Behavior Genetics

, Volume 22, Issue 4, pp 489–497

Multiple regression analysis of twin data: A model-fitting approach

Authors

  • S. S. Cherny
    • Institute for Behavioral GeneticsUniversity of Colorado
  • J. C. DeFries
    • Institute for Behavioral GeneticsUniversity of Colorado
  • D. W. Fulker
    • Institute for Behavioral GeneticsUniversity of Colorado
Article

DOI: 10.1007/BF01066617

Cite this article as:
Cherny, S.S., DeFries, J.C. & Fulker, D.W. Behav Genet (1992) 22: 489. doi:10.1007/BF01066617

Abstract

The multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting theh2 andc2 terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.

Key Words

multiple regressiontwinsmodel-fittingheritability
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Copyright information

© Plenum Publishing Corporation 1992