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 - Franke, Lude
AU - Barroso, Inês
AU - Stegle, Oliver
AU - 't Hoen, P.A.
AU - van Meurs, Joyce B J
AU - Isaacs, Aaron
AU - Jansen, Rick
AU - Franke, Lude
AU - Boomsma, D.I.
AU - Pool, R.
AU - van Dongen, J.
AU - Hottenga, J.J.
AU - Van Greevenbroek, Marleen 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, Andre 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 - van Galen, Michiel
AU - Bot, Jan
AU - van’t Hof, Peter
AU - Deelen, Patrick
AU - Nooren, Irene
AU - Moed, Matthijs
AU - Vermaat, Martijn
AU - Zhernakova, Dasha V.
AU - Luijk, René
AU - Heijmans, Bastiaan T.
AU - C.’t Hoen, Peter A.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
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 - http://www.scopus.com/inward/citedby.url?scp=85057296018&partnerID=8YFLogxK
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UR - https://www.ncbi.nlm.nih.gov/pubmed/30478441
U2 - https://doi.org/10.1038/s41588-018-0271-0
DO - https://doi.org/10.1038/s41588-018-0271-0
M3 - Article
C2 - 30478441
SN - 1061-4036
VL - 51
SP - 180
EP - 186
JO - Nature Genetics
JF - Nature Genetics
IS - 1
ER -