請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53747
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鍾嘉綾(Chia-Lin Chung) | |
dc.contributor.author | Heng-An Lin | en |
dc.contributor.author | 林珩安 | zh_TW |
dc.date.accessioned | 2021-06-16T02:28:50Z | - |
dc.date.available | 2020-08-06 | |
dc.date.copyright | 2015-08-06 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-03 | |
dc.identifier.citation | Abramoff, M. D., Magalhães, P. J., & Ram, S. J. 2004. Image processing with ImageJ. Biophotonics international, 11, 36-42.
Amante-Bordeos, A., Sitch, L. A., Nelson, R., Dalmacio, R. D., Oliva, N. P., Aswidinnoor, H., & Leung, H. 1992. Transfer of bacterial blight and blast resistance from the tetraploid wild rice Oryza minuta to cultivated rice, Oryza sativa. Theoretical and Applied Genetics, 84, 345-354. Ammiraju, J. S. S., Luo, M., Goicoechea, J. L., Wang, W., Kudrna, D., Mueller, C., . . . Wing, R. A. 2006. The Oryza bacterial artificial chromosome library resource: construction and analysis of 12 deep-coverage large-insert bac libraries that represent the 10 genome types of the genus Oryza. Genome Research, 16(1), 140-147. Ashikari, M., Sakakibara, H., Lin, S., Yamamoto, T., Takashi, T., Nishimura, A., . . . Matsuoka, M. 2005. Cytokinin oxidase regulates rice grain production. Science, 309(5735), 741-745. Ballini, E., Morel, J. B., Droc, G., Price, A., Courtois, B., Notteghem, J. L., & Tharreau, D. 2008. A genome-wide meta-analysis of rice blast resistance genes and quantitative trait loci provides new insights into partial and complete resistance. Molecular Plant-Microbe Interactions, 21(7), 859-868. Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. Bastien, M., Sonah, H., & Belzile, F. 2014. Genome wide association mapping of resistance in soybean with a genotyping-by-sequencing approach. The Plant Genome, 7, 1-13. Begum, H., Spindel, J. E., Lalusin, A., Borromeo, T., Gregorio, G., Hernandez, J., . . . McCouch, S. R. 2015. Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLoS ONE, 10(3), e0119873. Brachi B, Morris GP, & JO, B. 2011. Genome-wide association studies in plants the missing heritability is in the field. Genome biology, 12, 232. Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 23(19), 2633-2635. Braulio J. Soto-Cerda, & Cloutier, S. (2012). Chapter 3: Association mapping in plant genomoes InTech. Browning, S. R., & Browning, B. L. 2007. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Genetics, 81(5), 1084-1097. Bu, L. L. (2013). The study on genetic diversity of Magnaporthe oryzae in Taiwan. (Master), National Chiayi University Bush, W. S., & Moore, J. H. 2012. Chapter 11: Genome-wide association studies. PLoS Computational Biology, 8(12), e1002822. Chen, L.-C., Huang, S.-H., & Cheng, C.-H. 2009 Review of resistance screening of pest and pathogen in rice Proceedings of Symposium on Achievements and Perspectives of Rice Protection in Taiwan. Chen, R. K., Tsai, M. H., & Chen, K. Y. 2013. The construction of random-type SNP molecular marker database for Taiwanese japonica rice varieties. Research Bulletin of Taiwan District Agricultural Improvement Station, 61, 15-28. Cheng, S.-H. 2013. Crops production and food safty in Taiwan New Century Thinktank Forum, 64, 34-37. Cooper, J. D., Smyth, D. J., Smiles, A. M., Plagnol, V., Walker, N. M., Allen, J., . . . Todd, J. A. 2008. Meta-analysis of genome-wide association study data identifies additional type 1 diabetes loci. Nat Genet, 40(12), 1399-1401. Doyle, J. J., & Doyle, J. L. 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin, 19, 11-15. Duggal, P., Gillanders, E. M., Holmes, T. N., & Bailey-Wilson, J. E. 2008. Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies. BMC Genomics, 9, 516. Eizenga, G. C., Ali, M. L., Bryant, R. J., Yeater, K. M., McClung, A. M., & McCouch, S. R. 2014. Registration of the rice diversity panel 1 for genomewide association studies. Journal of Plant Registrations, 8(1), 109. Elshire, R., Glaubitz, J., Sun, Q., Poland, J., Kawamoto, K., Buckler, E., & Mitchell, S. 2011. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE, 6, e19379. Famoso, A. N., Zhao, K., Clark, R. T., Tung, C. W., Wright, M. H., Bustamante, C., . . . McCouch, S. R. 2011. Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet, 7(8), e1002221. FAO. 2014. Rice market monitor : production, international trade, rice utilization and domestic prices Food and Agriculture Organization of the United Nations(4), 1-23. Flint-Garcia, S. A., Thornsberry, J. M., S, E., & IV, B. 2003. Structure of linkage disequilibrium in plants. Annual Review of Plant Biology, 54(1), 357-374. Garris, A. J., Tai, T. H., Coburn, J., Kresovich, S., & McCouch, S. 2005. Genetic structure and diversity in Oryza sativa L. Genetics, 169(3), 1631-1638. Hayashi, N., Ando, I., & Imbe, T. 1998. Identification of a new resistance gene to a chinese blast fungus isolate in the japanese rice cultivar, Aichi Asahi. Phytopathology, 88(8), 822-827. Huang, J., Si, W., Deng, Q., & Yang, S. 2014. Rapid evolution of avirulence genes in rice blast fungus Magnaporthe oryzae. BMC Genetics, 15:45. Huang, X., & Han, B. 2014. Natural variations and genome-wide association studies in crop plants. Annual Review of Plant Biology, 65(1), 531-551. Huang, X., Wei, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., . . . Han, B. 2010. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet, 42(11), 961-967. Huang, X., Zhao, Y., Wei, X., Li, C., Wang, A., Zhao, Q., . . . Han, B. 2012. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet, 44(1), 32-39. IRRI. 1996. Standard evaluation system for rice. . Fourth edition. Manila, Philippines, IRRI. Jansen, R. C., & Stam, P. 1994. High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136(4), 1447-1455. Jeff Glaubitz, R. E., Terry Casstevens, James Harriman, Ed Buckler. (2013). TASSEL 3 Genotyping by Sequencing (GBS) pipeline documentation. Jeung, J. U., Kim, B. R., Cho, Y. C., Han, S. S., Moon, H. P., Lee, Y. T., & Jena, K. K. 2007. A novel gene, Pi40(t), linked to the DNA markers derived from NBS-LRR motifs confers broad spectrum of blast resistance in rice. Theoretical and Applied Genetics, 115(8), 1163-1177. Kawahara, Y., de la Bastide, M., Hamilton, J., Kanamori, H., McCombie, W., Ouyang, S., . . . Matsumoto, T. 2013. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice, 6(1), 1-10. Khush, G. S. 1997. Origin, dispersal, cultivation and variation of rice Plant Molecular Biology, 35, 25-34. Korinsak, S., Sirithunya, P., Meakwatanakarn, P., Sarkarung, S., Vanavichit, A., & Toojinda, T. 2011. Changing allele frequencies associated with specific resistance genes to leaf blast in backcross introgression lines of Khao Dawk Mali 105 developed from a conventional selection program. Field Crops Research, 122(1), 32-39. Korte, A., & Farlow, A. 2013. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods, 9(29). Kovach, M. J., Sweeney, M. T., & McCouch, S. R. 2007. New insights into the history of rice domestication. Trends in Genetics, 23(11), 578-587. Kumar, V., Singh, A., Mithra, S. V. A., Krishnamurthy, S. L., Parida, S. K., Jain, S., . . . Mohapatra, T. 2015. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Research. Lamari, L. (2008). Assess 2.0 Image Analysis Software for Plant Disease Quantification. St. Paul, MN: American Phytopathological Society. Lander, E. S., & Botstein, D. 1989. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics, 121(1), 185-199. Langmead, B., & Salzberg, S. L. 2012. Fast gapped-read alignment with Bowtie 2. Nat Meth, 9(4), 357-359. Li, D., Liu, H., Zhang, H., Wang, X., & Song, F. 2008. OsBIRH1, a DEAD-box RNA helicase with functions in modulating defence responses against pathogen infection and oxidative stress. J Exp Bot, 59(8), 2133-2146. doi: 10.1093/jxb/ern072 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., . . . Genome Project Data Processing, S. 2009. The sequence alignment/map format and SAMtools. Bioinformatics, 25(16), 2078-2079. Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. 2010. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol, 34(8), 816-834. Lin, Z., Griffith, M. E., Li, X., Zhu, Z., Tan, L., Fu, Y., . . . Sun, C. 2007. Origin of seed shattering in rice (Oryza sativa L.). Planta, 226(1), 11-20. Londo, J. P., Chiang, Y.-C., Hung, K.-H., Chiang, T.-Y., & Schaal, B. A. 2006. Phylogeography of Asian wild rice, Oryza rufipogon, reveals multiple independent domestications of cultivated rice, Oryza sativa. Proceedings of the National Academy of Sciences, 103(25), 9578-9583. Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. 2007. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet, 39(7), 906-913. Marone, D., Russo, M. A., Laido, G., De Leonardis, A. M., & Mastrangelo, A. M. 2013. Plant nucleotide binding site-leucine-rich repeat (NBS-LRR) genes: active guardians in host defense responses. Int J Mol Sci, 14(4), 7302-7326. Mather, K. A., Caicedo, A. L., Polato, N. R., Olsen, K. M., McCouch, S., & Purugganan, M. D. 2007. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics, 177(4), 2223-2232. McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. 2008. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet, 9(5), 356-369. McDowell, J. M., & Woffenden, B. J. 2003. Plant disease resistance genes: recent insights and potential applications. Trends in Biotechnology, 21(4), 178-183. McNally, K. L., Childs, K. L., Bohnert, R., Davidson, R. M., Zhao, K., Ulat, V. J., . . . Leach, J. E. 2009. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences, 106(30), 12273-12278. Miah, G., Rafii, M. Y., Ismail, M. R., Puteh, A. B., Rahim, H. A., Asfaliza, R., & Latif, M. A. 2013. Blast resistance in rice: a review of conventional breeding to molecular approaches. Molecular Biology Reports, 40(3), 2369-2388. Morris, G. P., Ramu, P., Deshpande, S. P., Hash, C. T., Shah, T., Upadhyaya, H. D., . . . Kresovich, S. 2013. Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci U S A, 110(2), 453-458. doi: 10.1073/pnas.1215985110 Norton, G. J., Douglas, A., Lahner, B., Yakubova, E., Guerinot, M. L., Pinson, S. R. M., . . . Price, A. H. 2014. Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa l.) grown at four international field sites. PLoS ONE. NR, W. 2005. Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Research and Human Genetics, 8(2), 87-94. Okuyama, Y., Kanzaki, H., Abe, A., Yoshida, K., Tamiru, M., Saitoh, H., . . . Terauchi, R. 2011. A multifaceted genomics approach allows the isolation of the rice Pia-blast resistance gene consisting of two adjacent NBS-LRR protein genes. The Plant Journal, 66(3), 467-479. Pan, Q., Wang, L., Ikenashi, H., & Tabisaka, T. 1996. Identification of a new blast resistance gene in the indica rice cultivar Kasalath using Japanese differential cultivars and isozyme markers. Phytopathology, 86(1071-1075). Plank, J. E. V. D. (1963). Plant disease: epidamics and control Academic Press. Ramkumar, G., Madhav, M. S., Rama Devi, S. J. S., Manimaran, P., Mohan, K. M., Prasad, M. S., . . . Viraktamath, B. C. 2014. Nucleotide diversity of Pita, a major blast resistance gene and identification of its minimal promoter. Gene, 546(2), 250-256. Rasheed, A., Xia, X., Ogbonnaya, F., Mahmood, T., Zhang, Z., Mujeeb-Kazi, A., & He, Z. 2014. Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis. BMC Plant Biol, 14, 128. Remington, D. L., Thornsberry, J. M., Matsuoka, Y., Wilson, L., Rinehart-Whitt, S., Doebley, J., . . . Buckler, E. S. 2001. Structure of linkage disequilibrium and phenotypic associations in the maize genome Proceeding at National Academy of Science USA, 98, 11479-11484. Ribot, C., Hirsch, J., Balzergue, S., Tharreau, D., Nottéghem, J.-L., Lebrun, M.-H., & Morel, J.-B. 2008. Susceptibility of rice to the blast fungus, Magnaporthe grisea. Journal of Plant Physiology, 165(1), 114-124. Sánchez-Rodríguez, C., Estévez, J. M., Llorente, F., Hernández-Blanco, C., Jordá, L., Pagán, I., . . . Molina, A. 2009. The ERECTA receptor-like kinase regulates cell wall–mediated resistance to pathogens in Arabidopsis thaliana. Molecular Plant-Microbe Interactions, 22(8), 953-963. Sallaud, C., Lorieux, M., Roumen, E., Tharreau, D., Berruyer, R., Svestasrani, P., . . . Notteghem, J. L. 2003. Identification of five new blast resistance genes in the highly blast-resistant rice variety IR64 using a QTL mapping strategy. Theoretical and Applied Genetics, 106(8), 1532-1532. Sequencing Project International Rice, G. 2005. The map-based sequence of the rice genome. Nature, 436(7052), 793-800. Shaobing Peng, & Khush, G. S. 2003. Four decades of breeding for varietal improvement of irrigated lowland rice in the international rice research institute. Plant Production Science, 6(3), 157-164. Sharma, T. R., Rai, A. K., Gupta, S. K., Vijayan, J., Devanna, B. N., & Ray, S. 2012. Rice blast management through host-plant resistance: retrospect and prospects. Agricultural Research, 1(1), 37-52. Shung, J. Y. (2011). Genotypic analysis of Magnaporthe oryzae in Taiwan by molecular markers. (Master), National Chiayi University Slatkin, M. 2008. Linkage disequilibrium-understanding the evolutionary past and mapping the medical future. Nat Rev Genet, 9(6), 477-485. Suwarno Willy, B. (2012). Combining ability, association mapping, and genomic predictions for provitamin a carotenoid concentrations in tropical maize (Zea mays L.). (Doctor of Philosoph), University of wisconsin-madison, US. Tabien, R. E., Li, Z., Paterson, A. H., Marchetti, M. A., Stansel, J. W., Pinson, S. R. M., & Park, W. D. 2000. Mapping of four major rice blast resistance genes from ’Lemont’ and ’Teqing’ and evaluation of their combinatorial effect for field resistance. Theoretical and Applied Genetics, 101(8), 1215-1225. Tabien, R. E., Pinson, S. R. M., Marchetti, M. A., Li, Z., Park, W. D., Paterson, A. H., & Stansel, J. W. (1996). Blast resistance genes from Teqing and Lemont Manila, Philippines: International Rice Research Institute. Tsai, W. H. 2009. Review of rice blast research Proceedings of Symposium on Achievements and Perspectives of Rice Protection in Taiwan, 1-12. Uh, H. W., Deelen, J., Beekman, M., Helmer, Q., Rivadeneira, F., Hottenga, J. J., . . . Houwing-Duistermaat, J. J. 2012. How to deal with the early GWAS data when imputing and combining different arrays is necessary. Eur J Hum Genet, 20(5), 572-576. Utami, D. W., Moeljopawiro, S., Aswidinnoor, H., Setiawan, A., & Hanarida, I. 2008. Blast resistance genes in wild rice Oryza rufipogon and rice cultivar IR64. Indonesian Journal of Agriculture, 1(2), 71-76. Verma, S. S., de Andrade, M., Tromp, G., Kuivaniemi, H., Pugh, E., Namjou-Khales, B., . . . Ritchie, M. D. 2014. Imputation and quality control steps for combining multiple genome-wide datasets. Frontiers in Genetics, 5, 370. Wang, C., Yang, Y., Yuan, X., Xu, Q., Feng, Y., Yu, H., . . . Wei, X. 2014. Genome-wide association study of blast resistance in indica rice. BMC Plant Biol, 14(1), 311. Wang, J., & Shete, S. 2011. A powerful hybrid approach to select top single-nucleotide polymorphisms for genome-wide association study. BMC Genet, 12, 3. Wang, X., Jia, Y., Shu, Q. Y., & Wu, D. 2008. Haplotype diversity at the Pi-ta locus in cultivated rice and its wild relatives. Phytopathology, 98(12), 1305-1311. doi: 10.1094/PHYTO-98-12-1305 Wilson, R. A., & Talbot, N. J. 2009. Under pressure: investigating the biology of plant infection by Magnaporthe oryzae. Nat Rev Micro, 7(3), 185-195. Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., . . . Xiong, L. 2014. