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Extensions to multivariate normal models for pedigree analysis

J L Hopper, J D Mathews

    Annals of Human Genetics
    |October 1, 1982
    PubMed
    Summary
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    This study introduces new methods for analyzing quantitative genetic traits in families, including tests for normality and outlier detection. These advancements improve the accuracy of genetic marker and family environment effect estimations.

    Area of Science:

    • Quantitative genetics
    • Statistical genetics
    • Biostatistics

    Background:

    • The analysis of quantitative traits in pedigrees has historically relied on assumptions of multivariate normality.
    • Previous methods, like those by Lange, Westlake & Spence (1976), utilized likelihood approaches under these assumptions.

    Purpose of the Study:

    • To develop and validate novel statistical methods for the analysis of quantitative traits within family structures.
    • To enhance the robustness of pedigree analysis by addressing the assumption of multivariate normality and identifying potential data anomalies.

    Main Methods:

    • Introduction of a statistical test to assess the assumption of multivariate normality in pedigree data.
    • Development of methods for detecting outlying families and individuals.

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  • Implementation of variance component estimation for measured genetic marker effects.
  • Introduction of a flexible parameterization for estimating shared family environment effects.
  • Inclusion of a method to account for pedigree ascertainment through probands.
  • Application of numerical methods for likelihood maximization.

    • Main Results:

    • The developed methods provide tools for testing multivariate normality and identifying outliers in pedigree data.
    • New approaches allow for the estimation of genetic marker effects and shared family environment effects.
    • The likelihood ratio criterion for significance testing demonstrates expected asymptotic distribution in typical sample sizes, supported by simulations and theory.

    Conclusions:

    • The introduced innovations offer a more comprehensive and robust framework for the statistical genetic analysis of quantitative traits in pedigrees.
    • These methods improve the reliability of genetic and environmental effect estimations, even with potential deviations from normality or the presence of outliers.