In the first part of this session, basic terms and concepts of behavioral genetics such as types of gene-environment correlations and gene-environment interactions (G × E) will be introduced. The second part gives an overview on the TwinLife study design and useful tips for working with its data, e.g. using relevant documents (shortguide, codebooks, longitudinal overview).

Using twin data, genetic and shared environmental confounders can be controlled. In this session, the approach of the co-twin control design will be further explained, including two different types of models (fixed-effects models and mixed-effects models).

In this session, the classical univariate ACE model is covered, including its assumptions, and how to implement it in R (data preparation, useful packages and codes). ACE models are used to decompose the variance in traits into genetic, shared environmental, and unique environmental components.

This session presents the bivariate twin model, extending the basic ACE concept to two or more related traits. These models allow for examining how genetic and environmental factors contribute to the covariation between traits.

The first session reviews the content of the basic workshop: uni- and bivariate ACE models and their implementation in R.

The second session introduces the ACE-beta model, which allows for the modeling of genetic and shared environmental confounders while modeling quasi-causal effects. Its implementation in R is discussed.

This session expands on the basic forms of gene-environment interplay: Gene-Environment Correlation (rGE) and Gene-Environment Interaction (GxE). It includes applied examples as well as the Purcell Modell, a variance components model for GxE, and its implementation in R.