Analysis of genetic factors influencing transformation efficiency of Rosa hybrida cultivars

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Nguyen, Thi Hong Nhung: Analysis of genetic factors influencing transformation efficiency of Rosa hybrida cultivars. Hannover : Gottfried Wilhelm Leibniz Universität, Diss., 2019, vi, 156 S. DOI: https://doi.org/10.15488/7465

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Abstract: 
The production of ornamental roses makes substantial contributions to the global floriculture industry; furthermore, roses have been used for medicine, perfume and food purposes for centuries and are among the top five ornamentals worldwide. However, the traditional methods for breeding roses are time-consuming and may have unwittingly eliminated agronomically useful traits. One of the alternatives is genetic transformation, an efficient technology for improving useful agronomic rose traits without these limitations. To improve the efficiency of transformations in the rose, the propagation and regeneration capacity of 96 rose genotypes were investigated to find suitable varieties for regeneration and micropropagation, as well as for genetic modifications. By combining genetic analysis and association mapping, candidate genes associated with regenerating and propagating traits were identified. For phenotypic analyses, the shoot regeneration and in vitro propagation traits of 96 rose genotypes were investigated. Shoot regeneration rates varied significantly between genotypes, with values from 0.88–88.33%, and shoot ratios (number of shoots per explant) varied from 0.008–1.2. Significant differences in callus size on CIM1 (callus inducing medium 1) were observed on a scale of 0–4 and 0.82–4 on CIM2. Significant variation in shoot multiplication rate was found with variation from 0.5–4.24 among genotypes. Significant variation in in vitro root number (ranging from 0.12–18.7), root length (0.26–25.76 cm) as well as in vivo root number, root length and root biomass were recorded among the genotypes. These analyses indicated significant genetic influence acting on these traits.For genetic analysis, GWAS (Genome Wide Association Study) was performed to detect the molecular markers associated with the traits (root and shoot characteristics as well as callus formation). In this analysis, 12 SNP (Single Nucleotide Polymorphism) markers from ESTs (Expressed Sequence Tags) matching known candidate genes involved in shoot morphogenesis were detected. For callus formation, 26 SNPs that are significantly associated with callus formation on CIM1 and 13 SNPs significantly associated with callus formation on CIM2 were found. A total of 6 SNPs were found to be significantly associated with shoot multiplication rate. For rooting traits, 49 SNPs were significantly associated with in vitro root length, 98 SNPs were associated with in vivo root number, 218 SNPs were associated with in vivo root length and 4 SNPs were associated with in vivo root biomass. Additionally, by using the KASP (kompetitive allelspezifische PCR) technology to verify significantly associated markers for shoot organogenesis in other populations of garden roses, the trihelix transcription factor GT2-like (Rh12GR_53908_964P) and a putative leucine-rich repeat receptor-like protein kinase (Rh12GR_21560_124Q) were determined to influence shoot organogenesis in roses. Other detected markers should be used in future experiments to validate the genes in other populations and examine their functionality in transgenic approaches.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: doctoralThesis
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Naturwissenschaftliche Fakultät
Dissertationen

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pos. country downloads
total perc.
1 image of flag of Germany Germany 92 32.17%
2 image of flag of United States United States 46 16.08%
3 image of flag of France France 15 5.24%
4 image of flag of Russian Federation Russian Federation 12 4.20%
5 image of flag of Ecuador Ecuador 12 4.20%
6 image of flag of Czech Republic Czech Republic 12 4.20%
7 image of flag of China China 9 3.15%
8 image of flag of Korea, Republic of Korea, Republic of 8 2.80%
9 image of flag of India India 8 2.80%
10 image of flag of Netherlands Netherlands 6 2.10%
    other countries 66 23.08%

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