Person: Hui Wang
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Hui Wang
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Hui Wang
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- Integrative transcriptome and metabolome analysis reveals the mechanisms of light-induced pigmentation in purple waxy maize(Frontiers Media S.A., 2023) Yuan Lu; Yao Yu; Yanfang Xuan; Ayiguli Kari; Caixia Yang; Chenyu Wang; Chao Zhang; Wei Gu; Hui Wang; Yingxiong Hu; Pingdong Sun; Yuan Guan; Wenshuai Si; Bing Bai; Xuecai Zhang; Yunbi Xu; Prasanna, B.M.; Biao Shi; Hongjian Zheng
Publication - Genome-wide association study and genomic prediction on plant architecture traits in sweet corn and waxy corn(MDPI, 2023) Dang, D.; Yuan Guan; Hongjian Zheng; Xuecai Zhang; Ao Zhang; Hui Wang; Yanye Ruan; Qing Li
Publication - Epistasis activation contributes substantially to heterosis in temperate by tropical maize hybrids(Frontiers Media S.A., 2022) Zhiqin Sang; Hui Wang; Yuxin Yang; Zhanqin Zhang; Xiaogang Liu; Zhiwei Li; Yunbi Xu
Publication - Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize(Elsevier, 2022) Ao Zhang; Pérez-Rodríguez, P.; San Vicente Garcia, F.M.; Palacios-Rojas, N.; Dhliwayo, T.; Yubo Liu; Zhenhai Cui; Yuan Guan; Hui Wang; Hongjian Zheng; Olsen, M.; Prasanna, B.M.; Yanye Ruan; Crossa, J.; Xuecai Zhang
Publication - Large-scale analysis of combining ability and heterosis for development of hybrid maize breeding strategies using diverse germplasm resources(Frontiers, 2020) Kanchao Yu; Hui Wang; Xiaogang Liu; Cheng Xu; Zhiwei Li; Xiaojie Xu; Jiacheng Liu; Zhenhua Wang; Yunbi Xu
Publication - Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm(Elsevier, 2021) Yubo Liu; Guanghui Hu; Ao Zhang; Loladze, A.; Yingxiong Hu; Hui Wang; Jingtao Qu; Xuecai Zhang; Olsen, M.; San Vicente Garcia, F.M.; Crossa, J.; Feng Lin; Prasanna, B.M.
Publication - Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing(Springer, 2020) Nan Wang; Hui Wang; Ao Zhang; Yubo Liu; Diansi Yu; Zhuanfang Hao; Ilut, D.; Glaubitz, J.C.; Yanxin Gao; Jones, E.; Olsen, M.; Xinhai Li; San Vicente Garcia, F.M.; Prasanna, B.M.; Crossa, J.; Pérez-Rodríguez, P.; Xuecai Zhang
Publication - Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding(Nature Publishing Group, 2020) Nan Wang; Yibing Yuan; Hui Wang; Diansi Yu; Yubo Liu; Ao Zhang; Gowda, M.; Nair, S.K.; Zhuanfang Hao; Yanli Lu; San Vicente Garcia, F.M.; Prasanna, B.M.; Xinhai Li; Xuecai Zhang
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 - Development of a multiple-hybrid population for genome-wide association studies: theoretical consideration and genetic mapping of flowering traits in maize(Nature Publishing Group, 2017) Hui Wang; Cheng Xu; Xiaogang Liu; Zifeng Guo; Xiaojie Xu; Shanhong Wang; Chuanxiao Xie; Wen-Xue Li; Cheng Zou; Yunbi XuVarious types of populations have been used in genetics, genomics and crop improvement, including bi- and multi-parental populations and natural ones. The latter has been widely used in genome-wide association study (GWAS). However, inbred-based GWAS cannot be used to reveal the mechanisms involved in hybrid performance. We developed a novel maize population, multiple-hybrid population (MHP), consisting of 724 hybrids produced using 28 temperate and 23 tropical inbreds. The hybrids can be divided into three subpopulations, two diallels and NC (North Carolina Design) II. Significant genetic differences were identified among parents, hybrids and heterotic groups. A cluster analysis revealed heterotic groups existing in the parental lines and the results showed that MHPs are well suitable for GWAS in hybrid crops. MHP-based GWAS was performed using 55 K SNP array for flowering time traits, days to tassel, days to silk, days to anthesis and anthesis-silking interval. Two independent methods, PEPIS developed for hybrids and TASSEL software designed for inbred line populations, revealed highly consistent results with five overlapping chromosomal regions identified and used for discovery of candidate genes and quantitative trait nucleotides. Our results indicate that MHPs are powerful in GWAS for hybrid-related traits with great potential applications in the molecular breeding era.
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