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

文献类型： 外文期刊

第一作者： Xu, Yunbi

作者： Xu, Yunbi;Li, Huihui;Qian, Qian;Xu, Yunbi;Xu, Yunbi;Zhang, Xingping;Zheng, Hongjian;Zhang, Jianan;Li, Huihui;Olsen, Michael S.;Prasanna, Boddupalli M.;Varshney, Rajeev K.

作者机构：

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

期刊名称：MOLECULAR PLANT （ 影响因子：27.5； 五年影响因子：22.6 ）

ISSN： 1674-2052

年卷期： 2022 年 15 卷 11 期

页码：

收录情况： SCI

摘要： 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 experi-mental design and, more recently, by incorporation of molecular marker genotypes. However, plant perfor-mance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and geno-type 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 multidimen-sionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional infor-mation 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 construction. We 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 intelli-gence (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) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

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