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 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…

• BA
  Bill and Melinda Gates Foundation
• NK
  National Key Research and Development Program of China

  Award: 2016YFD0101803
• CP
  Central Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences

  Award: Y2020PT20