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

Published online by Cambridge University Press:  10 January 2019

Alexia Jolicoeur-Martineau
Affiliation:
Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Qc, Canada
Jay Belsky
Affiliation:
Department of Human Ecology, University of California, Davis, USA
Eszter Szekely
Affiliation:
Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Qc, Canada Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, Canada
Keith F. Widaman
Affiliation:
Graduate School of Education, University of California, Riverside, USA
Michael Pluess
Affiliation:
Department of Biological and Experimental Psychology, Queen Mary University of London, UK
Celia Greenwood
Affiliation:
Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Qc, Canada Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Qc, Canada
Ashley Wazana*
Affiliation:
Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Qc, Canada Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, Canada Douglas Mental Health University Institute, Montreal, Qc, Canada.
*
Author for correspondence: Ashley Wazana, Centre for Child Development and Mental Health, Jewish General Hospital, 4335 Cote Sainte Catherine Road, Montreal, Quebec, H3T 1E4Montreal, Quebec, Canada; E-mail: ashley.wazana@mcgill.ca.

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.

Type
Regular Articles
Copyright
Copyright © Cambridge University Press 2019

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