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun, 5, 5087. doi: 10.1038/ncomms6087 Yohei, K., Nobuya, K., Donghe, X., & Yoshimichi, F. 2009. Resistance genes and selection DNA markers for blast disease in rice. Japan International Research Center for Agricultural Science, 43(4), 255-280. Yu, J., Hu, S., Wang, J., Wong, G. K., Li, S., Liu, B., . . . Yang, H. 2002. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science, 296(5565), 79-92. Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., Hu, T., . . . Altshuler, D. 2008. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet, 40(5), 638-645. Zeng, Z. B. 1994. Precision mapping of quantitative trait loci. Genetics, 136(4), 1457-1468. Zhang, Li, J., Li, X., Liu, X., Zhao, X., & Lu, Y. 2011. Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS ONE, 6(12), e27565. Zhang, Q., Maroof, M. S., Lu, T. Y., & Shen, B. Z. 1992. Genetic diversity and differentiation of indica and japonica rice detected by RFLP analysis. Theoretical and Applied Genetics, 83(4), 495-499. Zhao, K., Tung, C. W., Eizenga, G. C., Wright, M. H., Ali, M. L., Price, A. H., . . . McCouch, S. R. 2011. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun, 2, 467. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53747 | - |
dc.description.abstract | 稻熱病 (Rice blast) 由子囊真菌Magnaporthe oryzae 引起,為水稻產區每年發生的重要病害,抗病育種為已知最有效及環境友善的防治方法,然而病原菌演化快速,抗病品種在大面積種植後,常於3-5年內失去抗性。為有效防治此病害,必須持續發掘新的抗性基因及了解現有抗性品種的抗性來源。本研究目標是以關聯定位的方法,尋找312多樣性種原及21個商業品種之稻熱病抗性基因(座),應用於抗病育種。在表現型分析中,我們以D41-2和12YL-DL3-2兩株具代表性的本土稻熱病菌株,於生長箱內進行接種試驗並記錄各品種病斑面積百分比、病斑型態及抗感病特性,為減少不同次接種試驗造成的誤差,病斑面積百分比的資料以最佳線性無偏估法 (best linear unbiased predictor, BLUP) 進行資料校正,並選用重複試驗中表現一致的品種資料進行關聯定位分析。在基因型分析部分,則是針對商業品種進行genotyping by sequencing (GBS) 分析,並和多樣性種原的44K SNPs資料以及臺農84號、臺南11號之全基因體次世代定序 (next-generation sequencing, NGS) 資料進行合併。GBS 和 44K SNPs 資料的重疊性小於0.05%,NGS和44K SNPs 資料則是可以正常合併。用D41-2接種資料進行關聯定位分析,共得到57個與抗病顯著相關的區域,其中有26個區域和已知的稻熱病R 基因十分接近,17個區域經過Gramene資料庫比對得到636個候選基因,其餘14個區域並未比對到任何已知R 基因或候選基因。以12YL-DL-3-2接種資料進行分析,共得到60個顯著相關的區域,其中35個區域接近已知R 基因,25個區域經過資料庫比對得到472個候選基因,剩下的區域則沒有比對到任何已知基因。兩組資料共同最高的位點皆落在第12條染色體,接近Pita 抗性基因座的位置。經過單套體 (haplotype) 分析,評估各個抗性品種可能帶有抗性基因座之位置,相關資訊可做為未來抗性基因驗證分析及稻熱病抗病育種之基礎。 | zh_TW |
dc.description.abstract | Rice blast, caused by Magnaporthe oryzae, is an important rice disease annually occurring in rice-growing regions. Resistance breeding is the most effective and environmental-friendly method to manage blast disease. However, the resistance can easily be overcome in 3-5 years due to the fast evolution of M. oryzae. Discovering novel R genes and revealing the resistance genes in commercial varieties can help develop more effective breeding and disease management strategies. This study aimed to use genome-wide association mapping to identify the blast resistance genes in 312 diverse accessions and 21 commercial rice varieties. In phenotyping experiments, two representative M. oryzae isolates, D41-2 and 12YL-DL-3-2, were chosen to inoculate plant materials in the growth chamber. Diseased leaf area, lesion type, and resistance/susceptible reaction were recorded. To eliminate the bias among different inoculation experiments, best linear unbiased predictor (BLUP) values were calculated for the diseased leaf area data. Only the consistent data among different repetitions were chosen for further association analysis. In genotyping experiments, we conducted Illumina next-generation sequencing (NGS) for Tainan 11 (TN11) and Tainung 84 (TNG84) and used genotyping by sequencing (GBS) to discover single nucleotide polymorphisms (SNPs) in commercial rice varieties. After integration of the cross-platform genotype data, including NGS, GBS, and the 44K SNPs data from rice diversity panel, we found that the overlapped SNPs between GBS and 44K data were less than 5%, while NGS data can be successfully combined with 44K SNPs data. In the association analysis, 57 regions were found significantly associated with resistance to the isolate D41-2, with 26 regions colocalized with known R genes and/or quantitative trait loci (QTLs), 17 regions containing 636 defense-related candidate genes, and 14 regions that did not match any known R genes/QTLs or defense-related candidate genes. Sixty regions were significantly associated with resistance to the 12YL-DL-3-2 isolate, with 35 regions colocalized with known R genes/QTLs, and 25 regions containing 472 defense-related candidate genes. The highest peak from the datasets of both isolates was located at the Pita locus on chromosome 12. Analysis of haplotypes revealed the loci conditioning resistance in each resistant accessions. The results will be useful for validation of identified resistance genes/QTLs and donor selection for future resistance breeding. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:28:50Z (GMT). No. of bitstreams: 1 ntu-104-R02633002-1.pdf: 22295212 bytes, checksum: d45ad9d2b9681b31973b71b0c9e9dffe (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員會審定書 #
中文摘要 II ABSTRACT IV CONTENTS VI LIST OF TABLES IX LIST OF FIGURES XI LIST OF SUPPLEMENYARY DATA XV Chapter 1 Research motivation 1 Chapter 2 Literature review 3 2.1 Rice 3 2.1.1 Introduction 3 2.1.2 Phylogeny and distribution 3 2.1.1 Rice domestication 4 2.1.2 Genome architecture 4 2.2 Rice blast 5 2.2.1 Pathogen and life cycle 5 2.2.2 Symptoms 5 2.2.3 Invasion mechanism 6 2.2.4 Disease management 6 2.2.5 Rice blast in Taiwan and the impact on rice production 7 2.3 Plant-host interaction 7 2.3.1 Vertical and horizontal resistance 7 2.3.2 R genes/QTLs against blast in rice 8 2.3.3 Avr genes in pathogen 9 2.4 Blast resistance breeding 9 2.5 QTL mapping 10 2.5.1 Linkage mapping 10 2.6 Genome-wide association studies (GWAS) 11 2.6.1 History 11 2.6.2 Materials for GWAS 13 2.6.3 Genotyping methods and marker density 13 2.6.4 Linkage disequilibrium (LD) 14 2.6.5 Analysis model 15 2.6.6 Validation 16 Chapter 3 Materials and methods 18 3.1 Phenotyping 18 3.1.1 Plant materials and growth condition 18 3.1.2 Inoculation 19 3.1.3 Disease evaluation 21 3.2 Genotyping 21 3.3 Association analysis 23 3.3.1 Input data 23 3.3.2 Association mapping 25 3.3.3 Analysis of significant signals 27 3.4 Haplotype analysis 27 Chapter 4 Results 30 4.1 Phenotyping 30 4.1.1 Inoculation of blast differential varieties 30 4.1.2 Reaction of susceptible control lines 30 4.1.3 Phenotype distribution 31 4.2 Genotyping 31 4.3 Association analysis 32 4.3.1 Optimal statistical models for different datasets 32 4.3.2 Threshold setting and significant regions 33 4.3.3 Candidate genes determination 34 4.3.4 Haplotype analysis and selection of resistant donor accessions 35 Chapter 5 Discussion 36 5.1 The possibility to combine 44K SNPs, NGS and GBS data for GWAS 36 5.2 Known blast R genes in diversity panel and commercial rice varieties 37 5.3 Defense-related genes in the identified regions 40 5.4 Future perspectives 41 REFERENCES 43 LIST OF TABLES Table 1. Rice diversity panel…………………………………………………………...49 Table 2. List of commercial varieties, susceptible control lines, and three sets of blast differential varieties…………………………………………………………………….54 Table 3. Tagged sequence report of genotyping by sequencing (GBS)………………...56 Table 4. List of different phenotyping and genotyping datasets for genome-wide association analyses in this study………………………………………………………59 Table 5. Reaction patterns of international standard differential varieties and Taiwan differential varieties to the Magnaporthe oryzae isolates D41-2 and 12YL-DL3-2…...62 Table 6. Reaction patterns of IRBLs to the Magnaporthe oryzae isolates D41-2 and 12YL-DL-3-2…………………………………………………………………………..63 Table 7. Integrated genotype data of the 21 selected rice varieties…..……………….. 