Analytic approaches to twin data using structural equation models
Lecturer in Quantitative Behavioral Genetics at the Social, Genetic & Development Psychiatry Research Centre of the Institute of Psychiatry, King's College London. She works on the development of statistical methods and computer software for gene-mapping in quantitative traits and the development of structural equation models for (selected) family and twin data.
Professor of Psychiatric and Statistical Genetics at the Institute of Psychiatry, King's College London. He works on the development of statistical and computational methods for gene mapping in complex disorders, and on the application of these methods to psychiatric conditions such as schizophrenia and affective disorders.
F. V. Rijsdijk, SGDP Centre, Institute of Psychiatry, 111 Denmark Hill, London SE5 8AF, UK Tel: +44 (0) 207 848 0890 Fax: +44 (0) 207 848 0866 e-mail: f.rijsdijk{at}iop.kcl.ac.uk
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.
Keywords: classical twin study, behavioural genetics, Mx, SEM, genetic model fitting
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