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Multiple regression analysis of twin data obtained from selected samples.

M C LaBuda, J C DeFries, D W Fulker

    Genetic Epidemiology
    |January 1, 1986
    PubMed
    Summary
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    This study introduces a flexible multiple regression analysis for twin data, offering a powerful method to assess genetic and environmental influences on traits. The approach provides direct heritability (h2) and shared environment (c2) estimates, proving useful for various research applications.

    Area of Science:

    • Behavioral Genetics
    • Quantitative Genetics
    • Twin Studies

    Background:

    • Traditional model-fitting approaches for twin data analysis can be complex.
    • Assessing genetic and environmental contributions to phenotypes requires robust statistical methods.

    Purpose of the Study:

    • To present a flexible multiple regression analysis for twin data.
    • To derive expected partial regression coefficients for genetic etiology testing.
    • To introduce an extended model for analyzing combined affected and control twin pair samples.

    Main Methods:

    • Multiple regression analysis of twin data, including proband scores and coefficient of relationship.
    • Fitting an augmented model with an interaction term for heritability (h2) and shared environment (c2) estimates.

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  • Formulating an extended model for differential heritability and shared environment analysis in distinct twin groups.

    • Main Results:

    • The multiple regression approach provides a powerful test of genetic etiology.
    • Direct estimates of heritability (h2) and shared environmental influences (c2) are obtained.
    • The extended model allows for tests of differential h2 and c2 in affected and control groups.

    Conclusions:

    • Multiple regression analysis of twin data is a flexible and powerful alternative to traditional methods.
    • This approach facilitates the estimation of genetic and environmental parameters.
    • The method is applicable to diverse twin study designs, including those with clinical populations.