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Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.

Zi-Jie Liu1, Chen-Tuo Liao1

  • 1Department of Agronomy, National Taiwan University, Taipei 106319, Taiwan.

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|February 11, 2026
PubMed
Summary
This summary is machine-generated.

Genomic selection (GS) effectively identifies superior plant genotypes across environments. The CDmean(v2) method optimizes training sets for GS in multi-environment trials, improving elite genotype identification with efficient computational cost.

Keywords:
genomic predictiongenotype-by-environment interactionmultienvironment trialplant breedingtraining set optimization

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomics

Background:

  • Genomic selection (GS) aids in identifying superior genotypes for plant breeding.
  • Genotype-by-environment (G×E) interactions complicate genotype performance evaluation in multi-environment trials (METs).

Purpose of the Study:

  • To develop and evaluate training set optimization methods for genomic selection (GS) in multi-environment trials (METs).
  • To compare the efficiency of CDmean(v2) and CD(mean.MET) optimization criteria against random sampling using selection-focused metrics.

Main Methods:

  • A GS prediction model was used, incorporating fixed environment-specific means, random additive genetic effects, and G×E interactions.
  • Two optimization methods, CDmean(v2) and CD(mean.MET), were evaluated using simulation experiments with real crop genotype data (rice, barley, maize).
  • Training set performance was assessed using normalized discounted cumulative gain, Spearman's rank correlation, and rank sum ratio.

Main Results:

  • The CDmean(v2) method consistently demonstrated high efficiency in identifying top-performing genotypes across diverse crop datasets.
  • CDmean(v2) outperformed random sampling and CD(mean.MET) in selecting elite genotypes.
  • The TrainSel package provides an efficient implementation of CDmean(v2) for practical application.

Conclusions:

  • The CDmean(v2) optimization method is recommended for GS-assisted breeding programs due to its superior performance in identifying elite genotypes.
  • This approach offers a practical and computationally efficient solution for training set optimization in METs.
  • Effective training set selection is crucial for maximizing the benefits of GS in plant breeding programs.