Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

ABSTRACT: Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of tr

SUBMITTER: Muvunyi BP 

PROVIDER: S-EPMC9257107 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature