PY - 2019

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AB - Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/30478441

U2 - 10.1038/s41588-018-0271-0

DO - 10.1038/s41588-018-0271-0

M3 - Article

VL - 51

SP - 180

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JO - Nature Genetics

T2 - Nature Genetics

JF - Nature Genetics

SN - 1061-4036

IS - 1

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