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Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.
Domingue, Benjamin W; Kanopka, Klint; Mallard, Travis T; Trejo, Sam; Tucker-Drob, Elliot M.
Afiliação
  • Domingue BW; Graduate School of Education, Stanford University and Center for Population Health Sciences, Stanford Medicine, Stanford, USA. bdomingu@stanford.edu.
  • Kanopka K; Graduate School of Education, Stanford University, Stanford, USA.
  • Mallard TT; Department of Psychology, University of Texas at Austin, Austin, USA.
  • Trejo S; Department of Sociology and Office of Population Research, Princeton University, Princeton, USA.
  • Tucker-Drob EM; Department of Psychology and Population Research Center, University of Texas at Austin, Austin, USA. tuckerdrob@utexas.edu.
Behav Genet ; 52(1): 56-64, 2022 01.
Article em En | MEDLINE | ID: mdl-34855050
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Interação Gene-Ambiente Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Behav Genet Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Herança Multifatorial / Interação Gene-Ambiente Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Behav Genet Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos