Twin data analysis with ACE-decomposed explanatory variables using Stata

Several authors have introduced different methods for decomposing the variance of a variable into an additive genetic (A), a shared environmental (C), and a unique environmental (E) component using twin data and multilevel mixed-effects (MME) models; Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh, Skrondel, and Gjessing 2008, who used Stata). In recent years, the focus of behavioral genetic research has increasingly shifted toward analyzing the causal influence of these genetic and environmental components of traits on the development of inequalities. Regarding methods, this implies estimating the effects of ACE components, that is, estimating models with ACE-decomposed explanatory variables. This presentation compares different MME implementations of such models using the meglm and the gsem packages of Stata: A bivariate ACE decomposition (McArdle and Prescott 2005), a one step-estimator for the ACE decomposition and its effects, and a more flexible two-step estimator based on plausible values for the ACE components. Conceptually, these models are extensions of hybrid MME models (Allison 2009), which replace the within-between-group-decomposition of explanatory variables with an ACE-decomposition. To demonstrate how these models facilitate the causal analyses of inequalities, the presentation uses examples based on data of TwinLife, the new German twin family panel.