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Related Experiment Videos

Maximizing selection efficiency for categorical traits

T H Meuwissen1, B Engel, J H van der Werf

  • 1DLO Institute of Animal Science and Health, AM Zeist, The Netherlands.

Journal of Animal Science
|July 1, 1995
PubMed
Summary
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Generalized linear mixed models (GLMMp) improve genetic gain for categorical traits compared to linear mixed models (LMM). Optimizing categorical data recording significantly boosts genetic improvement rates.

Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Statistical genetics

Background:

  • Genetic improvement for categorical traits is challenging due to low information content and inapplicability of standard methods.
  • Ordinary linear mixed models (LMM) do not theoretically account for the categorical nature of traits.

Purpose of the Study:

  • To compare the effectiveness of generalized linear mixed models (GLMMp) against LMM for genetic improvement of categorical traits.
  • To evaluate strategies for enhancing genetic gain by optimizing data recording and model application.

Main Methods:

  • Comparison of LMM with GLMMp, which models an underlying continuous variable for categorical traits.
  • Analysis of genetic gain rates in closed nucleus breeding schemes under different selection strategies.

Related Experiment Videos

  • Assessment of the impact of subdividing categorical trait data on genetic gain.

    • Main Results:

    • GLMMp increased genetic gain by 1-2% over LMM in standard breeding schemes.
    • Genetic gain increased by 7-20% when sire and herd effects were confounded.
    • Subdividing high-incidence categories boosted gain by up to 84%; direct recording of underlying variables yielded 109-278% more gain.

    Conclusions:

    • GLMMp offers a theoretically sound and practically beneficial approach for genetic improvement of categorical traits.
    • Strategic data recording, particularly refining high-incidence categories, is crucial for maximizing genetic gain.
    • Direct recording of underlying continuous variables presents the most substantial gains in genetic improvement.