Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software

Authors

  • S. Rabe-Hesketh,

    Corresponding author
    1. Graduate School of Education & Graduate Group in Biostatistics, University of California, Berkeley, California 94720, U.S.A. & Institute of Education, University of London, London WC1H OAL, U.K.
      email:sophiarh@berkeley.edu
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  • A. Skrondal,

    1. Department of Statistics & Methodology Institute, London School of Economics, London, WC2A 2AE, U.K. & Division of Epidemiology, Norwegian Institute of Public Health, 0403 Oslo, Norway
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  • H. K. Gjessing

    1. Division of Epidemiology, Norwegian Institute of Public Health, 0403 Oslo, Norway & Section of Epidemiology and Medical Statistics, University of Bergen, 5020 Bergen, Norway
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email:sophiarh@berkeley.edu

Abstract

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.

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