Analytic approaches to twin data using structural equation models

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ID: 305644
2002
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Abstract
The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these 'latent' factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.
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openalex_W2119947818 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Frühling Rijsdijk
Journal Briefings in bioinformatics
Year 2002
DOI
10.1093/bib/3.2.119
URL
https://doi.org/10.1093/bib/3.2.119
Keywords Keywords not found

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