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

Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Lude Franke, Inês Barroso, Oliver Stegle, BIOS Consortium

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.
Original languageEnglish
Pages (from-to)180-186
Number of pages7
JournalNature Genetics
Volume51
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Cite this

Moore, R., Casale, F. P., Jan Bonder, M., Horta, D., Franke, L., Barroso, I., ... BIOS Consortium (2019). A linear mixed-model approach to study multivariate gene–environment interactions. Nature Genetics, 51(1), 180-186. https://doi.org/10.1038/s41588-018-0271-0