Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping.

ABSTRACT: The accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected

SUBMITTER: Campbell M 

PROVIDER: S-EPMC6508851 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature