請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73218
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 廖振鐸 | |
dc.contributor.author | Chih-Chien Shen | en |
dc.contributor.author | 沈之謙 | zh_TW |
dc.date.accessioned | 2021-06-17T07:23:02Z | - |
dc.date.available | 2019-07-10 | |
dc.date.copyright | 2019-07-10 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-07-02 | |
dc.identifier.citation | Cobb, J.N., DeClerck, G., Greenberg, A., Clark, R., McCouch, S., 2013. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics 126, 867–887.
Crossa, J., Campos, G. de los, Pérez, P., Gianola, D., Burgueño, J., Araus, J.L., Makumbi, D., Singh, R.P., Dreisigacker, S., Yan, J., Arief, V., Banziger, M., Braun, H.-J., 2010. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186, 713–724. https://doi.org/10.1534/genetics.110.118521 de los Campos, G., Hickey, J.M., Pong-Wong, R., Daetwyler, H.D., Calus, M.P.L., 2013. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345. https://doi.org/10.1534/genetics.112.143313 Hyten, D.L., Song, Q., Zhu, Y., Choi, I.-Y., Nelson, R.L., Costa, J.M., Specht, J.E., Shoemaker, R.C., Cregan, P.B., 2006. Impacts of genetic bottlenecks on soybean genome diversity. Proceedings of the National Academy of Sciences 103, 16666–16671. https://doi.org/10.1073/pnas.0604379103 Jones, D.R., Schonlau, M., Welch, W.J., 1998. Efficient global optimization of expensive black-box functions. Journal of Global Optimization 13, 455–492. https://doi.org/10.1023/A:1008306431147 Khoury, C.K., Bjorkman, A.D., Dempewolf, H., Ramirez-Villegas, J., Guarino, L., Jarvis, A., Rieseberg, L.H., Struik, P.C., 2014. Increasing homogeneity in global food supplies and the implications for food security. Proceedings of the National Academy of Sciences 111, 4001–4006. https://doi.org/10.1073/pnas.1313490111 Kulis, B., Jordan, M.I., 2011. Revisiting k-means: New algorithms via Bayesian nonparametrics. arXiv preprint arXiv:1111.0352. Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819. Mockus, J., 1994. Application of Bayesian approach to numerical methods of global and stochastic optimization. Journal of Global Optimization 4.4 (1994): 347-365. https://doi.org/10.1007/BF01099263 Pérez, P., de los Campos, G., 2014. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483. https://doi.org/10.1534/genetics.114.164442 Pérez-Rodríguez, P., Gianola, D., González-Camacho, J.M., Crossa, J., Manès, Y., Dreisigacker, S., 2012. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes|Genomes|Genetics 2, 1595. https://doi.org/10.1534/g3.112.003665 Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., De Bakker, P.I., Daly, M.J., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559–575. Reif, J.C., Zhang, P., Dreisigacker, S., Warburton, M.L., van Ginkel, M., Hoisington, D., Bohn, M., Melchinger, A.E., 2005. Wheat genetic diversity trends during domestication and breeding. Theoretical and Applied Genetics 110, 859–864. Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N., 2016. Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE 104, 148–175. Tanaka, R., Iwata, H., 2018. Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates. Theoretical and Applied Genetics 131, 93–105. Tanksley, S.D., McCouch, S.R., 1997. Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277, 1063–1066. Tester, M., Langridge, P., 2010. Breeding technologies to increase crop production in a changing world. Science 327, 818–822. Wu, P.-Y., Tung, C.-W., Lee, C.-Y., Liao, C.-T., 2019. Genomic prediction of pumpkin hybrid performance. The Plant Genome 0, 0. https://doi.org/10.3835/plantgenome2018.10.0082 Xavier, A., Muir, W.M., Craig, B., Rainey, K.M., 2016. Walking through the statistical black boxes of plant breeding. Theoretical and Applied Genetics 129, 1933–1949. Zhao, K., Tung, C.-W., Eizenga, G.C., Wright, M.H., Ali, M.L., Price, A.H., Norton, G.J., Islam, M.R., Reynolds, A., Mezey, J., 2011. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2, 467. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73218 | - |
