| 1 | Factors affecting genomic selection revealed by empirical evidence in maize显示文摘Genomic selection(GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy(rMG) in GS were evaluated systematically, using six agronomic traits(plant height, ear height, ear length, ear diameter,grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability,statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; rMGshowed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction(RR-BLUP), genomic BLUP(GBLUP), Bayes A, Bayes B, Bayes C, Bayesian least absolute shrinkage and selection operator(Bayesian LASSO), and reproducing kernel Hilbert space(RKHS), the RKHS and additive-dominance model(Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties. | Xiaogang Liu Hongwu Wang Hui Wang Zifeng Guo Xiaojie Xu Jiacheng Liu Shanhong Wang Wen-Xue Li Cheng Zou Boddupalli M.Prasanna Michael S.Olsen Changling Huang Yunbi Xu | 2018 | The Crop Journal2018，6，4： | 8 |
| 2 | Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction显示文摘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. | Yunbi Xu Xingping Zhang Huihui Li Hongjian Zheng Jianan Zhang Michael S.Olsen Rajeev K.Varshney Boddupalli M.Prasanna Qian Qian | 2022 | Molecular Plant2022，15，11： | 7 |
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