Jianan Zhang

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.

BACKGROUND: The peanut (Arachis hypogaea L.) is an important oilseed crop in tropical and subtropical regions of the world. However, little about the molecular biology of the peanut is currently known. Recently, next-generation sequencing technology, termed RNA-seq, has provided a powerful approach for analysing the transcriptome, and for shedding light on the molecular biology of peanut. RESULTS: In this study, we employed RNA-seq to analyse the transcriptomes of the immature seeds of three different peanut varieties with different oil contents. A total of 26.1-27.2 million paired-end reads with lengths of 100 bp were generated from the three varieties and 59,077 unigenes were assembled with N50 of 823 bp. Based on sequence similarity search with known proteins, a total of 40,100 genes were identified. Among these unigenes, only 8,252 unigenes were annotated with 42 gene ontology (GO) functional categories. And 18,028 unigenes mapped to 125 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, 3,919 microsatellite markers were developed in the unigene library, and 160 PCR primers of SSR loci were used for validation of the amplification and the polymorphism. CONCLUSION: We completed a successful global analysis of the peanut transcriptome using RNA-seq, a large number of unigenes were assembled, and almost four thousand SSR primers were developed. These data will facilitate gene discovery and functional genomic studies of the peanut plant. In addition, this study provides insight into the complex transcriptome of the peanut and established a biotechnological platform for future research.