Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model.

ABSTRACT: The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effe

SUBMITTER: Kerin M 

PROVIDER: S-EPMC7536582 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature