Smooth-threshold multivariate genetic prediction incorporating gene-environment interactions.

ABSTRACT: We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene-environment (GxE) interaction effects-i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automat

SUBMITTER: Ueki M 

PROVIDER: S-EPMC8664495 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature