The paper discusses the application of variance components models to analyze gene-environment interaction (G×E) in twin studies. G×E refers to the genetic control of sensitivity to environmental factors, which can be incorporated into twin analyses by partitioning genetic effects into mean and environment-dependent parts. The model allows for one or more environmental moderator variables that can be continuous or binary, interact with each other, and have nonlinear moderating properties. The paper explores the use of these models through simulations in both individual differences twin analysis and sibpair quantitative trait locus linkage analysis. It highlights the importance of considering G×E in quantitative and molecular studies to enhance gene-mapping efforts.
The paper also addresses the challenges of detecting G×E within various study designs, such as using latent or measured variables for G and E. It discusses the limitations of stratifying samples by environmental moderators and emphasizes the need for more powerful methods. The authors propose a model that can detect G×E with continuous moderators, which is most relevant to classical twin studies. They provide a detailed framework for analyzing G×E in the context of twin studies, including the estimation of parameters and the visualization of variance components.
Additionally, the paper explores the impact of gene-environment correlation (rGE) on G×E analysis. It shows how the current model can discriminate between G×E and rGE, allowing for the detection of G×E in the presence of rGE. The simulations demonstrate the robustness of the model in recovering true parameter values and detecting interactions, even in the presence of rGE. The paper concludes by discussing the limitations and potential improvements of the model, emphasizing the need for careful interpretation of results.The paper discusses the application of variance components models to analyze gene-environment interaction (G×E) in twin studies. G×E refers to the genetic control of sensitivity to environmental factors, which can be incorporated into twin analyses by partitioning genetic effects into mean and environment-dependent parts. The model allows for one or more environmental moderator variables that can be continuous or binary, interact with each other, and have nonlinear moderating properties. The paper explores the use of these models through simulations in both individual differences twin analysis and sibpair quantitative trait locus linkage analysis. It highlights the importance of considering G×E in quantitative and molecular studies to enhance gene-mapping efforts.
The paper also addresses the challenges of detecting G×E within various study designs, such as using latent or measured variables for G and E. It discusses the limitations of stratifying samples by environmental moderators and emphasizes the need for more powerful methods. The authors propose a model that can detect G×E with continuous moderators, which is most relevant to classical twin studies. They provide a detailed framework for analyzing G×E in the context of twin studies, including the estimation of parameters and the visualization of variance components.
Additionally, the paper explores the impact of gene-environment correlation (rGE) on G×E analysis. It shows how the current model can discriminate between G×E and rGE, allowing for the detection of G×E in the presence of rGE. The simulations demonstrate the robustness of the model in recovering true parameter values and detecting interactions, even in the presence of rGE. The paper concludes by discussing the limitations and potential improvements of the model, emphasizing the need for careful interpretation of results.