Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.

ABSTRACT: The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factor

SUBMITTER: Atanda SA 

PROVIDER: S-EPMC11367796 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature