Background: Genotype × environment interaction (G × E) arises when genes influence sensitivity to the environment. G × E is easily recognized in experimental organisms that permit randomization of genotypes over fixed environmental treatments. Genotype–environment correlation (rGE) arises when genetic effects create or evoke exposure to environmental differences. Simultaneous analysis of G × E and such ‘active’ or ‘evocative’ rGE in humans is intractable with linear structural models widely used in behavioral genetics because environments are random effects often correlated with genotype. The causes of the environmental variation, therefore, need to be modeled at the same time as the primary outcome.
Methods: A Markov Chain Monte Carlo approach is used to resolve three distinct pathways involving genes and life events affecting the development of post-pubertal depression in female twins and its relationship to pre-pubertal anxiety: 1) the main of genes and environment; 2) the interaction of genes and environment (G × E); and 3) genotype–environment correlation (rGE).
Results: A model including G × E and rGE in addition to the main effects of genes and environment yields significant estimates of the parameters reflecting G × E and rGE. Omission of either G × E or rGE leads to overestimation of the effects of the measured environment and the unique random environment within families.
Conclusions: 1) Genetic differences in anxiety create later genetic differences in depression; 2) genes that affect early anxiety increase sensitivity (G × E) to adverse life events; 3) genes that increase risk to early anxiety increase exposure to depressogenic environmental influences (rGE). Additional genetic effects, specific to depression, further increase sensitivity to adversity. Failure to take into account the effects of G × E and rGE will lead to misunderstanding how genes and environment affect complex behavior.