Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).

ABSTRACT: Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS i

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10641074 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature