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Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

José Crossa1, Paulino Pérez-Rodríguez2, Jaime Cuevas3

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Summary

Genomic selection (GS) accelerates crop breeding by predicting superior genotypes. This review covers genomic-enabled prediction (GP) models, their accuracy in crops, and integrating GS with germplasm enhancement and hyperspectral imaging for faster genetic gains.

Keywords:
genomic selectiongenomic selection and genetic gains in crop breeding populationsgenomic-enabled prediction accuracymodel complexitymodels for genomic genotype×environment interaction

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Area of Science:

  • Plant breeding
  • Genetics
  • Agricultural science

Background:

  • Genomic selection (GS) is a powerful tool for accelerating crop improvement by enabling the selection of superior genotypes.
  • Genomic-enabled prediction (GP) models are central to GS, leveraging genomic information for predictive accuracy.

Purpose of the Study:

  • To review the history, principles, and complexities of GS and GP models.
  • To examine the accuracy of GP models in cereal and legume crops, including genotype×environment interactions.
  • To explore the potential of GS in germplasm enhancement and its integration with emerging technologies.

Main Methods:

  • Review of existing literature on genomic selection and prediction.
  • Analysis of GP model accuracy using random cross-validation in selected crops.
  • Discussion of genetic and statistical complexities, including G×E interactions.

Main Results:

  • GS has demonstrated tangible genetic gains in maize breeding.
  • GP model accuracy varies across different crop types and environments.
  • Hyperspectral imaging presents a promising avenue for integration with GS.

Conclusions:

  • Genomic selection significantly accelerates crop breeding cycles and genetic gain.
  • Understanding GP model complexities, including G×E, is crucial for effective application.
  • Integrating GS with germplasm enhancement and advanced imaging technologies can further optimize breeding programs.