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Analysis of selection experiments using mixed model methodology.

D A Sorensen, B W Kennedy

    Journal of Animal Science
    |July 1, 1986
    PubMed
    Summary
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    Mixed models accurately estimate genetic variance and selection response using minimum variance quadratic unbiased estimation (MIVQUE). This method works even without knowing base population heritability, partitioning genetic and environmental trends effectively.

    Area of Science:

    • Quantitative genetics
    • Animal breeding
    • Statistical modeling

    Background:

    • Mixed model techniques are crucial for estimating genetic variance and response to selection.
    • Accurate estimation is vital for effective breeding programs and understanding evolutionary processes.

    Purpose of the Study:

    • To illustrate the application of mixed model techniques for estimating genetic variance and selection response.
    • To derive and evaluate a minimum variance quadratic unbiased estimator (MIVQUE) for genetic variance.
    • To assess the properties of mixed model estimators for selection response.

    Main Methods:

    • Application of mixed model techniques with a reduced animal model.
    • Derivation of a minimum variance quadratic unbiased estimator (MIVQUE) for genetic variance.

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  • Monte Carlo simulations to evaluate the properties of mixed model estimators for response.

    • Main Results:

    • The mixed model estimator for response requires base population heritability, but MIVQUE estimates from data provide good agreement with true response when heritability is unknown.
    • Under certain conditions, the mixed model estimator can partition phenotypic trends into genetic and environmental components without a control population.
    • These ideal conditions are rarely met in long-term selection experiments.

    Conclusions:

    • Mixed models, particularly using MIVQUE, offer a robust approach to estimating genetic parameters and selection response.
    • The ability to partition phenotypic trends highlights the power of these models, though limitations exist in practical, long-term applications.
    • Further research is needed to address complexities like finite loci and opposing natural selection on estimator properties.