64 Table 8. Results of genome-wide association mapping for rice resistance against the Magnaporthe oryza isolate D41-2……………………………………………………..65 Table 9. Results of genome-wide association mapping for rice resistance against the Magnaporthe oryzae isolate 12YL-DL-3-2……………………………………………86 Table 10. Genetic regions significantly associated with resistance to the Magnaporthe oryzae isolate D41-2…………………………………………………………………..119 Table 11. Genetic regions significantly associated with resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2………………………………………………………….121 Table 12. Candidate genes in the genetic regions significantly associated with resistance to the Magnaporthe oryzae isolate D41-2…………………………………………….123 Table 13. Candidate genes in the genetic regions significantly associated with resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2……………………………………156 Table 14. Results of haploview analysis and resistance haplotype determination. The results were based on evaluation with the Magnaporthe oryzae isolate D41-2………173 Table 15. Results of haploview analysis and resistance haplotype determination. The results were based on evaluation with the Magnaporthe oryzae isolate 12YL-DL-3-2.…………………………………………………………………………176 Table 16. Haplotypes of the blast-resistance-associated-regions in the resistant accessions. The results were based on evaluation with the Magnaporthe oryzae isolate D41-2………………………………………………………………………………….179 Table 17. Haplotypes of the blast-resistance-associated-regions in the resistant accessions. The results were based on evaluation with the Magnaporthe oryzae isolate 12YL-DL-3-2…………………………………………………………………………183 Table 18. Rice accessions with good potential for blast resitance breeding in Taiwan. The results were based on evaluation with the Magnaporthe oryzae isolate D41-2………………………………………………………………………………….185 Table 19. Rice accessions with good potential for blast resistance breeding in Taiwan. The results were based on evaluation with the Magnaporthe oryzae isolate 12YL-DL-3-2………………………………………………………………………….188 Table 20. Diseased leaves of candidate rice accessions on the 7th day after inoculation with Magnaporthe oryzae isolate D41-2…………..………………………………….191 Table 21. Diseased leaves of candidate rice accessions on the 7th day after inoculation with Magnaporthe oryzae isolate 12YL-DL-3-2…………..………………………….192 LIST OF FIGURES Figure 1. Diseased leaf area (DLA) of susceptible control (A),(C) Lomello; (B),(D) LTH by inoculated with two Magnaporthe oryzae isolates (A),(B) D41-2; (C), (D) 12YL-DL-3-2 ANOVA and Tukey’s HSD analysis were performed on the DLA data with repetition.……………………..………………………………………….………193 Figure 2. Diseased leaf area (DLA) of susceptible control (A)-(C) Loemllo; (D)-(F) LTH by inoculated with Magnaporthe oryzae isolates D41-2. ANOVA and Tukey’s HSD analysis were performed on the DLA data with blocks ………..…………….…194 Figure 3. Diseased leaf area (DLA) of susceptible control (A)-(C) Loemllo; (D)-(F) LTH by inoculated with Magnaporthe oryzae isolates 12YL-DL-3-2. ANOVA and Tukey’s HSD analysis were performed on the DLA data with blocks………………..195 Figure 4. Distribution of the diseased leaf area (DLA) data from blast resistance evaluation using the Magnaporthe oryzae isolate D41-2……………………………..196 Figure 5. Distribution of the BLUP adjusted diseased leaf area (BLUP adjusted DLA) data from blast resistance evaluation using the Magnaporthe oryza isolate D41-2. 198 Figure 6. Distribution of the lesion type (LT) data from blast resistance evaluation using the Magnaporthe oryzae isolate D41-2……………………………………………….200 Figure 7. Distribution of the diseased leaf area (DLA) data from blast resistance evaluation using the Magnaporthe oryzae isolate 12YL-DL-3-2…………………….201 Figure 8. Distribution of the BLUP adjusted diseased leaf area (BLUP adjusted DLA) data from blast resistance evaluation using the Magnaporthe oryzae isolate 12YL-DL-3-2…………………………………………………………………………203 Figure 9. Distribution of the lesion type (LT) data from blast resistance evaluation using the Magnaporthe oryzae isolate 12YL-DL-3-2……………………………………….205 Figure 10. The numbers of common single nucleotide polymorphisms (SNPs) resulted from the integration of genotypes from genotyping by sequencing (GBS), next-generation sequencing (NGS), and the 44K SNPs data…………………………206 Figure 11. