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

• Published in: Nature genetics Vol. 51; no. 1; pp. 180 - 186
• Main Authors: Moore, Rachel, Casale, Francesco Paolo, Jan Bonder, Marc, Horta, Danilo, Franke, Lude, Barroso, Inês, Stegle, Oliver
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2019; Nature Publishing Group
• Subjects: 631/208/191; 631/208/205; 639/705/531; Agriculture; Algorithms; Animal Genetics and Genomics; Biomedical and Life Sciences; Biomedicine; Body mass index; Body size; Cancer Research; Computer Simulation; Data processing; Disease; Environment; Environmental impact analysis; Exercise; Gene expression; Gene Function; Gene-Environment Interaction; Genetic variation; Genomes; Genotype; Genotype-environment interactions; Genotypes; Human Genetics; Humans; Linear Models; Linear models (Statistics); Loci; Models, Genetic; Obesity; Physical activity; Physical fitness; Population; Power; Principal components analysis; Quantitative Trait Loci - genetics; technical-report; United Kingdom > UK
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/s41588-018-0271-0

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. StructLMM is a new method to identify genotype–environment interactions (G×E) that involve multiple exposures or environments. When applied to UK Biobank and eQTL data, StructLMM discovers new G×E signals.