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smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

Molecular Plant 2022年第11期15卷 1664-1695页

作者： Yunbi Xu Xingping Zhang Huihui Li Hongjian Zheng Jianan Zhang Michael S.Olsen Rajeev K.Varshney Boddupalli M.Prasanna Qian Qian Institute of Crop Sciences CIMMYT-ChinaChinese Academy of Agricultural SciencesBeijing 100081China CIMMYT-China Tropical Maize Research Center School of Food Science and EngineeringFoshan UniversityFoshanGuangdong 528231China Peking University Institute of Advanced Agricultural Sciences WeifangShandong 261325China CIMMYT-China Specialty Maize Research Center Shanghai Academy of Agricultural SciencesShanghai 201400China Molbreeding Biotechnology Co. Ltd.ShijiazhuangHebei 050035China National Nanfan Research Institute(Sanya) Chinese Academy of Agricultural SciencesSanyaHainan 572024China CIMMYT(International Maize and Wheat Improvement Center) ICRAF CampusUnited Nations AvenueNairobiKenya State Agricultural Biotechnology Centre Centre for Crop and Food InnovationFood Futures InstituteMurdoch UniversityMurdochAustralia

The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker ***,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great ***,we first review innovative technologies for predictive *** then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model *** propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)***,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding *** call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.

关键词： smart breeding genomic selection integrated genomic-enviromic selection spatiotemporal omics crop design machine and deep learning big data artificial intelligence

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