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Log-linear models for linked loci.

B S Weir, S R Wilson

    Biometrics
    |September 1, 1986
    PubMed
    Summary
    This summary is machine-generated.

    This study clarifies gene association models, differentiating between multiplicative and additive approaches. Including three-gene interactions significantly improves model accuracy for genetic association analysis.

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    Area of Science:

    • Genetics
    • Biostatistics

    Background:

    • Gene association studies often employ multiplicative or additive models to analyze relationships between genes at different loci.
    • Distinguishing between intergametic and intragametic linkage disequilibrium is challenging when double heterozygotes are indistinguishable.

    Purpose of the Study:

    • To clarify the distinctions between multiplicative and additive models for gene association.
    • To explore an alternative model formulation incorporating sums and products of linkage disequilibrium terms.
    • To assess the impact of higher-order gene interactions on model fit.

    Main Methods:

    • Comparative analysis of multiplicative (Haber, 1984) and additive (Weir, 1979) genetic models.
    • Development of an alternative model formulation using sums and products of linkage disequilibrium terms.

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  • Re-examination of existing genetic data to evaluate model performance.

    • Main Results:

    • The study highlights difficulties in separating inter- and intragametic linkage disequilibrium in certain parameterizations.
    • An alternative model formulation reveals measures of association involving three and four genes.
    • Inclusion of trigenic disequilibria significantly enhanced the fit of the model to re-examined data.

    Conclusions:

    • The proposed alternative model provides a more comprehensive approach to analyzing gene associations.
    • Higher-order gene interactions, specifically trigenic disequilibria, are crucial for accurately modeling genetic associations.
    • This work refines genetic association modeling by accounting for complex gene interactions.