Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

ABSTRACT: Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi-omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic-based hybrid prediction has ignored parental phenotypic information, which is of fundamental importance in plant breeding. In this study, we integrated parental phenotypic information into various multi-omic prediction models applied in hybrid breeding of rice and compared the predictabilities of 15 combinations f

SUBMITTER: Xu Y 

PROVIDER: S-EPMC7868986 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature