Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.)

• Published in: Frontiers in genetics 2021-06, Vol.12, p.692870-692870
• Main Authors: Labroo, Marlee R., Ali, Jauhar, Aslam, M. Umair, de Asis, Erik Jon, dela Paz, Madonna A., Sevilla, M. Anna, Lipka, Alexander E., Studer, Anthony J., Rutkoski, Jessica E.
• Format: Article
• Language: English
• Subjects: best linear unbiased prediction; breeding; general combining ability; Genetics; genomic selection; hybrid rice
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.692870

Hybrid rice varieties can outyield the best inbred varieties by 15 – 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F 1 hybrids. We identified male sterile lines with good combining ability as well as F 1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant ( r = −0.131) and potential yield ( r = −0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.