Centre d'Information et de documentation du CRA Rhône-Alpes
CRA
Informations pratiques
-
Adresse
Centre d'information et de documentation
du CRA Rhône-Alpes
Centre Hospitalier le Vinatier
bât 211
95, Bd Pinel
69678 Bron CedexHoraires
Lundi au Vendredi
9h00-12h00 13h30-16h00Contact
Tél: +33(0)4 37 91 54 65
Mail
Fax: +33(0)4 37 91 54 37
-
Détail de l'auteur
Auteur Felix THOEMMES |
Documents disponibles écrits par cet auteur (1)
Faire une suggestion Affiner la recherche
What is the biological reality of gene–environment interaction estimates? An assessment of bias in developmental models / Sarah R. MOORE in Journal of Child Psychology and Psychiatry, 57-11 (November 2016)
[article]
Titre : What is the biological reality of gene–environment interaction estimates? An assessment of bias in developmental models Type de document : Texte imprimé et/ou numérique Auteurs : Sarah R. MOORE, Auteur ; Felix THOEMMES, Auteur Article en page(s) : p.1258-1267 Langues : Anglais (eng) Mots-clés : Gene–environment interaction methodology child development neural development molecular genetics Index. décimale : PER Périodiques Résumé : Background Standard models used to test gene–environment interaction (G × E) hypotheses make the causal assumption that there are no unobserved variables that could be biasing the interaction estimate. Whether this assumption can be met in nonexperimental studies is unclear because the interactive biological pathways from genetic polymorphisms and environments to behavior, and the confounders that can be introduced along these pathways, are often not delineated. This is problematic in the context of studies focused on caregiver–child dyads, in which common genes and environments induce gene–environment correlation. To address the impact of sources of bias in G × E models specifically assessing the interaction between child genotype and caregiver behavior, we provide a causal framework that integrates biological and statistical concepts of G × E, and assess the magnitude of bias introduced by various confounding pathways in different causal circumstances. Methods A simulation assessed the magnitude of bias introduced by four types of confounding pathways in different causal models. Unadjusted and adjusted statistical models were then applied to the simulated data to assess the efficacy of these procedures to capture unbiased G × E estimates. Finally, the simulation was run under null effects of the genotype to assess the impact of biasing sources on the false-positive rate. Results Common environmental pathways between caregiver and child inflated G × E estimates and raised the false-positive rate. Evocative effects of the child also inflated G × E estimates. Conclusions Gene–environment interaction studies should be approached with consideration to the causal pathways at play and the confounding opportunities along these pathways to facilitate the inclusion of adequate statistical controls and correct inferences from study findings. Bridging biological and statistical concepts of G × E can significantly improve research design and the communication of how a G × E process fits into a broader developmental framework. En ligne : http://dx.doi.org/10.1111/jcpp.12579 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=295
in Journal of Child Psychology and Psychiatry > 57-11 (November 2016) . - p.1258-1267[article] What is the biological reality of gene–environment interaction estimates? An assessment of bias in developmental models [Texte imprimé et/ou numérique] / Sarah R. MOORE, Auteur ; Felix THOEMMES, Auteur . - p.1258-1267.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 57-11 (November 2016) . - p.1258-1267
Mots-clés : Gene–environment interaction methodology child development neural development molecular genetics Index. décimale : PER Périodiques Résumé : Background Standard models used to test gene–environment interaction (G × E) hypotheses make the causal assumption that there are no unobserved variables that could be biasing the interaction estimate. Whether this assumption can be met in nonexperimental studies is unclear because the interactive biological pathways from genetic polymorphisms and environments to behavior, and the confounders that can be introduced along these pathways, are often not delineated. This is problematic in the context of studies focused on caregiver–child dyads, in which common genes and environments induce gene–environment correlation. To address the impact of sources of bias in G × E models specifically assessing the interaction between child genotype and caregiver behavior, we provide a causal framework that integrates biological and statistical concepts of G × E, and assess the magnitude of bias introduced by various confounding pathways in different causal circumstances. Methods A simulation assessed the magnitude of bias introduced by four types of confounding pathways in different causal models. Unadjusted and adjusted statistical models were then applied to the simulated data to assess the efficacy of these procedures to capture unbiased G × E estimates. Finally, the simulation was run under null effects of the genotype to assess the impact of biasing sources on the false-positive rate. Results Common environmental pathways between caregiver and child inflated G × E estimates and raised the false-positive rate. Evocative effects of the child also inflated G × E estimates. Conclusions Gene–environment interaction studies should be approached with consideration to the causal pathways at play and the confounding opportunities along these pathways to facilitate the inclusion of adequate statistical controls and correct inferences from study findings. Bridging biological and statistical concepts of G × E can significantly improve research design and the communication of how a G × E process fits into a broader developmental framework. En ligne : http://dx.doi.org/10.1111/jcpp.12579 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=295