Person:
Xiaogang Liu

Profile Picture
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Xiaogang Liu
First Name
Name
Xiaogang Liu
ORCID ID
0000-0003-2727-8582

Filters

2017 - 201922020 - 20225
Reset filters

Settings

Search Results

Now showing 1 - 7 of 7
  • Thumbnail Image
    Heterosis and heterotic patterns of maize germplasm revealed by a multiple-hybrid population under well-watered and drought-stressed conditions
    (Elsevier BV., 2022) Zhiqin Sang; Zhanqin Zhang; Yuxin Yang; Zhiwei Li; Xiaogang Liu; Yunbi Xu; Weihua Li
    Publication
  • Thumbnail Image
    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
  • Thumbnail Image
    Enhancing genetic gain through genomic selection: from livestock to plants
    (Elsevier, 2020) Yunbi Xu; Xiaogang Liu; Junjie Fu; Hongwu Wang; Jiankang Wang; Changling Huang; Prasanna, B.M.; Olsen, M.; Guoying Wang; Zhang Aimin
    Publication
  • Thumbnail Image
    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
  • Thumbnail Image
    Favorable haplotypes and associated genes for flowering time and photoperiod sensitivity identified by comparative selective signature analysis and GWAS in temperate and tropical maize
    (Institute of Crop Sciences, 2020) Zhiwei Li; Xiaogang Liu; Xiaojie Xu; Jiacheng Liu; Zhiqin Sang; Kanchao Yu; Yuxin Yang; Wenshuang Dai; Xin Jin; Yunbi Xu
    Publication
  • Thumbnail Image
    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 Xu
    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; 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
  • Thumbnail Image
    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 Xu
    Various 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.
    Publication