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Jiankang Wang

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Jiankang Wang
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Jiankang Wang

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    Genetic diversity and selection signatures in synthetic-derived wheats and modern spring wheat
    (Frontiers, 2022) Ali, M.; Shan, D.; Jiankang Wang; Sadiq, H.; Rasheed, A.; He Zhonghu; Huihui Li
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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
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    Genome-wide analyses reveal footprints of divergent selection and popping-related traits in CIMMYT’s maize inbred lines
    (Oxford University Press, 2021) Jing Li; Delin Li; Zavala Espinosa, C.; Trejo Pastor, V.; Rasheed, A.; Palacios-Rojas, N.; Jiankang Wang; Santacruz-Varela, A.; De Almeida Silva, N.C.; Schnable, P.S.; Costich, D.E.; Huihui Li
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    Characterization of genetic diversity and genome-wide association mapping of three agronomic traits in Qingke barley (Hordeum Vulgare L.) in the Qinghai-Tibet Plateau
    (Frontiers, 2020) Zhiyong Li; Lhundrup, N.; Guo, G.; Dol, K.; Panpan Chen; Liyun Gao; Wangmo Chemi; Jing Zhang; Jiankang Wang; Tashi Nyema; Dondrup Dawa; Huihui Li
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    Quantitative genetic studies with applications in plant breeding in the omics era
    (Elsevier, 2020) Jiankang Wang; Crossa, J.; Junyi Gai
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    CGIAR modeling approaches for resource‐constrained scenarios: I. Accelerating crop breeding for a changing climate
    (Crop Science Society of America (CSSA), 2020) Ramirez-Villegas, J.; Molero Milan, A.; Alexandrov, N.; Asseng, S.; Challinor, A.; Crossa, J.; Van Eeuwijk, F.A.; Ghanem, M.E.; Grenier, C.; Heinemann, A.B.; Jiankang Wang; Juliana, P.; Kehel, Z.; Kholova, J.; Koo, J.; Pequeno, D.N.L.; Quiroz, R.; Rebolledo, C.; Sukumaran, S.; Vadez, V.; White, J.W.; Reynolds, M.P.
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    Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS
    (Wiley, 2019) Jing Li; Gou-Bo Chen; Rasheed, A.; Delin Li; Sonder, K.; Zavala Espinosa, C.; Jiankang Wang; Costich, D.E.; Schnable, P.S.; Hearne, S.; Huihui Li
    Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high-quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom-up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large-scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding.
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    Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)
    (Elsevier, 2018) Ji Yao; Dehui Zhao; Xinmin Chen; Yong Zhang; Jiankang Wang
    In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory, the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models (RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection (GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value (GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than mid parent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.
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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.
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