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| 1 | Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives显示文摘在为基因在领域庄稼和树印射和发现的分子的标记试金的开发和应用程序有一个很快升起的趋势。因此 ? 远,超过 50 个 SNP 数组和 genotyping-by-sequencing (GBS ) 的 15 种不同类型平台在超过 25 庄稼种类和长期的树被开发了。然而,少得多努力在为引起程序使用开发 ultra-high-throughput 和划算的 genotyping 平台上被作了。在这评论,我们在存在 SNP 数组和 GBS 技术和策略讨论科学瓶颈开发指向的平台为收割分子的繁殖。我们建议实际繁殖平台应该采用的那未来自动化了 genotyping 技术,任何一个数组或定序基于,目标 ? 功能的多型性 underpinning 经济特点,并且为量的特点提供理想的预言精确性,用在在庄稼的宽基因背景下面的通用应用程序。如此的平台的发展在庄稼植物由于大遗传型显型差距由于费用低效率,和知识水平在工艺的水平面对严肃的挑战。如此的 genotyping 平台将在考虑在主要庄稼在下一十年里被完成,这被期望?(a)快速的开发?在重要特点的基因发现,(b)通过新分析模型和人口图案加深了量的特点的理解,导致基因和为重要繁殖特点负责的小径的鉴定的多层的 -omics 数据的(c)集成,和在大规模 genotyping 的费用有效性的(d)改进。庄稼繁殖芯片和 genotyping 平台将提供崭新的机会与减轻气候变化的效果的需要的收益潜力,质量,和提高的改编加速栽培变种的开发。 | Awais Rasheed Yuanfeng Hao Xianchun Xia Awais Khan Yunbi Xu Rajeev K. Varshney Zh-onghu He | 2017 | Molecular Plant2017，10，8： | 42 |
| 2 | The Genetic Architecture of Flowering Time and Photoperiod Sensitivity in Maize as Revealed by QTL Review and Meta Analysis显示文摘The control of flowering is not only important for reproduction,but also plays a key role in the processes of domestication and adaptation.To reveal the genetic architecture for flowering time and photoperiod sensitivity,a comprehensive evaluation of the relevant literature was performed and followed by meta analysis.A total of 25 synthetic consensus quantitative trait loci(QTL)and four hot-spot genomic regions were identified for photoperiod sensitivity including 11 genes related to photoperiod response or flower morphogenesis and development.Besides,a comparative analysis of the QTL for flowering time and photoperiod sensitivity highlighted the regions containing shared and unique QTL for the two traits.Candidate genes associated with maize flowering were identified through integrated analysis of the homologous genes for flowering time in plants and the consensus QTL regions for photoperiod sensitivity in maize(Zea mays L.).Our results suggest that the combination of literature review,meta-analysis and homologous blast is an efficient approach to identify new candidate genes and create a global view of the genetic architecture for maize photoperiodic flowering.Sequences of candidate genes can be used to develop molecular markers for various models of marker-assisted selection,such as marker-assisted recurrent selection and genomic selection that can contribute significantly to crop environmental adaptation. | Jie Xu Yaxi Liu Jian Liu Moju Cao Jing Wang Hai Lan Yunbi Xu Yanli Lu Guangtang Pan Tingzhao Rong | 2012 | Journal of Integrative Plant Biology2012，54，6： | 14 |
| 3 | Meta‐analysis and candidate gene mining of low‐phosphorus tolerance in maize显示文摘Plants with tolerance to low‐phosphorus(P) can grow better under low‐P conditions, and understanding of genetic mechanisms of low‐P tolerance can not only facilitate identifying relevant genes but also help to develop low‐P tolerant cultivars. QTL meta‐analysis was conducted after a comprehensive review of the reports on QTL mapping for low‐P tolerance‐related traits in maize. Meta‐analysis produced 23 consensus QTL(cQTL), 17 of which located in similar chromosome regions to those previously reported to influence root traits. Meanwhile, candidate gene mining yielded 215 genes, 22 of which located in the cQTL regions.These 22 genes are homologous to 14 functionally characterized genes that were found to participate in plant low‐P tolerance, including genes encoding miR399s, Pi transporters and purple acid phosphatases. Four cQTL loci(cQTL2‐1,cQTL5‐3, cQTL6‐2, and cQTL10‐2) may play important roles for low‐P tolerance because each contains more original QTL and has better consistency across previous reports. | Hongwei Zhang Mohammed Shalim Uddin Cheng Zou Chuanxiao Xie Yunbi Xu Wen‐Xue Li | 2014 | Journal of Integrative Plant Biology2014，56，3： | 14 |
| 4 | Breeding by design for future rice:Genes and genome technologies显示文摘1.Introduction Rice is a staple food for 3.2 billion people.The food security threat that shook many Asian countries in 2008 still looms,because farmers are facing the challenge of producing more rice with fewer resources of water,land,and inputs. | Jianlong Xu Yongzhong Xing Yunbi Xu Jianmin Wan | 2021 | The Crop Journal2021，9，3： | 10 |
| 5 | 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 |
| 6 | 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 |
| 7 | Zea mays(L.) P1 locus for cob glume color identified as a post-domestication selection target with an effect on temperate maize genomes显示文摘Artificial selection during domestication and post-domestication improvement results in loss of genetic diversity near target loci. However, the genetic locus associated with cob glume color and the nature of the genomic pattern surrounding it was elusive and the selection effect in that region was not clear. An association mapping panel consisting of 283 diverse modern temperate maize elite lines was genotyped by a chip containing over 55,000 evenly distributed SNPs. Ten-fold resequencing at the target region on 40 of the panel lines and 47 tropical lines was also undertaken. A genome-wide association study(GWAS) for cob glume color confirmed the P1 locus, which is located on the short arm of chromosome 1, with a-log10 P value for surrounding SNPs higher than the Bonferroni threshold(α/n, α < 0.001) when a mixed linear model(MLM) was implemented. A total of 26 markers were identified in a 0.78 Mb region surrounding the P1 locus, including 0.73 Mb and 0.05 Mb upstream and downstream of the P1 gene, respectively. A clear linkage disequilibrium(LD) block was found and LD decayed very rapidly with increasing physical distance surrounding the P1 locus. The estimates of π and Tajima's D were significantly(P < 0.001) lower at both ends compared to the locus. Upon comparison of temperate and tropical lines at much finer resolution by resequencing(180-fold finer than chip SNPs), a more structured LD block pattern was found among the 40 resequenced temperate lines. All evidence indicates that the P1 locus in temperate maize has not undergone neutral evolution but has been subjected to artificial selection during post-domestication selection or improvement. The information and analytical results generated in this study provide insights as to how breeding efforts have affected genome evolution in crop plants. | Chuanxiao Xie Jianfeng Weng Wenguo Liu Cheng Zou Zhuanfang Hao Wenxue Li Minshun Li Xiaosen Guo Gengyun Zhang Yunbi Xu Xinhai Li Shihuang Zhang | 2013 | The Crop Journal2013，1，1： | 6 |
| 8 | Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip显示文摘Genotyping platforms,as critical supports for genomics,genetics,and molecular breeding,have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput,automatic,large-scale,and shared facilities.In this study,we integrated an improved genotyping by target sequencing(GBTS)system with capture-in-solution(liquid chip)technology to develop a multiple single-nucleotide polymorphism(mSNP)approach in which mSNPs can be captured from a single amplicon.From one 40K maize mSNP panel,we developed three types of markers(40K mSNPs,251K SNPs,and 690K haplotypes),and generated multiple panels with various marker densities(1K–40K mSNPs)by sequencing at different depths.Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs.Compared with the one-amplicon-one-SNP system,mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection,linkage disequilibrium decay analysis,and genome-wide association studies.The technologies,protocols,and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals,plants,and microorganisms. | Zifeng Guo Quannv Yang Feifei Huang Hongjian Zheng Zhiqin Sang Yanfen Xu Cong Zhang Kunsheng Wu Jiajun Tao Boddupalli MPrasanna Michael SOlsen Yunbo Wang Jianan Zhang Yunbi Xu | 2021 | Plant Communications2021，2，6： | 5 |
