The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood

Peter M. Visscher; Beben Benyamin; Ian White

doi:10.1375/1369052042663742

The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood

Peter M. Visscher, Beben Benyamin, Ian White

Research output: Contribution to journal › Article › peer-review

35 Citations (Scopus)

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.

Original language	English
Pages (from-to)	670-674
Number of pages	5
Journal	Twin Research
Volume	7
Issue number	6
DOIs	https://doi.org/10.1375/1369052042663742
Publication status	Published or Issued - Dec 2004
Externally published	Yes

ASJC Scopus subject areas

Pediatrics, Perinatology, and Child Health
Obstetrics and Gynaecology
Genetics(clinical)

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10.1375/1369052042663742

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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.",

author = "Visscher, {Peter M.} and Beben Benyamin and Ian White",

note = "Funding Information: PMV thanks the UK Biotechnology and Biological Research Council for financial support. We thank Karoline Schousboe for discussions (in Melbourne) that led to this study and Kirsten Ohm-Kyvik and Thorkild Sorensen for continued support. We thank Robin Thompson, Arthur Gilmour and Mike Goddard for helpful discussions and suggestions on how to parameterize {\textquoteleft}clones{\textquoteright} in variance component estimation software. We thank Shaun Purcell for useful comments.",

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doi = "10.1375/1369052042663742",

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AU - Benyamin, Beben

AU - White, Ian

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The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood

Abstract

ASJC Scopus subject areas

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