Genomic selection: A breakthrough technology in rice breeding

作     者：Yang Xu Kexin Ma Yue Zhao Xin Wang Kai Zhou Guangning Yu Cheng Li Pengcheng Li Zefeng Yang Chenwu Xu Shizhong Xu Yang Xu;Kexin Ma;Yue Zhao;Xin Wang;Kai Zhou;Guangning Yu;Cheng Li;Pengcheng Li;Zefeng Yang;Chenwu Xu;Shizhong Xu

作者机构：Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou 225009JiangsuChina Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCA 92507USA 

出 版 物：《The Crop Journal》 (作物学报（英文版）)

年 卷 期：2021年第9卷第3期

页      面：669-677页

核心收录：

学科分类：09[农学] 0901[农学-作物学] 

基　　金：supported by the National Natural Science Foundation of China(31801028,32061143030,and 41801013) the National Key Technology Research and Development Program of China(2016YFD0100303) the Priority Academic Program Development of Jiangsu Higher Education Institutions the Innovative Research Team of Ministry of Agriculture the Qing-Lan Project of Yangzhou University 

主　　题：Genomic selection Rice Hybrid Predictive ability Model 

摘      要：Rice(Oryza sativa)provides a staple food source for more than half the world ***,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global *** selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are *** simulation and experimental studies have demonstrated the usefulness of GS in rice ***,several affecting factors and limitations require careful consideration when performing *** this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice *** also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic ***,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.