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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 廖振鐸 | |
dc.contributor.author | Jen-Hsiang Ou | en |
dc.contributor.author | 歐任翔 | zh_TW |
dc.date.accessioned | 2021-06-08T04:03:29Z | - |
dc.date.copyright | 2018-08-03 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-02 | |
dc.identifier.citation | Akdemir, D., Sanchez, J. I., and Jannink, J. L. (2015). Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution, 47(1):38.
Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with r package rrblup. The Plant Genome, 4(3):250–255. Heffner, E. L., Sorrells, M. E., and Jannink, J. L. (2009). Genomic selection for crop improvement. Crop Science, 49(1):1–12. Isidro, J., Jannink, J. L., Akdemir, D., Poland, J., Heslot, N., and Sorrells, M. E. (2015). Training set optimization under population structure in genomic selection. Theoretical and Applied Genetics, 128(1):145–158. Laloë, D. (1993). Precision and information in linear models of genetic evaluation. Genetics Selection Evolution, 25(6):557. Lorenz, A. J., Smith, K. P., and Jannink, J. L. (2012). Potential and optimization of genomic selection for fusarium head blight resistance in six-row barley. Crop Science, 52(4):1609–1621. Meuwissen, T. H. E., Hayes, B. J., and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4):1819–1829. Rincent, R., Charcosset, A., and Moreau, L. (2017). Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theoretical and Applied Genetics, 130(11):2231–2247. Rincent, R., Laloë, D., Nicolas, S., Altmann, T., Brunel, D., Revilla, P., Rodriguez, V. M., Moreno-Gonzalez, J., Melchinger, A., Bauer, E., and Schön, C. C. (2012). Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics, 192(2):715–728. Searle, S. R., Casella, G., and McCulloch, C. E. (1982). Matrix algebra usful for statistics. Wiley. Shariati, M. M., Sørensen, P., and Janss, L. (2012). A two step bayesian approach for genomic prediction of breeding values. BMC Proceedings, 6(2):S12. VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11):4414–4423. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2):65–85. Xavier, A., Muir,W. M., Craig, B., and Rainey, K. M. (2016). Walking through the statistical black boxes of plant breeding. Theoretical and Applied Genetics, 129(10):1933–1949. Zhao, K., Tung, C. W., Elizenga, G. C., Wright, M. H., Ali, M. L., Price, A. H., Norton, G. J., Islam, M. R., Reynolds, A., Mezey, J., and McClung, A. M. (2011). Genomewide association mapping reveals a rich genetic architechture of complex traits in Oryza sativa. Nature communications, 2:467. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22118 | - |
dc.description.abstract | 在面對一群已知基因型而尚未做外表型調查的候選族群時,我們提出了一個有效的演算法以幫助我們由候選族群中選擇最佳的次族群作為訓練族群(training set),這些被選中的訓練族群會被調查外表型資料並以其基因型和表現型資料建立全基因組選拔(genomic selection, GS) 模型。在本篇研究中,我們考慮全基因組迴歸模式(whole-genome regression model),並以脊迴歸(ridge regression) 來估計GS 模型中分子標記的效應,所配適的GS 模型在育種中會接著被用於計算只有基因型資料之測試族群的育種價估計值(genomic estimated breeding values, GEBV),我們提出一個新的判斷準則用於決定所需的訓練族群,這個準則是由GEBV 與真實外表型值的皮爾生相關係數(Pearson’s correlation coefficient) 所發展而來,在本篇研究中我們使用R 語言來分析一組水稻的資料,由結果顯示,使用我們提出的演算法所選擇的訓練族群相較於隨機選擇訓練族群能夠使所配適的模型具有更高的預測準確性。 | zh_TW |
dc.description.abstract | For a given candidate set of individuals which have been genotyped but not phenotyped, we develop a highly efficient algorithm to determine an optimal subset from the candidate set. The chosen subset serves as a training set to be phenotyped, and then a genomic selection (GS) model is built based on its resulting phenotype and genotype data. In this study, we typically consider
the whole-genome regression model, and adopt ridge regression estimation for marker effects in the GS model. The resulting GS model is then employed to predict genomic estimated breeding values (GEBVs) for a given test set of individuals which have been genotyped only. We propose a new optimality criterion to determine the required training set, which is directly derived from Pearson’s correlation between the GEBVs and phenotypic values of the test set. Pearson’s correlation is the standard measure for prediction accuracy of a GS model. We implement our training set determination algorithm in R language, and illustrate it with a rice genome data set. It is shown that the training set generated from our algorithm can usually achieve a significantly improved prediction accuracy in comparison with a randomly selected training set. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T04:03:29Z (GMT). No. of bitstreams: 1 ntu-107-R05621203-1.pdf: 6887115 bytes, checksum: 861ef36dbd200982608191fa994062c1 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 致謝 v
中文摘要 vii Abstract ix 1 Introduction 1 2 Methods 5 3 Results 9 3.1 Principal component analysis 10 3.2 The choice of l 10 3.3 Accuracy Improvement 11 3.4 Population structure 13 3.5 R functions 13 4 Discussion 19 Appendix 21 Appendix A. The derivation of (2.2) 21 Appendix B. The derivation of r-score 22 Appendix C. The abbreviation of traits 24 Appendix D. Source code 25 Bibliography 29 | |
dc.language.iso | en | |
dc.title | 全基因組選拔訓練族群之決定 | zh_TW |
dc.title | Training set determination for genomic selection | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 高振宏,蔡欣甫,董致韡 | |
dc.subject.keyword | 基因組育種值,基因組預測,植物育種,預測準確性,單一核?酸多型性分子標記, | zh_TW |
dc.subject.keyword | GEBV,genomic prediction,plant breeding,prediction accuracy,SNP marker, | en |
dc.relation.page | 30 | |
dc.identifier.doi | 10.6342/NTU201802290 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2018-08-02 | |
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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