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 1 in Table 4 by using different models ………………………….207 Figure 12. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 2 in Table 4 by using different models…………………………..209 Figure 13. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 3 in Table 4 by using different models…………………………..211 Figure 14. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 4 in Table 4 by using different models…………………..………213 Figure 15. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 5 in Table 4 by using different models…………………….…….215 Figure 16. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the subpopulation dataset no. 6 to no. 10 in Table 4…………………………….217 Figure 17. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 11 in Table 4 by using different models…………………………219 Figure 18. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 12 in Table 4 by using different models…………………………221 Figure 19. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 13 in Table 4 by using different models…………………………223 Figure 20. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 14 in Table 4 by using different models…………………………225 Figure 21. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 15 in Table 4 by using different models…………………………227 Figure 22. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 16 in Table 4 by using different models…………………………229 Figure 23. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 17 in Table 4 by using different models…………………………231 Figure 24. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 18 in Table 4 by using different models…………………………233 Figure 25. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 19 in Table 4 by using different models…………………………235 Figure 26. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the subpopulation dataset no. 20 to no. 27 in Table 4…………………………...237 Figure 27. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate D41-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 28 and no.29 in Table 4 by using different models……………...241 Figure 28. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 30 in Table 4 by using different models………….…..242 Figure 29. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the subpopulation dataset no. 31 to no. 35 in Table 4 by using naive models…………………………………………………………………………………244 Figure 30. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 36 in Table 4 by using different models……………...246 Figure 31. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the subpopulation dataset no. 37 to no. 41 in Table 4………………..248 Figure 32. Genome-wide association scan for blast resistance to the Magnaporthe oryzae isolate 12YL-DL-3-2. Manhattan plots and quantile-quantile (Q-Q) plots were generated from the dataset no. 42 in Table 4 by using different models……………...250 LIST OF SUPPLEMENYARY DATA Supplementary table 1. Rice blast code and diagram……………………………….. 252 Supplementary figure 1. Phenotyping process………………………………………. 253 | |
dc.language.iso | zh-TW | |
dc.title | 水稻稻熱病抗性基因全基因體關聯定位 | zh_TW |
dc.title | Genome-wide association mapping of blast resistance genes in rice diversity panel and Taiwan rice varieties | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 沈偉強(Wei-Chiang Shen),董致韡(Chih-Wei Tung),吳志文(Chih-Wen Wu) | |
dc.subject.keyword | 稻熱病,數量性狀基因座,全基因體關聯定位,Genotyping by sequencing (GBS), | zh_TW |
dc.subject.keyword | Rice blast,Quantitative trait locus (QTLs),Genome-wide association study (GWAS),Genotyping by sequencing (GBS), | en |
dc.relation.page | 254 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-03 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 植物病理與微生物學研究所 | zh_TW |
顯示於系所單位: | 植物病理與微生物學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-104-1.pdf 目前未授權公開取用 | 21.77 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。