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

P.A. 't Hoen, Joyce B J van Meurs, Aaron Isaacs, Rick Jansen, Lude Franke, D.I. Boomsma, R. Pool, J. van Dongen, J.J. Hottenga, Marleen J. Van Greevenbroek, Coen D A Stehouwer, Carla J H van der Kallen, Casper G Schalkwijk, Cisca Wijmenga, Alexandra Zhernakova, Ettje F Tigchelaar, P. Eline Slagboom, Marian Beekman, Joris Deelen, Diana van HeemstJan H Veldink, Leonard H van den Berg, Cornelia M van Duijn, Bert A. Hofman, Andre 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, Dasha V. Zhernakova, René Luijk, Marc Jan Bonder, Freerk van Dijk, Wibowo Arindrarto, P. Szymon M. Kielbasa, Morris a. Swertz, Erik W van Zwet, BIOS Consortium

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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.

Original languageEnglish
Pages (from-to)180-186
Number of pages7
JournalNature Genetics
Volume51
Issue number1
Early online date26 Nov 2018
DOIs
Publication statusPublished - Jan 2019

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