Analytic approaches to twin data using structural equation models

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 compone...

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Bibliographic Details
Published in:Briefings in bioinformatics 2002-06, Vol.3 (2), p.119-133
Main Authors: RIJSDIJK, Frühling V, SHAM, Pak C
Format: Article
Language:English
Subjects:
Behavior
Bioinformatics
Biological and medical sciences
Chi-Square Distribution
Data Interpretation, Statistical
Fundamental and applied biological sciences. Psychology
General aspects
Genetics
Humans
Likelihood Functions
Mathematical models
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Multivariate Analysis
Software
Twin Studies as Topic - methods
Twins
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Summary: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.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/3.2.119