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Multifactorial genetic models for quantitative traits in humans.

C R Boyle, R C Elston

    Biometrics
    |March 1, 1979
    PubMed
    Summary
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    This study reviews methods for analyzing human family data to understand genetic and environmental influences on traits. It details models for estimating variance components and genetic mechanisms using pedigree analysis and likelihood methods.

    Area of Science:

    • Biometric analysis
    • Human genetics
    • Quantitative genetics

    Background:

    • Quantitative traits in human families can reveal genetic and environmental influences.
    • Analysis of variance components and genetic mechanisms is crucial for understanding trait inheritance.

    Purpose of the Study:

    • To review methods for estimating variance components from human pedigree data.
    • To describe mixed models for elucidating genetic mechanisms, including major gene and polygenic effects.
    • To present a general model incorporating familial environmental variation, assortative mating, and genetic effects.

    Main Methods:

    • Estimation of variance components directly from human pedigree data.
    • Use of path coefficients from correlations between relatives.

    Related Experiment Videos

  • Application of mixed models with likelihood derivations for parameter estimation and hypothesis testing.
  • Development of an algorithm for calculating pedigree likelihoods under a general model.

    • Main Results:

    • Established methods for partitioning population variance into genetic and environmental components.
    • Described a mixed model for nuclear families accounting for major locus, polygenic effects, and sibling environmental correlation.
    • Presented a general model accommodating multiple familial environmental sources, assortative mating, and complex genetic effects.

    Conclusions:

    • Biometric analysis of human family data provides powerful tools for dissecting trait etiology.
    • Advanced statistical models and computational algorithms are essential for accurate parameter estimation and hypothesis testing.
    • Further research is needed to address remaining challenges in biometric analysis.