Person: Yunbi Xu
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Yunbi Xu
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Yunbi Xu
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- Maize genomics and molecular breeding in China: potential benefits of global outreach(CIMMYT, 2019) Yunbi Xu
Publication - Molecular Assisted Breeding in Maize(CIMMYT, 2018) Olsen, M.; Nair, S.K.; Gedil, M.; Xuecai Zhang; Yunbi Xu; Gowda, M.; Beyene, Y.; Ogugo, V.; N’ganga, M.; Murithi, A.
Publication - Factors affecting genomic selection revealed by empirical evidence in maize(Elsevier, 2018) Xiaogang Liu; Hongwu Wang; Hui Wang; Zifeng Guo; Xiaojie Xu; Jiacheng Liu; Shanhong Wang; Wen-Xue Li; Cheng Zou; Prasanna, B.M.; Olsen, M.; Changling Huang; Yunbi XuGenomic 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; rMG showed 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), BayesA, BayesB, BayesC, 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.
Publication - Large-scale evaluation of maize germplasm for low-phosphorus tolerance(Public Library of Science, 2015) Hongwei Zhang; Ruineng Xu; Chuanxiao Xie; Changling Huang; Hong Liao; Yunbi Xu; Jiankang Wang; Wen-Xue LiLow-phosphorus (LP) stress is a global problem for maize production and has been exacerbated by breeding activities that have reduced the genetic diversity of maize. Although LP tolerance in maize has been previously evaluated, the evaluations were generally performed with only a small number of accessions or with samples collected from a limited area. In this research, 826 maize accessions (including 580 tropical/subtropical accessions and 246 temperate accessions) were evaluated for LP tolerance under field conditions in 2011 and 2012. Plant height (PH) and leaf number were measured at three growth stages. The normalized difference vegetation index (NDVI) and fresh ear weight (FEW) were also measured. Genetic correlation analysis revealed that FEW and NDVI were strongly correlated with PH, especially at later stages. LP-tolerant and -sensitive accessions were selected based on the relative trait values of all traits using principal component analysis, and all the 14 traits of the tolerant maize accessions showed less reduction than the sensitive accessions under LP conditions. LP tolerance was strongly correlated with agronomic performance under LP stress conditions, and both criteria could be used for genetic analysis and breeding of LP tolerance. Temperate accessions showed slightly better LP tolerance than tropical/subtropical ones, although more tolerant accessions were identified from tropical/subtropical accessions, which could be contributed by their larger sample size. This large-scale evaluation provides useful information, LP-tolerant germplasm resources and evaluation protocol for genetic analysis and developing maize varieties for LP tolerance.
Publication - Identification of candidate genes for drought tolerance by whole-genome resequencing in maize(Springer Nature, 2014) Jie Xu; Yuan, Y.; Yunbi Xu; Gengyun Zhang; Guo, X.; Wu, F.; Wang, Q; Tingzhao Rong; Pan, G.; Cao, M.; Tang, Q.; Shibin Gao; Yaxi Liu; Jing Wang; Hai Lan; Lu, Y.Drought stress is one of the major limiting factors for maize production. With the availability of maize B73 reference genome and whole-genome resequencing of 15 maize inbreds, common variants (CV) and clustering analyses were applied to identify non-synonymous SNPs (nsSNPs) and corresponding candidate genes for drought tolerance. A total of 524 nsSNPs that were associated with 271 candidate genes involved in plant hormone regulation, carbohydrate and sugar metabolism, signaling molecules regulation, redox reaction and acclimation of photosynthesis to environment were detected by CV and cluster analyses. Most of the nsSNPs identified were clustered in bin 1.07 region that harbored six previously reported QTL with relatively high phenotypic variation explained for drought tolerance. Genes Ontology (GO) analysis of candidate genes revealed that there were 35 GO terms related to biotic stimulus and membrane-bounded organelle, showing significant differences between the candidate genes and the reference B73 background. Changes of expression level in these candidate genes for drought tolerance were detected using RNA sequencing for fertilized ovary, basal leaf meristem tissue and roots collected under drought stressed and well-watered conditions. The results indicated that 70% of candidate genes showed significantly expression changes under two water treatments and our strategies for mining candidate genes are feasible and relatively efficient. Our results successfully revealed candidate nsSNPs and associated genes for drought tolerance by comparative sequence analysis of 16 maize inbred lines. Both methods we applied were proved to be efficient for identifying candidate genes for complex traits through the next-generation sequencing technologies (NGS). These selected genes will not only facilitate understanding of genetic basis of drought stress response, but also accelerate genetic improvement through marker-assisted selection in maize.
Publication - Combined linkage and association mapping reveal QTL for host plant resistance to common rust (Puccinia sorghi) in tropical maize(BMC (part of Springer Nature), 2018) Hongjian Zheng; Jiafa Chen; Chunhua Mu; Makumbi, D.; Yunbi Xu; Mahuku, G.Background: Common rust, caused by Puccinia sorghi, is an important foliar disease of maize that has been associated with up to 50% grain yield loss. Development of resistant maize germplasm is the ideal strategy to combat P. sorghi. Results: Association mapping performed using a mixed linear model (MLM), integrating population structure and family relatedness identified 25 QTL (P<3.12×10-5) that were associated with resistance to common rust and distributed on chromosomes 1, 3, 5, 6, 8, and 10. We identified three QTLs associated with all three disease parameters (final disease rating, mean disease rating, and area under disease progress curve) located on chromosomes 1, 3, and 8. A total of 5 QTLs for resistance to common rust were identified in the RIL population. Nine candidate genes located on chromosomes 1, 5, 6, 8, and 10 for resistance to common rust associated loci were identified through detailed annotation. Conclusions: Using a diverse set of inbred lines genotyped with high density markers and evaluated for common rust resistance in multiple environments, it was possible to identify QTL significantly associated with resistance to common rust and several candidate genes. The results point to the need for fine mapping common rust resistance by targeting regions identified in common between this study and others using diverse germplasm.
Publication - Molecular assisted breeding in maize(CIMMYT, 2017) Olsen, M.; Nair, S.K.; Gedil, M.; Xuecai Zhang; Huestis, G.M; Yunbi Xu; Gowda, M.; Beyene, Y.
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