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Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3350767/ https://www.ncbi.nlm.nih.gov/pubmed/22146987 http://dx.doi.org/10.1007/s10519-011-9522-x |
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author | Molenaar, Dylan van der Sluis, Sophie Boomsma, Dorret I. Dolan, Conor V. |
author_facet | Molenaar, Dylan van der Sluis, Sophie Boomsma, Dorret I. Dolan, Conor V. |
author_sort | Molenaar, Dylan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3350767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-33507672012-05-30 Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design Molenaar, Dylan van der Sluis, Sophie Boomsma, Dorret I. Dolan, Conor V. Behav Genet Original Research 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. Springer US 2011-12-07 2012 /pmc/articles/PMC3350767/ /pubmed/22146987 http://dx.doi.org/10.1007/s10519-011-9522-x Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
spellingShingle | Original Research Molenaar, Dylan van der Sluis, Sophie Boomsma, Dorret I. Dolan, Conor V. Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title | Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title_full | Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title_fullStr | Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title_full_unstemmed | Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title_short | Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design |
title_sort | detecting specific genotype by environment interactions using marginal maximum likelihood estimation in the classical twin design |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3350767/ https://www.ncbi.nlm.nih.gov/pubmed/22146987 http://dx.doi.org/10.1007/s10519-011-9522-x |
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