ArticleBehavior genetics2012
Detecting specific genotype by environment interactions using marginal maximum likelihood estimation in the classical twin design.
Article in Behavior genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bias in Gene-by-Environment Interaction Effects with Sum Scores; An Application to Well-being Phenotypes.Behavior genetics · 2023Article
- Maximizing the value of twin studies in health and behaviour.Nature human behaviour · 2023Review
- Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®.Behavior genetics · 2023Article
- Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network.European child & adolescent psychiatry · 2022Review
- Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A-C Covariance.Behavior genetics · 2021Article
- Genes, Culture and Conservatism-A Psychometric-Genetic Approach.Behavior genetics · 2016Article
- Evidence for Gender-Dependent Genotype by Environment Interaction in Adult Depression.Behavior genetics · 2016Article
- Heteroscedastic Latent Trait Models for Dichotomous Data.Psychometrika · 2015Article
- Latent classiness and other mixtures.Behavior genetics · 2014Article
- Genotype by environment interactions in cognitive ability: a survey of 14 studies from four countries covering four age groups.Behavior genetics · 2013Article
- The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions.Psychometrika · 2012Article
- Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect.Multivariate behavioral researchArticle
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Authors and funding
4 authors.
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Abstract
Considerable effort has been devoted to the analysis of genotype by environment (G × E) interactions in various phenotypic domains, such as cognitive abilities and personality. In many studies, environmental variables were observed (measured) variables. In case of an unmeasured environment, van der Sluis et al. (2006) proposed to study heteroscedasticity in the factor model using only MZ twin data. This method is closely related to the Jinks and Fulker (1970) test for G × E, but slightly more powerful. In this paper, we identify four challenges to the investigation of G × E in general, and specifically to the heteroscedasticity approaches of Jinks and Fulker and van der Sluis et al. We propose extensions of these approaches purported to solve these problems. These extensions comprise: (1) including DZ twin data, (2) modeling both A × E and A × C interactions; and (3) extending the univariate approach to a multivariate approach. By means of simulations, we study the power of the univariate method to detect the different G × E interactions in varying situations. In addition, we study how well we could distinguish between A × E, A × C, and C × E. We apply a multivariate version of the extended model to an empirical data set on cognitive abilities.
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