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Including genetic relationships in selection decisions: alternative methodologies.

J R Brisbane1, J P Gibson

  • 1Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, NIG 2W1, Ontario, Canada.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|October 31, 2013
PubMed
Summary
This summary is machine-generated.

This study explores methods for reducing inbreeding in animal populations using adjusted estimated breeding values (EBV). A new computational strategy offers greater efficiency and effectiveness in selection decisions.

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Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Population genetics

Background:

  • Reducing inbreeding is crucial for maintaining genetic diversity and long-term viability in livestock populations.
  • Traditional selection methods may not adequately account for complex genetic relationships, potentially leading to increased inbreeding.
  • Estimated breeding values (EBV) are essential tools in selection, but their accuracy can be improved by incorporating genetic relationship information.

Purpose of the Study:

  • To investigate variations of an iterative methodology for reducing inbreeding by incorporating genetic relationships into selection decisions.
  • To evaluate an alternative, more computationally efficient strategy for maximizing population selection criteria.
  • To assess the impact of altering relationship weights and using EBV in one or both sexes on genetic gain and inbreeding levels.

Main Methods:

  • The study employed an iterative methodology for selection decisions, utilizing adjusted estimated breeding values (EBV) that account for genetic relationships.
  • An alternative computing strategy was developed and compared to the original method for its efficiency and effectiveness in maximizing the population selection criterion.
  • Different weighting schemes for various genetic relationships within the adjusted EBV were tested, alongside strategies involving EBV in one or both sexes, and compared against methods not using genetic relationships.

Main Results:

  • A novel computing strategy was identified that requires less computation while achieving equal or greater function values compared to the original iterative method.
  • Adjusting the weights for different types of genetic relationships in the EBV did not significantly impact genetic gain at a specified inbreeding level.
  • Using the adjusted EBV method in both sexes yielded greater genetic gain at a given inbreeding level compared to using it in only one sex, and surpassed three selection strategies that ignored genetic relationships.

Conclusions:

  • The adjusted EBV method, particularly when applied to both sexes, is effective in reducing inbreeding while maximizing genetic gain.
  • The newly proposed computational strategy offers a more efficient approach to implementing selection decisions that incorporate genetic relationships.
  • The benefits of the adjusted EBV method in enhancing genetic gain at controlled inbreeding levels are evident even for sex-limited traits, highlighting its broad applicability in breeding programs.