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Optimization of multi-environment trials for genomic selection based on crop models.

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Summary

Optimizing multi-environment trials (METs) using the new OptiMET criterion improves the prediction of genotype × environment interactions. This approach enhances genetic parameter estimation for more accurate crop breeding.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Agricultural science

Background:

  • Genotype × environment interactions (GEI) are crucial in plant breeding, impacting multi-environment trials (METs).
  • Genomic selection (GS) models need adaptation for accurate GEI prediction in diverse environments.
  • Crop growth models (CGMs) combined with genetic parameters offer a promising approach to increase prediction accuracy.

Purpose of the Study:

  • To develop a method for optimizing the selection of environments within METs for accurate genetic parameter estimation.
  • To introduce and evaluate a statistical criterion, OptiMET, for MET optimization.

Main Methods:

  • Developed the OptiMET criterion to statistically optimize MET composition.
  • Combined crop growth models (CGMs) with genomic selection (GS) models.
  • Evaluated OptiMET using simulated data and real wheat phenology data.

Main Results:

  • METs optimized with OptiMET yielded lower error in genetic parameter estimation.
  • OptiMET-defined METs demonstrated higher quantitative trait loci (QTL) detection power and prediction accuracies.
  • OptiMET-based METs were more efficient than random METs, even with fewer environments.

Conclusions:

  • OptiMET is an effective tool for designing optimal METs to improve GEI prediction.
  • The criterion enhances the exploitation of METs and advanced phenotyping tools.
  • This method leads to more efficient and accurate plant breeding strategies.