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Currently, two main approaches exist to distinguish differential susceptibility from diathesis-stress and vantage sensitivity in Genotype × Environment interaction (G × E) research: regions of significance (RoS) and competitive-confirmatory approaches. Each is limited by its single-gene/single-environment foci given that most phenotypes are the product of multiple interacting genetic and environmental factors. We thus addressed these two concerns in a recently developed R package (LEGIT) for constructing G × E interaction models with latent genetic and environmental scores using alternating optimization. Herein we test, by means of computer simulation, diverse G × E models in the context of both single and multiple genes and environments. Results indicate that the RoS and competitive-confirmatory approaches were highly accurate when the sample size was large, whereas the latter performed better in small samples and for small effect sizes. The competitive-confirmatory approach generally had good accuracy (a) when effect size was moderate and N ≥ 500 and (b) when effect size was large and N ≥ 250, whereas RoS performed poorly. Computational tools to determine the type of G × E of multiple genes and environments are provided as extensions in our LEGIT R package.

Distinguishing differential susceptibility, diathesis-stress, and vantage sensitivity: Beyond the single gene and environment model