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022			_a2590-3462 (Online)
024	8		_ahttps://doi.org/10.1016/j.xplc.2024.101199
040			_aMX-TxCIM
041			_aeng
100	0		_aYang Xu _917252
245	1	0	_aMetabolic marker-assisted genomic prediction improves hybrid breeding
260			_aUnited States of America : _bCell Press ; _bPlant Communications Shanghai Editorial, _c2025.
500			_aPeer review
500			_aOpen access
520			_aHybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or eXtreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.
546			_aText in English
591			_aYunbi Xu : Not CIMMYT Affiliation
597			_dNational Key Research and Development Program _dNational Natural Science Foundation of China _dJiangsu Province Agricultural Science and Technology Independent Innovation _dPriority Academic Program Development of Jiangsu Higher Education Institutions ( _dSeed Industry Revitalization Project of Jiangsu Province _dJiangsu Provincial Key Research and Development Program _dShenzhen Science and Technology Innovation Program _dHebei University of Science and Technology _dShanghai Agricultural Science and Technology Innovation Program _dQing Lan Project of Jiangsu Province _dYangzhou University High-end Talent Support Program _fBreeding for Tomorrow _uhttps://hdl.handle.net/10568/179152
650		7	_aGenomics _91132 _2AGROVOC
650		7	_aForecasting _92701 _2AGROVOC
650		7	_aHybrids _91151 _2AGROVOC
650		7	_aBreeding _2AGROVOC _91029
650		7	_aMetabolome _2AGROVOC _938192
650		7	_aAssociation mapping _91512 _2AGROVOC
650		7	_aMarker-assisted selection _910737 _2AGROVOC
650		7	_aMaize _2AGROVOC _91173
650		7	_aGenome-wide association studies _931443 _2AGROVOC
700	1		_aWenyan Yang _929404
700	0		_aJie Qiu _938186
700	0		_aKai Zhou _929403
700	1		_aGuangning Yu _929400
700	0		_aYuxiang Zhang _936502
700	0		_aXin Wang _96995
700	1		_aYuxin Jiao _929402
700	0		_aXinyi Wang _938187
700	0		_aShujun Hu _938188
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
700	0		_aPengcheng Li _917447
700	0		_aYue Lu _938189
700	0		_aRujia Chen _938190
700	0		_aTianyun Tao _938191
700	0		_aZefeng Yang _917448
700	1		_aYunbi Xu _gGlobal Maize Program _8INT2735 _9857
700	0		_aChenwu Xu _917254
773	0		_dUnited States of America : Cell Press ; Plant Communications Shanghai Editorial, 2025. _gv. 6, no. 3, art. 101199 _tPlant Communications _x2590-3462
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35473
942			_cJA _n0 _2ddc
999			_c68613 _d68605