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Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software
Author(s) -
RabeHesketh S.,
Skrondal A.,
Gjessing H. K.
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00803.x
Subject(s) - categorical variable , mixed model , generalized linear mixed model , linear model , software , computer science , biometrics , random effects model , twin study , variance (accounting) , statistics , data mining , mathematics , heritability , artificial intelligence , biology , genetics , medicine , accounting , business , programming language , meta analysis
Summary Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or ‘phenotype’ into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S‐PLUS are available at the Biometrics website.