Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame.

ABSTRACT: Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted. Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses. Results:</

SUBMITTER: Sabag I 

PROVIDER: S-EPMC10040590 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature