Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

ABSTRACT:

Key message

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environme

SUBMITTER: Hu H 

PROVIDER: S-EPMC8580906 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature