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 theh 2 andc 2 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.
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This research was supported in part by NICHD Grants HD-11681 and HD-27802. Analyses of the data were facilitated by BRSG Grant RR-07013-25 awarded to the University of Colorado by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health. The article was written while the first author was supported in part by the Natural Sciences and Engineering Research Council of Canada.
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Cherny, S.S., DeFries, J.C. & Fulker, D.W. Multiple regression analysis of twin data: A model-fitting approach. Behav Genet 22, 489–497 (1992). https://doi.org/10.1007/BF01066617
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DOI: https://doi.org/10.1007/BF01066617