Distinguishing differential susceptibility, diathesis-stress, and vantage sensitivity: Beyond the single gene and environment model. (February 2020)

• Record Type: Journal Article
• Authors: Jolicoeur-Martineau, Alexia; Belsky, Jay; Szekely, Eszter; Widaman, Keith F.; Pluess, Michael; Greenwood, Celia; Wazana, Ashley
• Is Part Of: Development and psychopathology. Volume 32:Number 1(2020)
• Page Start: 73
• Page End: 83
• Publication Date: 2020-02
• Subjects: diathesis-stress, -- differential-susceptibility, -- gene-by-environment interaction, -- regions of significance, -- vantage sensitivity; Child psychopathology -- Periodicals; Developmental psychology -- Periodicals; Psychology, Pathological -- Periodicals; 618.9289
• DOI: 10.1017/S0954579418001438
• Languages: English
• ISSNs: 0954-5794

Abstract

Abstract: 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.