Genomic prediction applied to multiple traits and environments in second season maize hybrids.

ABSTRACT: Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, nu

SUBMITTER: de Oliveira AA 

PROVIDER: S-EPMC7413256 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature