A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.

ABSTRACT: Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machi

SUBMITTER: Aono AH 

PROVIDER: S-EPMC9605989 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature