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Sire evaluation for multiple binary responses when information is missing on some traits.

J L Foulley, D Gianola

    Journal of Dairy Science
    |October 1, 1986
    PubMed
    Summary
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    This study presents a sire evaluation method for multiple binary traits with missing data. The approach uses a multivariate normal model and posterior distribution to provide accurate genetic predictions for improved animal breeding.

    Area of Science:

    • Animal genetics
    • Quantitative genetics
    • Statistical genetics

    Background:

    • Sire evaluation is crucial for genetic improvement in livestock.
    • Missing data in multiple binary traits poses challenges for accurate sire evaluation.
    • Existing methods may introduce bias when dealing with categorical traits.

    Purpose of the Study:

    • To develop a robust sire evaluation procedure for multiple binary responses with missing information.
    • To address potential biases in selection for categorical traits.
    • To provide accurate genetic predictions for fixed and random effects.

    Main Methods:

    • Utilizes a genetic model based on an underlying multivariate normal distribution with thresholds.
    • Employs a posterior distribution comprising conditionally independent likelihood functions.

    Related Experiment Videos

  • Involves nonlinear maximization to determine point estimators and predictors.

    • Main Results:

    • The procedure effectively handles missing data in multiple binary traits.
    • Demonstrates accurate estimation of genetic parameters and effects.
    • An example illustrates joint selection for calving ease and skeletal development.

    Conclusions:

    • The proposed sire evaluation method offers a statistically sound approach for complex breeding scenarios.
    • It has the potential to mitigate bias associated with selection for categorical traits.
    • This methodology enhances the accuracy of genetic predictions in animal breeding programs.