TY - JOUR T1 - Gene-environment interactions using a Bayesian whole genome regression model JF - bioRxiv DO - 10.1101/797829 SP - 797829 AU - Matthew Kerin AU - Jonathan Marchini Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/09/797829.abstract N2 - The contribution of gene-environment (GxE) interactions for many human traits and diseases is poorly characterised. We propose a method, LEMMA, that estimates an interpretable environmental score (ES) that interacts with genetic markers throughout the genome. When applied to body mass index, systolic, diastolic and pulse pressure in the UK Biobank we estimate that 9.3%, 3.9%, 1.6% and 12.5% of phenotypic variance is explained by GxE interactions, and that rare variants explain most of this variance. We also identify 3 loci that interact with the estimated environmental scores (−log10 p > 7). ER -