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

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.