Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets.

ABSTRACT: Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed tha

SUBMITTER: Sabadin F 

PROVIDER: S-EPMC9583387 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature