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Multitrait animal model with genetic groups.

Y Da1, M Grossman

  • 1Department of Animal Sciences, University of Illinois, Urbana 61801.

Journal of Dairy Science
|September 1, 1991
PubMed
Summary
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This study extends the multitrait animal model to include genetic groups, developing computationally feasible methods for mixed model equations and restricted maximum likelihood (REML) estimation, even with missing data.

Area of Science:

  • Animal genetics
  • Quantitative genetics
  • Statistical genetics

Background:

  • The multitrait animal model is crucial for genetic evaluations in animal breeding.
  • Incorporating genetic groups of unknown parents improves the accuracy of these evaluations.
  • Computational efficiency is a key challenge in complex genetic models.

Purpose of the Study:

  • To extend the multitrait animal model to effectively incorporate genetic groups.
  • To develop computationally feasible mixed model equations for this extended model.
  • To propose a strategy for handling missing data within this framework.

Main Methods:

  • Development of mixed model equations incorporating genetic groups.
  • Application of canonical and triangular transformations to the model.

Related Experiment Videos

  • Derivation of formulations for restricted maximum likelihood (REML) estimation with genetic groups.

    • Main Results:

    • Computationally feasible mixed model equations were obtained for the multitrait animal model with genetic groups.
    • A strategy for including genetic groups with missing data was proposed and validated.
    • REML estimation with genetic groups demonstrated near-feasibility compared to models without groups.

    Conclusions:

    • The extended multitrait animal model with genetic groups is computationally viable.
    • The proposed methods facilitate accurate genetic evaluations, even with missing data.
    • The findings offer practical advancements for genetic parameter estimation in animal populations.