Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Genomic prediction accuracy improves with heritability, necessitating efficient multienvironment yield trial (MEYT) designs.
• Optimizing MEYT design under budget constraints is vital for accurate hybrid performance prediction.

Purpose of the Study:

• To identify optimal sparse distribution strategies for plots across environments in MEYT.
• To enhance the accuracy of hybrid performance prediction in winter wheat using genomic selection.

Main Methods:

• Utilized a dataset of 1,604 winter wheat hybrids with genomic and phenotypic information.
• Compared biometric models for phenotypic data analysis in a resampling study with imbalanced trials.
• Evaluated the impact of genotypic relationship information on prediction accuracy.

Main Results:

• Genotypic values showed stronger association with reference values when genotypic relationship information was incorporated.
• Balanced environmental sampling adequately characterized environments and improved hybrid performance estimation accuracy.
• A design with equally sized, overlapping genotype subgroups tested across subsets of environments yielded high accuracy and low variability.

Conclusions:

• Incorporating genotypic relationships and balanced environmental sampling significantly improves genomic prediction in MEYT.
• An optimized MEYT design with overlapping genotype subgroups is effective for winter wheat breeding under genomic selection.
• This study demonstrates a utility for optimizing MEYT design for hybrid wheat trials with genomic selection.