rGE). The analyses of rGE thus are closely related to issues of gene-environment independence and to questions of causality. The analyses of GxE interaction will employ several approaches that can make use of the large existing datasets. The first approach focuses on the estimation of the total contribution of genes when environmental exposures have been measured. In this approach genotypes and other, non-measured, influences are modelled as latent factors. Because of the presence of genome-wide marker data, a second approach is to estimate GxE interaction in a design with measured genotypes and environmental exposures (note that because of the twin design the remaining variance can still be attributed to latent G and E). The causal relation of environmental exposure and later outcome may be complex, but longitudinal twin data offer excellent opportunities to test models of causality versus other models of association between genes and environment [46, 47].
