Smart breeding driven by big data, artificial intelligence and integrated genomic-enviromic prediction.
The first paradigm of plant breeding involves direct selection based phenotypic observation, followed by predictive breeding using statistical models constructed for quantitative traits based on genetic experimental design and more recently by incorporating molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combining 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 challenges. Here, we first review innovative technologies for predictive breeding. We 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 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 intelligence (mainly focus on machine and deep learning). How to implement iGEP was discussed, 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 the 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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Smart breeding driven by big data, artificial intelligence and integrated genomic-enviromic prediction.
Semantic Scholar · Computer Science · 2022
Abstract
The first paradigm of plant breeding involves direct selection based phenotypic observation, followed by predictive breeding using statistical models constructed for quantitative traits based on genetic experimental design and more recently by incorporating molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combining 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 challenges. Here, we first review innovative technologies for predictive breeding. We 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 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 intelligence (mainly focus on machine and deep learning). How to implement iGEP was discussed, 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 the 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.