000			03201nab|a22003977a|4500
001			62983
003			MX-TxCIM
005			20230522171931.0
008			201118s2021||||xxk|||p|op||||00||0|eng|d
022			_a1467-7644
022			_a1467-7652 (Online)
024	8		_ahttps://doi.org/10.1111/pbi.13458
040			_aMX-TxCIM
041			_aeng
100	0		_aYang Xu _917252
245	1	0	_aIncorporation of parental phenotypic data into multi‐omic models improves prediction of yield‐related traits in hybrid rice
260			_aUnited Kingdom : _bWiley, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aHybrid 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.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aRice _91243 _2AGROVOC
650		7	_aHybrids _2AGROVOC _91151
700	0		_aYue Zhao _917449
700	0		_aXin Wang _96995
700	0		_aYing Ma _917445
700	0		_aPengcheng Li _917447
700	0		_aZefeng Yang _917448
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
700	0		_aChenwu Xu _917254
700	0		_aShizhong Xu _916865
773	0		_gv. 19, no. 2, p. 261-272 _dUnited Kingdom : Wiley, 2021. _tPlant Biotechnology Journal _x1467-7652 _wu57523
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21060
942			_cJA _n0 _2ddc
999			_c62983 _d62975