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

Abstract excerpt

Genomic selection (GS) is a promising strategy in plant breeding for identifying superior genotypes with high true breeding values (TBVs) across multiple environments. However, the relative performance of candidate genotypes often varies due to complex genotype-by-environment (G × E) interactions in multienvironment trials (METs). To address this challenge, we employed a GS prediction model incorporating fixed...

Topics

Join the communities discussing this publication.

• Algorithms
• Environment
• Gene-Environment Interaction
• Genome, Plant
• Oryza
• Plant Breeding