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A Bayesian Decision Theory Approach for Genomic Selection.

Bartolode Jesús Villar-Hernández1, Sergio Pérez-Elizalde2, José Crossa2,3

  • 1Colegio de Postgraduados, Montecillos, Edo. de México, México.

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Summary
This summary is machine-generated.

Bayesian decision theory offers a formal method for genomic selection (GS) in breeding programs. Utilizing specific loss functions can improve long-term selection response and genetic gain for multiple traits.

Keywords:
Bayesian Decision TheoryGenPredGenomic SelectionLoss FunctionShared Data ResourcesSimulation Scenarios

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Area of Science:

  • Quantitative genetics
  • Plant and animal breeding
  • Statistical genomics

Background:

  • Breeders require efficient methods to select superior individuals for next-generation breeding.
  • Genomic selection (GS) is a key tool, but optimal selection criteria are continuously explored.

Purpose of the Study:

  • To propose and evaluate a formal Bayesian decision theory framework for genomic selection.
  • To assess the performance of novel univariate and multivariate loss functions in breeding selection.

Main Methods:

  • Developed and tested three univariate loss functions (KL, CRPS, LinLin) and their multivariate counterparts (KL, EnergyS, MALF).
  • Expressed loss functions in terms of heritability and validated on wheat data and simulations.
  • Compared loss function performance against standard selection (Std) using selection response and genetic variance metrics.

Main Results:

  • Single-trait selection using loss functions showed improved long-term performance with a 30% selection intensity.
  • Multi-trait selection resulted in positive genetic gains across all traits, including negatively correlated ones.
  • Population genetic variances remained statistically similar across loss functions by the 10th selection cycle.

Conclusions:

  • The proposed Bayesian decision theory framework provides a robust method for genomic selection.
  • Specific loss functions offer a valuable criterion for selecting candidates in recurrent breeding programs.
  • Optimizing selection intensity is crucial for realizing long-term benefits from loss function-guided selection.