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Distinguishing differential susceptibility, diathesis-stress, and vantage sensitivity: Beyond the single gene and environment model / Alexia JOLICOEUR-MARTINEAU in Development and Psychopathology, 32-1 (February 2020)
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Titre : Distinguishing differential susceptibility, diathesis-stress, and vantage sensitivity: Beyond the single gene and environment model Type de document : Texte imprimé et/ou numérique Auteurs : Alexia JOLICOEUR-MARTINEAU, Auteur ; Jay BELSKY, Auteur ; Eszter SZEKELY, Auteur ; Keith F. WIDAMAN, Auteur ; Michael PLUESS, Auteur ; Celia M. T. GREENWOOD, Auteur ; Ashley WAZANA, Auteur Article en page(s) : p.73-83 Langues : Anglais (eng) Mots-clés : diathesis-stress differential-susceptibility gene-by-environment interaction regions of significance vantage sensitivity Index. décimale : PER Périodiques Résumé : Currently, two main approaches exist to distinguish differential susceptibility from diathesis-stress and vantage sensitivity in Genotype x Environment interaction (G x 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 x E interaction models with latent genetic and environmental scores using alternating optimization. Herein we test, by means of computer simulation, diverse G x 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 x E of multiple genes and environments are provided as extensions in our LEGIT R package. En ligne : http://dx.doi.org/10.1017/s0954579418001438 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=415
in Development and Psychopathology > 32-1 (February 2020) . - p.73-83[article] Distinguishing differential susceptibility, diathesis-stress, and vantage sensitivity: Beyond the single gene and environment model [Texte imprimé et/ou numérique] / Alexia JOLICOEUR-MARTINEAU, Auteur ; Jay BELSKY, Auteur ; Eszter SZEKELY, Auteur ; Keith F. WIDAMAN, Auteur ; Michael PLUESS, Auteur ; Celia M. T. GREENWOOD, Auteur ; Ashley WAZANA, Auteur . - p.73-83.
Langues : Anglais (eng)
in Development and Psychopathology > 32-1 (February 2020) . - p.73-83
Mots-clés : diathesis-stress differential-susceptibility gene-by-environment interaction regions of significance vantage sensitivity Index. décimale : PER Périodiques Résumé : Currently, two main approaches exist to distinguish differential susceptibility from diathesis-stress and vantage sensitivity in Genotype x Environment interaction (G x 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 x E interaction models with latent genetic and environmental scores using alternating optimization. Herein we test, by means of computer simulation, diverse G x 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 x E of multiple genes and environments are provided as extensions in our LEGIT R package. En ligne : http://dx.doi.org/10.1017/s0954579418001438 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=415