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A comparison of methods for training population optimization in genomic selection.

Javier Fernández-González1, Deniz Akdemir2, Julio Isidro Y Sánchez3

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Optimizing training sets for genomic selection (GS) is key to maximizing accuracy and minimizing costs. Targeted optimization methods, like maximizing CDmean, are superior, requiring 50-55% of data for high accuracy.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomic selection

Background:

  • Genomic selection (GS) is a vital breeding tool, necessitating efficient training set design to balance prediction accuracy and phenotyping costs.
  • Numerous training set optimization methods exist, but a comprehensive comparative analysis is lacking.

Purpose of the Study:

  • To benchmark various training set optimization methods and determine optimal training set sizes for genomic selection.
  • To provide practical guidelines for breeding programs using GS.

Main Methods:

  • Extensive benchmarking of optimization methods and training set sizes across seven datasets, six species, and diverse genetic architectures.
  • Evaluation using multiple genomic selection models under varying heritabilities and population structures.

Main Results:

  • Targeted optimization methods outperformed untargeted ones, especially in low heritability scenarios.
  • Maximizing the mean coefficient of determination (CDmean) was the best targeted method; minimizing average relatedness was optimal for untargeted methods.
  • 50-55% of the candidate set achieved 95-100% of maximum accuracy in targeted optimization, versus 65-85% for untargeted methods.

Conclusions:

  • Training set diversity enhances GS robustness against population structure.
  • Optimal training set size and method selection are crucial for efficient genomic selection in breeding programs.