Variance Components Models for Gene–Environment Interaction in Twin Analysis

TLDR

Gene‑environment interaction is a common and important source of variation for complex behavioral traits. The study aims to show that accounting for gene‑environment interaction can guide and improve gene‑mapping efforts. They extend variance‑components twin models by partitioning genetic effects into a mean part and an environment‑dependent part, allowing for continuous or binary moderators, within‑pair differences, interactions with residual effects, nonlinear, scalar or qualitative interactions, and correlation with genetic effects to test gene‑environment interaction. Simulations demonstrate the utility of these models for individual‑differences twin analysis and for sib‑pair QTL linkage analysis.

Abstract

Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.