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Hongwu Wang

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Hongwu Wang
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Hongwu Wang

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Now showing 1 - 4 of 4
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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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    Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations
    (Frontiers, 2017) Ao Zhang; Hongwu Wang; Beyene, Y.; Semagn, K.; Yubo Liu; Shiliang Cao; Zhenhai Cui; Yanye Ruan; Burgueño, J.; San Vicente Garcia, F.M.; Olsen, M.; Prasanna, B.M.; Crossa, J.; Haiqiu Yu; Xuecai Zhang
    Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.
    Publication
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    Development of a maize 55 K SNP array with improved genome coverage for molecular breeding
    (Springer Verlag, 2017) Cheng Xu; Yonghong Ren; Yinqiao Jian; Zifeng Guo; Zhang, Y.; Chuanxiao Xie; Junjie Fu; Hongwu Wang; Guoying Wang; Yunbi Xu; Zhang Li-Ping; Cheng Zou
    With the decrease of cost in genotyping, single nucleotide polymorphisms (SNPs) have gained wide acceptance because of their abundance, even distribution throughout the maize (Zea mays L.) genome, and suitability for high-throughput analysis. In this study, a maize 55 K SNP array with improved genome coverage for molecular breeding was developed on an Affymetrix® Axiom® platform with 55,229 SNPs evenly distributed across the genome, including 22,278 exonic and 19,425 intronic SNPs. This array contains 451 markers that are associated with 368 known genes and two traits of agronomic importance (drought tolerance and kernel oil biosynthesis), 4067 markers that are not covered by the current reference genome, 734 markers that are differentiated significantly between heterotic groups, and 132 markers that are tags for important transgenic events. To evaluate the performance of 55 K array, we genotyped 593 inbred lines with diverse genetic backgrounds. Compared with the widely-used Illumina® MaizeSNP50 BeadChip, our 55 K array has lower missing and heterozygous rates and more SNPs with lower minor allele frequency (MAF) in tropical maize, facilitating in-depth dissection of rare but possibly valuable variation in tropical germplasm resources. Population structure and genetic diversity analysis revealed that this 55 K array is also quite efficient in resolving heterotic groups and performing fine fingerprinting of germplasm. Therefore, this maize 55 K SNP array is a potentially powerful tool for germplasm evaluation (including germplasm fingerprinting, genetic diversity analysis, and heterotic grouping), marker-assisted breeding, and primary quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) for both tropical and temperate maize.
    Publication