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

Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M.S., Varshney, R.K.ORCID: 0000-0002-4562-9131, Prasanna, B.M. and Qian, Q. (2022) Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Molecular Plant . pp. 1-32.

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Free to read: https://doi.org/10.1016/j.molp.2022.09.001
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Abstract

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 genotypes. However, 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 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 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 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) 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.

Item Type: Journal Article
Murdoch Affiliation(s): Centre for Crop and Food Innovation
State Agricultural Biotechnology Centre
Publisher: Cell Press an Imprint of Elsevier
Copyright: © 2022 The Authors.
URI: http://researchrepository.murdoch.edu.au/id/eprint/66374
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