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Area of Science:

  • Plant breeding
  • Genomics
  • Quantitative genetics

Background:

  • Identifying superior genotypes is crucial for plant breeding programs.
  • Genomic selection (GS) aims to enhance breeding efficiency through genomic information.
  • Optimizing training set construction is key to improving GS performance.

Purpose of the Study:

  • To evaluate and propose methods for training set optimization in genomic selection.
  • To enhance the efficiency of identifying top-performing genotypes from breeding populations.
  • To compare optimal design-inspired approaches with accuracy-focused methods.

Main Methods:

  • Evaluation of various training set construction methods for genomic selection.
  • Implementation of two novel approaches inspired by optimal design criteria.
  • Comparison using simulation studies and real trait data analysis.
  • Assessment using metrics like normalized discounted cumulative gain and correlation coefficients.

Main Results:

  • A ridge regression-based method is recommended for populations without strong subpopulation structure.
  • Heuristic-based generalized coefficient of determination and D-optimality-like methods are preferred for structured populations.
  • A ranking method can down-scale large candidate populations before applying heuristic methods.
  • A computationally efficient version of a known method was verified.

Conclusions:

  • The choice of training set construction method in genomic selection depends on population structure.
  • Optimized training sets improve the efficiency of identifying superior genotypes in plant breeding.
  • Proposed methods offer effective strategies for enhancing genomic selection performance.