| 9 | Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants显示文摘Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies,the rate of genetic gain needs to be accelerated to meet humanity’s demand for agricultural products.In this regard,genomic selection(GS)has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects.Livestock scientists pioneered GS application largely due to livestock’s significantly higher individual values and the greater reduction in generation interval that can be achieved in GS.Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects,along with significant cost reduction.Moreover,it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain.In addition,establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small-and medium-sized enterprises and agricultural research systems in developing countries.New strategies centered on GS for enhancing genetic gain need to be developed. | Yunbi Xu Xiaogang Liu Junjie Fu Hongwu Wang Jiankang Wang Changling Huang Boddupalli MPrasanna Michael SOlsen Guoying Wang Aimin Zhang | 2020 | Plant Communications2020，1，1： | 4 |
| 10 | Crop genome editing: A way to breeding by design显示文摘Increasing population and consumption in our planet is placing unprecedented challenges on agriculture for meeting food security and sustainability needs[1].Meanwhile,the adaptation of modern agricultural techniques[2]is central to minimize extensive losses due to abiotic stresses[3]under global climate change. | Chuanxiao Xie Yunbi Xu Jianmin Wan | 2020 | The Crop Journal2020，8，3： | 4 |
| 11 | Integration of genomic selection with doubled-haploid evaluation in hybrid breeding: From GS 1.0 to GS 4.0 and beyond显示文摘DOUBLED-HAPLOID TECHNOLOGY FACES A GREAT CHALLENGE FOR HYBRID BREEDING,Ensuring food security for the ever-growing population is a common mission and a great challenge for agricultural scientists worldwide.Historically,advances in crop breeding and management practices have contributed substantially to crop productivity.Indeed,the substantial increase in global grain yields over the last eight decades is largely due to the adoption of hybrids.However,the rate of increase of hybrid yields began to slow down in the early 2000s,and since then,it has reached a plateau for many crops and regions(http://gffzzfa4d2e2b911e4942sox6x0bxp0w0w6fvp.ffgz.tsg.suse.edu.cn).Therefore,we must find solutions to accelerating genetic gain and boost hybrid development,for which developing new breeding technologies provides novel creative opportunities. | Junjie Fu Yangfan Hao Huihui Li Jochen C.Reif Shaojiang Chen Changling Huang Guoying Wang Xinhai Li Yunbi Xu Liang Li | 2022 | Molecular Plant2022，15，4： | 2 |
| 12 | Identification and functional characterization of the AGO1 ortholog in maize显示文摘Eukaryotic Argonaute proteins play primary roles in mi RNA and si RNA pathways that are essential for numerous developmental and biological processes. However, the functional roles of the four Zm AGO1 genes have not yet been characterized in maize(Zea mays L.). In the present study, Zm AGO1 a was identified from four putative Zm AGO1 genes for further characterization. Complementation of the Arabidopsis ago1-27 mutant with Zm AGO1 a indicated that constitutive overexpression of Zm AGO1 a could restore the smaller rosette, serrated leaves, later flowering and maturation, lower seed set, and darker green leaves at late stages of the mutant to the wild-type phenotype. The expression profiles of Zm AGO1 a under five different abiotic stresses indicated that Zm AGO1 a shares expression patterns similar to those of Argonaute genes in rice, Arabidopsis, and wheat.Further, variation in Zm AGO1 a alleles among diverse maize germplasm that resulted in several amino acid changes revealed genetic diversity at this locus. The present data suggest that Zm AGO1 a might be an important AGO1 ortholog in maize. The results presented provide further insight into the function of ZmAGO1a. | Dongdong Xu Hailong Yang Cheng Zou Wen-Xue Li Yunbi Xu Chuanxiao Xie | 2016 | Journal of Integrative Plant Biology2016，58，8： | 2 |
