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A general multivariate approach to linear modeling in human genetics.

G Carey

    American Journal of Human Genetics
    |December 1, 1986
    PubMed
    Summary
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    This study introduces conditional associations to extend linear models for human genetics. This approach handles complex family structures and multiple traits, simplifying genetic analyses.

    Area of Science:

    • Human Genetics
    • Biostatistics
    • Quantitative Genetics

    Background:

    • Traditional linear models face challenges with multiple phenotypes per individual in human genetics.
    • Assortment between mates and assortative placement of adoptees introduce complexities in genetic modeling.

    Purpose of the Study:

    • To extend the general linear model for human genetic analyses with multiple measured phenotypes.
    • To develop a novel modeling convention, conditional associations, to address complexities from mating and adoption patterns.
    • To provide a unified framework for linear modeling in human genetics.

    Main Methods:

    • Application of the general linear structural equation model.
    • Development of the conditional associations modeling convention.

    Related Experiment Videos

  • Derivation of formulas for expected covariance matrices under assortment and assortative placement.
  • Reduction of all linear models in human genetics to two fundamental equations.

    • Main Results:

    • The conditional associations convention effectively extends the general linear model for human genetic data.
    • Formulas are provided for simultaneous covariance matrix generation with multiple variables.
    • All linear genetic models can be simplified to two core equations.
    • A practical algorithm is presented for applying these equations in linear modeling.

    Conclusions:

    • The developed methods provide a robust framework for analyzing complex human genetic data with multiple phenotypes.
    • Conditional associations offer a powerful tool for researchers studying the genetic basis of traits influenced by family structure.
    • The unification of linear models simplifies complex genetic analyses and facilitates broader application.