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Multiple Regression Analysis of Twin Data: Etiology of Deviant Scores versus Individual Differences

Published online by Cambridge University Press:  01 August 2014

J.C. DeFries  and
D.W. Fulker
J.C. DeFries*
Affiliation:
Institute for Behavioral Genetics, University of Colorado, Boulder, USA
D.W. Fulker
Affiliation:
Institute for Behavioral Genetics, University of Colorado, Boulder, USA
*
Institute for Behavioral Genetics, University of Colorado, Boulder, 80309-0447, USA

Abstract

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The multiple regression analysis of twin data in which a cotwin's score is predicted from a proband's score and the coefficient of relationship (the basic model) provides a statistically powerful test of genetic etiology. When an augmented model that also contains an interaction term is fitted to the same data set, direct estimates of heritability (h2) and the proportion of variance due to shared environmental influences (c2) are obtained. A simple transformation of selected twin data prior to regression analysis facilitates direct estimates of h2g (an index of the extent to which the difference between the mean of probands and that of the unselected population is heritable) and a test of the hypothesis that the etiology of deviant scores differs from that of variation within the normal range.

Keywords

Genetic etiologyHeritabilityIndividual differencesMultiple regressionTwins
Type
Research Article
Information
Acta geneticae medicae et gemellologiae: twin research , Volume 37 , Issue 3-4 , October 1988 , pp. 205 - 216
Copyright
Copyright © The International Society for Twin Studies 1988

References

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