Dataset Information

Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

ABSTRACT: Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi-omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic-based hybrid prediction has ignored parental phenotypic information, which is of fundamental importance in plant breeding. In this study, we integrated parental phenotypic information into various multi-omic prediction models applied in hybrid breeding of rice and compared the predictabilities of 15 combinations from four sets of predictors from the parents, that is genome, transcriptome, metabolome and phenome. The predictability for each combination was evaluated using the best linear unbiased prediction and a modified fast HAT method. We found significant interactions between predictors and traits in predictability, but joint prediction with various combinations of the predictors significantly improved predictability relative to prediction of any single source omic data for each trait investigated. Incorporation of parental phenotypic data into various omic predictors increased the predictability, averagely by 13.6%, 54.5%, 19.9% and 8.3%, for grain yield, number of tillers per plant, number of grains per panicle and 1000 grain weight, respectively. Among nine models of incorporating parental traits, the AD-All model was the most effective one. This novel strategy of incorporating parental phenotypic data into multi-omic prediction is expected to improve hybrid breeding progress, especially with the development of high-throughput phenotyping technologies.

SUBMITTER: Xu Y

PROVIDER: S-EPMC7868986 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

Xu Yang Y Zhao Yue Y Wang Xin X Ma Ying Y Li Pengcheng P Yang Zefeng Z Zhang Xuecai X Xu Chenwu C Xu Shizhong S

Plant biotechnology journal 20200902 2

Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi-omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic-based hybrid pr ...[more]

PMID: 32738177

Similar Datasets

Project description:Hybrid rice breeding is an important strategy for enhancing grain yield. Breeding high-performance parental lines and identifying combining abilities is a top priority for hybrid breeding. Yuenongsimiao (YNSM) and its derivative variety Yuehesimiao (YHSM) are elite restorer lines with a high ability of fertility restoration, from which 67 derived hybrid combinations have been authorized to different degrees in more than 110 instances in China. In this study, we found that YNSM and YHSM contained three candidate restorer-of-fertility (Rf) genes, Rf3, Rf4, and Rf5/Rf1a, that might confer their restoration ability. Subsequently, we investigated heterosis and combining ability of YNSM and YHSM using 50 F1 hybrids from a 5 × 10 incomplete diallelic mating design. Our results indicated that hybrid combinations exhibited significant genetic differences, and the additive effects of the parental genes played a preponderant role in the inheritance of observed traits. The metrics of plant height (PH), 1000-grain weight (TGW), panicle length (PL), and the number of spikelets per panicle (NSP) were mainly affected by genetic inheritance with higher heritability. Notably, the general combining ability (GCA) of YHSM exhibited the largest positive effect on the number of grains per panicle (NGP), NSP, PL, and TGW. Thus, YHSM had the largest GCA effect on yield per plant (YPP). In addition, the GCA of YNSM exhibited a positive impact on YPP, mainly due to the critical contribution of seed setting percentage (SSP). Moreover, YNSM and YHSM exhibited negative GCA effects on PH, implying that YNSM and YHSM could effectively enhance plant lodging resistance by reducing the plant height of the derived hybrids. Remarkably, among the hybrids, Yuanxiang A/YNSM (YXA/YNSM), Shen 08S/Yuemeizhan (S08S/YMZ), and Quan 9311A/YHSM (Q9311A/YHSM) represent promising new combinations with a higher specific combining ability (SCA) effect value on YPP with a value more than 3.50. Our research thus highlights the promising application for the rational utilization of YNSM and YHSM in hybrid rice breeding.

Project description: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 F1 hybrids. We identified male sterile lines with good combining ability as well as F1 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.

Dataset Information

Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

Publications

Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets