title: The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood.
abstract: It is shown that maximum likelihood estimation of variance components from twin data can be parameterized in the framework of linear mixed models. Standard statistical packages can be used to analyze univariate or multivariate data for simple models such as the ACE and CE models. Furthermore, specialized variance component estimation software that can handle pedigree data and user-defined covariance structures can be used to analyze multivariate data for simple and complex models, including those where dominance and/or QTL effects are fitted. The linear mixed model framework is particularly useful for analyzing multiple traits in extended (twin) families with a large number of random effects.
authors.affiliation: Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Scotland, United Kingdom. peter.visscher@ed.ac.uk
authors.forename: Peter M
authors.initials: PM
authors.lastname: Visscher
authors.forename: Beben
authors.initials: B
authors.lastname: Benyamin
authors.forename: Ian
authors.initials: I
authors.lastname: White
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D015999:Multivariate Analysis; D018433:Twin Studies as Topic / Q000706:statistics & numerical data*; D014427:Twins