| 13 | Extension of the rice DH population genetic map with microsatellite markers显示文摘Genetic mapping of microsatellite markers was carried out in a rice DH population derived from a cross between Zaiyeqing 8 (indica) and Jingxi 17 (japonica). A total of 89 microsatellite markers, including 84 (GA)-n, 2(TCT) n, 2(ATT)-n and 1(ATC) n motifs, were integrated relatively evenly into the established genetic map of the DH population. This will facilitate the utilization of microsatillite markers in rice gene mapping and marker aided breeding. | Yunbi Xu Lishuang Shen Susan R. McCouch Lihuang Zhu | 1998 | Chinese Science Bulletin1998，43，2： | 2 |
| 14 | Architecture design of low-power motion estimation based on DHS-NPDS for H.264/AVC显示文摘A novel architecture of motion estimation(ME) based on improved normalized partial distortion search is proposed to meet three primary requirements for real-time video encoding,which are low-power,lowbandwidth and high area utilization efficiency.The ME engine supports both normalized partial distortion search and adaptive search window adjustment.The former can reduce the computational complexity of ME to save power and area;the latter can avoid unnecessary accessing the external memory to lower the data bandwidth.The proposed engine has been implemented with UMC 90nm CMOS technology.The implementation results show that,compared with traditional engines,the engine can achieve significant improvements of the hardware efficiency and the power efficiency respectively with a little throughput compromise. | CHEN YunBi LI ZhengDong GUO Li XIE JinSheng ZHAO Long | 2012 | Science China(Information Sciences)2012，55，10： | 2 |
| 15 | The Impact of Land Policy on the Relation between Housing and Land Prices:Evidence from China显示文摘 | Hongyan Du Yongkai Ma Yunbi An | | 0，，01： | 1 |
| 16 | Comparison of SSR and SNPs in assessment of genetic relatedness in maize显示文摘 | Xiaohong Yang Yunbi Xu Trushar Shah | 2011 | Genetica2011，139，： | 1 |
| 17 | Efficiency of selective genotyping for genetic analysis of complex traits and potential applications in crop improvement显示文摘 | Yanping Sun Jiankang Wang Jonathan H. Crouch Yunbi Xu | 2010 | Molecular Breeding2010，，3： | 1 |
| 18 | Development of a seed DNA-based genotyping system for marker-assisted selection in maize显示文摘 | Shibin Gao Carlos Martinez Debra J. Skinner Alan F. Krivanek Jonathan H. Crouch Yunbi Xu | 2008 | Molecular Breeding2008，，3： | 1 |
| 19 | Maize HapMap2 identifies extant variation from a genome in flux 显示文摘 | Jer-Ming Chia Chi Song Peter J Bradbury Denise Cos- tich Natalia de Leon John Doebley Robert J Elshire Brandon Gaut Laura Geller Jeffrey C Glaubitz Michael Gore Kate E Guill Jim Holland Matthew B Hufford Jinsheng Lai Meng Li Xin Liu Yanli Lu Richard Mc- Combie Rebecca Nelson Jesse Poland Boddupalli M Prasanna Tanja Pyhajarvi Tingzhao Rong Rajandeep S Sekhon Qi Sun Maud I Tenaillon Feng Tian Jun Wang Xun Xu Zhiwu Zhang Shawn M Kaeppler Jef- frey Ross-lbarra Michael D McMullen Edward S Buck- ler Gengyun Zhang Yunbi Xu Doreen Ware | 2012 | Nat Genet2012，，44： | 1 |
| 20 | The Impact of Land Policy on the Relation between Housing and Land Prices:Evidence from China 显示文摘 | Du Hongyan Yongkai Ma and Yunbi An | 2011 | The Quarterly Review of Eco- nomics and Finance2011，51，1： | 1 |
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