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Article: Analytic approaches to twin data using structural equation models.

TitleAnalytic approaches to twin data using structural equation models.
Authors
Issue Date2002
PublisherOxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/
Citation
Briefings In Bioinformatics, 2002, v. 3 n. 2, p. 119-133 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/175864
ISSN
2015 Impact Factor: 8.399
2015 SCImago Journal Rankings: 4.086

 

DC FieldValueLanguage
dc.contributor.authorRijsdijk, FVen_US
dc.contributor.authorSham, PCen_US
dc.date.accessioned2012-11-26T09:01:53Z-
dc.date.available2012-11-26T09:01:53Z-
dc.date.issued2002en_US
dc.identifier.citationBriefings In Bioinformatics, 2002, v. 3 n. 2, p. 119-133en_US
dc.identifier.issn1467-5463en_US
dc.identifier.urihttp://hdl.handle.net/10722/175864-
dc.description.abstractThe 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.en_US
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/en_US
dc.relation.ispartofBriefings in bioinformaticsen_US
dc.subject.meshChi-Square Distributionen_US
dc.subject.meshData Interpretation, Statisticalen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshModels, Geneticen_US
dc.subject.meshMultivariate Analysisen_US
dc.subject.meshSoftwareen_US
dc.subject.meshTwin Studies As Topic - Methodsen_US
dc.titleAnalytic approaches to twin data using structural equation models.en_US
dc.typeArticleen_US
dc.identifier.emailSham, PC: pcsham@hku.hken_US
dc.identifier.authoritySham, PC=rp00459en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.pmid12139432-
dc.identifier.scopuseid_2-s2.0-0036596190en_US
dc.identifier.volume3en_US
dc.identifier.issue2en_US
dc.identifier.spage119en_US
dc.identifier.epage133en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridRijsdijk, FV=6701830835en_US
dc.identifier.scopusauthoridSham, PC=34573429300en_US

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