This paper discusses variance components models for gene-environment interaction in twin analysis. Gene-environment interaction (G×E) is a common and important source of variation for complex behavioral traits. It can be incorporated into variance components twin analyses by partitioning genetic effects into a mean part and a part that is a linear function of the environment. The model allows for various environmental moderator variables that may interact with genetic and environmental effects, and can be used to test gene-environment interaction in the presence of gene-environment correlation. The paper explores the application of these models in both individual differences twin analysis and sibpair quantitative trait locus linkage analysis. The paper also discusses the implications of G×E for quantitative and molecular studies, and how it can direct and enhance gene-mapping efforts. It highlights the importance of considering G×E in twin analysis to distinguish between different scenarios of genetic and environmental influences on traits. The paper also discusses the impact of G×E on standard twin models, and how it can lead to biased parameter estimates. The paper presents a basic G×E model involving a continuous moderator variable that can interact with latent genetic and environmental effects. It also discusses nonlinear G×E using a quadratic approximation, and G×E in the presence of gene-environment correlation. The paper also discusses scalar and qualitative interactions, and the impact of distributional factors on G×E analysis. The paper presents an example of a normally-distributed trait with A, C, and E components representing 25%, 25%, and 50% of the trait variance respectively. It also discusses the simulation of data under different models, and the results of these simulations. The paper also discusses the performance of the basic G×E model in the presence of gene-environment correlation, and how it can distinguish between different types of interaction. The paper concludes that variance components models for G×E in twin analysis are a useful tool for understanding the complex interactions between genes and the environment. These models can help to identify the genetic and environmental factors that contribute to complex traits, and can provide insights into the underlying biology of these traits. The paper also highlights the importance of considering G×E in twin analysis to distinguish between different scenarios of genetic and environmental influences on traits.This paper discusses variance components models for gene-environment interaction in twin analysis. Gene-environment interaction (G×E) is a common and important source of variation for complex behavioral traits. It can be incorporated into variance components twin analyses by partitioning genetic effects into a mean part and a part that is a linear function of the environment. The model allows for various environmental moderator variables that may interact with genetic and environmental effects, and can be used to test gene-environment interaction in the presence of gene-environment correlation. The paper explores the application of these models in both individual differences twin analysis and sibpair quantitative trait locus linkage analysis. The paper also discusses the implications of G×E for quantitative and molecular studies, and how it can direct and enhance gene-mapping efforts. It highlights the importance of considering G×E in twin analysis to distinguish between different scenarios of genetic and environmental influences on traits. The paper also discusses the impact of G×E on standard twin models, and how it can lead to biased parameter estimates. The paper presents a basic G×E model involving a continuous moderator variable that can interact with latent genetic and environmental effects. It also discusses nonlinear G×E using a quadratic approximation, and G×E in the presence of gene-environment correlation. The paper also discusses scalar and qualitative interactions, and the impact of distributional factors on G×E analysis. The paper presents an example of a normally-distributed trait with A, C, and E components representing 25%, 25%, and 50% of the trait variance respectively. It also discusses the simulation of data under different models, and the results of these simulations. The paper also discusses the performance of the basic G×E model in the presence of gene-environment correlation, and how it can distinguish between different types of interaction. The paper concludes that variance components models for G×E in twin analysis are a useful tool for understanding the complex interactions between genes and the environment. These models can help to identify the genetic and environmental factors that contribute to complex traits, and can provide insights into the underlying biology of these traits. The paper also highlights the importance of considering G×E in twin analysis to distinguish between different scenarios of genetic and environmental influences on traits.