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Changling Huang

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Changling Huang
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Changling Huang

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    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
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    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
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    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 Li
    Low-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