RT Journal Article
SR Electronic
T1 Gene-environment interactions using a Bayesian whole genome regression model
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 797829
DO 10.1101/797829
A1 Matthew Kerin
A1 Jonathan Marchini
YR 2019
UL http://biorxiv.org/content/early/2019/10/09/797829.abstract
AB 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).