dc.description.abstract | 從大量種原庫中選拔出優良種原是作物育種計劃中非常重要的一環,而基因組預測(genomic prediction)在此是一項非常有效的工具。本研究提出了一種迭代策略,使用基因組預測的方式從大量候選種原庫中選拔最佳基因型,而候選種原的個體在未知外表型的情況下進行基因組預測。首先從候選種原中隨機抽樣少部分的外表型測量(phenotyped),且使用數據建立基因組BLUP(GBLUP)預測模型,採用預期增進(expected improvement, EI)準則來判斷是否已選拔出最佳基因型,如果在當前步驟未達到目標,則從剩餘子集合中的未知外表型個體來選拔且進行外表型調查,然後將外表型數據與當前訓練數據一起加入且更新GBLUP預測模型,重複該過程直至選拔出最佳基因型。我們透過真實數據集和模擬數據集,比較基於育種價(breeding value)或基因值(genomic values)分佈的EI準則下的迭代策略結果,我們發現相較於育種價,基於基因值的EI準則只需要較少的基因型(訓練集大小)即可找到最佳基因型。本研究使用了水稻,玉米,小麥和南瓜的五個真實基因組數據集。 | zh_TW |
dc.description.abstract | Genomic prediction has been a powerful tool to select superior accessions from a large germplasm collection in a plant breeding program. An iterative strategy using genomic prediction to discover the best genotype from a large candidate population is proposed in this study. The individuals of the candidate population have been genotyped only without being phenotyped yet. A genomic BLUP (GBLUP) prediction model is first built using the phenotype data for some individuals randomly sampled from the candidate population. The expected improvement (EI) criterion is employed to determine whether the best genotype is discovered. If the goal is not achieved at the current step, then a suitable subset from the remaining non-phenotyped individuals is chosen and phenotyped. The phenotype data are then added with the current training data to update the GBLUP prediction model. The procedure is repeated until the best genotype is discovered. The EI criterion based on the distribution of breeding values or genomic values is investigated for the iterative strategy, through several real datasets and simulated datasets. Our proposed EI criterion based on the genomic values is shown to frequently have less genotypes required (the training set size) to find the best genotype than that based on the breeding values. Five real genome datasets of rice, maize, wheat and pumpkin are used in this study. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:23:02Z (GMT). No. of bitstreams: 1 ntu-108-R06621205-1.pdf: 1250608 bytes, checksum: 6333c8ed682d5fbebdca70bdd935a6ff (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口委審定書 i
致謝 i 摘要 ii Abstract iii Introduction 1 Methods 4 Distributions of Breeding Values and Genomic Values 4 Expected Improvement Criteria 6 Iterative Strategy 8 Criteria Comparison Based on Real Datasets 9 Rice dataset 10 Maize dataset 10 CIMMYT wheat dataset 10 Perez’s wheat dataset 10 Pumpkin dataset 11 Criteria Comparison Based on Simulated Datasets 11 Results 13 Criteria Comparison Based on Real Datasets 13 Criteria Comparison Based on Simulated Datasets 19 Discussion 21 Reference 30 | |
dc.language.iso | en | |
dc.title | 從候選族群中選拔最佳基因型 | zh_TW |
dc.title | Identification of the best genotype from a large candidate set | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蔡欣甫 | |
dc.contributor.oralexamcommittee | 高振宏 | |
dc.subject.keyword | 貝氏優化,預期增進,基因組預測,基因組選種,作物育種, | zh_TW |
dc.subject.keyword | Bayesian optimization,Expected improvement,Genomic prediction,Genomic selection,Plant breeding, | en |
dc.relation.page | 37 | |
dc.identifier.doi | 10.6342/NTU201901172 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-07-03 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 1.22 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。