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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 廖振鐸(Chen-Tuo Liao) | |
dc.contributor.author | Zhen-Yu Tu | en |
dc.contributor.author | 杜鎮宇 | zh_TW |
dc.date.accessioned | 2021-05-20T00:51:45Z | - |
dc.date.available | 2020-08-21 | |
dc.date.available | 2021-05-20T00:51:45Z | - |
dc.date.copyright | 2020-08-21 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-06 | |
dc.identifier.citation | Blondel, M., A. Onogi, H. Iwata, and N. Ueda, (2015). A Ranking Approach to Genomic Selection. PLOS One 10, e0128570. Covarrubias-Pazaran, G. (2016). Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer. PLOS One 11, e0156744. Crossa, J., G. de los. Campos, P. Pérez, D. Gianola, J. Burgueño, et al. (2010). Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers. Genetics 186, 713–724. Gaynor, R.C., G. Gorjanc, A.R. Bentley, E.S. Ober, P. Howell, et al. (2017). A two-part strategy for using genomic selection to develop inbred lines. Crop Sci. 57: 2372–2386. Gong, C., J. Peng, and Q. Liu, (2019). Quantile Stein variational gradient descent for batch Bayesian optimization. Proceedings of the 36th International Conference on Machine Learning, PMLR 97, 2347-2356. Guo, G., F. Zhao, Y. Wang, Y. Zhang, L. Du, et al. (2014). Comparison of single-trait and multiple-trait genomic prediction models. BMC Genetics 15, 30. Hayashi, T. and H. Iwata, (2013). A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics 14, 34. Hyten, D. L., Q. Song, Y. Zhu, I.Y. Choi, R.L. Nelson, et al. (2006). Impacts of genetic bottlenecks on soybean genome diversity. Proceedings of the National Academy of Sciences 103, 16666–16671. Jia, Y. and J.L. Jannink, (2012). Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy. Genetics 192, 1513–1522. Khoury, C. K., A.D. Bjorkman, H. Dempewolf, J. Ramirez-Villegas, L. Guarino, et al. (2014). Increasing homogeneity in global food supplies and the implications for food security. Proceedings of the National Academy of Sciences 111, 4001–4006. McCouch, S., G.J. Baute, J. Bradeen, P. Bramel, P.K. Bretting, et al. (2013). Feeding the future. Nature 499, 23–24. Meuwissen, T.H.E., B.J. Hayes, and M.E. Goddard, (2001). Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps. Genetics 157, 1819. Ou, J.-H. and C.T. Liao, (2019). Training set determination for genomic selection. Theor. Appl. Genet. 132, 2781–2792. Reif, J.C., P. Zhang, S. Dreisigacker, M.L. Warburton, M. van Ginkel, et al. (2005). Wheat genetic diversity trends during domestication and breeding. Theor. Appl. Genet. 110, 859–864. Schulthess, A.W., Y. Wang, T. Miedaner, P. Wilde, J.C. Reif, et al. (2016). Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor. Appl. Genet. 129, 273–287. Searle, S.R. (1982). Matrix algebra useful for statistics. New York: JOHN WILEY SONS. Shahriari, B., K. Swersky, Z. Wang, R.P. Adams, N. de Freitas, (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104, 148–175. Shen. (2019). Identification of the best genotype from a large candidate set. Degree Thesis of National Taiwan University. Spindel, J., H. Begum, D. Akdemir, P. Virk, B. Collard, et al. (2015). Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines. PLOS Genetics 11, e1004982. Tanaka, R. and H. Iwata, (2018). Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates. Theor. Appl. Genet. 131, 93–105. Tanksley, S.D. and S.R. McCouch, (1997). Seed Banks and Molecular Maps: Unlocking Genetic Potential from the Wild. Science 277, 1063–1066. Tester, M. and P. Langridge, (2010). Breeding Technologies to Increase Crop Production in a Changing World. Science 327, 818–822. VanRaden, P.M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science 91, 4414–4423. Zhang, A., H. Wang, Y. Beyene, K. Semagn, Y. Liu, et al. (2017). Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations. Front. Plant Sci. 8, 1916. Zhao, K., C.W. Tung, G.C. Eizenga, M.H. Wright, M.L. Ali, et al. (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/123456789/8306 | - |
dc.description.abstract | 使用多批次外表型收集策略來從一組候選族群中找到一群優良基因型群體,以達到能節省外表型資料的蒐集,進而找到優良基因型群體。在本研究中我們假設候選族群已擁有基因型資料,並分多次選取部分個體收集其外表型,而後使用具有外表型資料的個體建立GBLUP多性狀模型、並估計候選族群個體的基因型值、對於不同性狀給予不同權重後相加成一個選拔指標,並進行排序。其中用於選取訓練族群個體的方法有r-score、M-PGV、EI-PGV以及EI-PGV-fwd,而所有方法的第一組起始個體選取皆使用r-score的方法,因為r-score只需要使用基因型的資訊而不需要考慮外表型的資訊。多性狀模型的應用讓我們同時針對多個性狀進行估計、然後根據不同性狀的重要性進行加權總合、最終得到的值稱作composite selection index (CSI)。針對排序後的CSI則使用correctly identified proportion (CIP)以及normalized discounted cumulative gain (NDCG) 作為評估指標,這兩項指標可以對感興趣的前幾名個體進行評量,且NDCG還多考慮了排序的正確性。經由上述的流程,最終能夠輔助我們選拔出個體來進行外表型資料蒐集、使得模型有良好的估計與排序,進而有效率的找到優良的基因型群體。 | zh_TW |
dc.description.abstract | A sequential phenotyping strategy is proposed to detect a set of superior genotypes efficiently from a candidate population. In this study, we assume that all of the individuals in the candidate population have been already genotyped. The iterative searching process is composed of the following steps. Step 0: a starting training set is determined from the candidate population according to the r-score algorithm. Step 1: a multiple-trait GBLUP model is trained using the phenotype and genotype data of the current training set. Step 2: a composite selection index (CSI) is constructed and estimated for each individual in the candidate population with genotypes based on the resulting multiple-trait GBLUP model. Step 3: two assessment indices, correctly identified proportion (CIP) and normalized discounted cumulative gain (NDCG) are calculated based on the estimates of CSI for a set of candidate individuals, and are used to evaluate the accuracy for the detection of the superior individuals. Step 4: four acquisition functions, r-score, M-PGV, EI-PGV and EI-PGV-fwd, are used to select additional training set added with the current training set. We further provide a stopping rule for the sequential strategy for practical applications. Three genome datasets are analyzed to illustrate our proposed sequential phenotyping strategy. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:51:45Z (GMT). No. of bitstreams: 1 U0001-0608202015295500.pdf: 2998121 bytes, checksum: 78758675aeaca35911649cacc2629ac9 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 摘要 i Abstract ii Introduction 1 Materials and Methods 5 44k Rice Dataset 5 Tropical Rice Breeding Lines Dataset 5 Wheat Dataset 6 Standardized Multiple-trait GBLUP Model 6 Composite Selection Index 7 The Distribution of Predicted Genotypic Values 8 The Expected Improvement Criteria for the CSI 9 r-score method 11 The Assessment Indices 12 Iterative Strategy 14 Criteria Comparison Based on Real Datasets 15 The Stopping Rule for the Iterative Strategy 16 Results 17 Criteria Comparison Based on Assessment Indices 17 The Stopping Rule for the Iterative Strategy 18 The True Genotypic Values for Specific Batches 19 Discussion 21 Reference 24 Tables and Figures 28 | |
dc.language.iso | en | |
dc.title | 透過多批次外表型收集策略選拔優良基因型 | zh_TW |
dc.title | A Sequential Batch Phenotyping Strategy for Detecting Superior Genotypes | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蔡欣甫(Shin-Fu Tsai) | |
dc.contributor.oralexamcommittee | 高振宏(Chen-Hung Kao),董致韡(Chih-Wei Tung) | |
dc.subject.keyword | 多批次外表型收集策略,多性狀,選拔指標,r-score,GBLUP, | zh_TW |
dc.subject.keyword | Sequential phenotyping strategy,Multiple traits,Composite selection index,r-score,GBLUP, | en |
dc.relation.page | 58 | |
dc.identifier.doi | 10.6342/NTU202002551 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2020-08-07 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
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