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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖振鐸(Chen-Tuo Liao) | |
| dc.contributor.author | Po-Chun Liao | en |
| dc.contributor.author | 廖柏鈞 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:15:44Z | - |
| dc.date.available | 2021-08-13 | |
| dc.date.available | 2022-11-23T09:15:44Z | - |
| dc.date.copyright | 2021-08-13 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-11 | |
| dc.identifier.citation | [1] T. H. Meuwissen, B. J. Hayes, and M. E. Goddard. Prediction of total genetic value using genome wide dense marker maps.GENETICS, 157(4):1819–1829, 2001. [2] S. Maenhout, B. De Baets, and G. Haesaert. Graphbased data selection for the construction of genomic prediction models.Genetics, 185(4):146375, 2010. [3] E. L. Heffner, M. E. Sorrells, and J. L. Jannink. Genomic selection for crop improvement, CropScience, 49(1):1–12, 2009. [4] A. J. Lorenz, K. Smith, and J. L. Jannink. Potential and optimization of genomic selection for fusarium head blight resistance in sixrow barley, Crop Science,52(4):1609–1621, 2012. [5] V. Wimmer, C. Lehermeier, T. Albrecht, H. J. Auinger, Y. Wang, and C.C. Schön. Genome wide prediction of traits with different genetic architecture through efficient variable selection.GENETICS, 195(2):573–587, 2013. [6] R. Rincent, D. Laloë, S. Nicolas, T. Altmann, D. Brunel, P. Revilla, V.M. Rodríguez, J. MorenoGonzalez, A. Melchinger, E. Bauer, CC. Schoen, N. Meyer, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, H. Monod, P. Flament, A. Charcosset, and L. Moreau. 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):715–728,2012 [7] D.Akdemir, J. I. Sanchez, and J. L. Jannink. Optimization of genomic selection training populations with a genetic algorithm.Genetics Selection Evolution, 47(1):38,2015. [8] A. Xavier, W. M. Muir, B. Craig, and K. M. Rainey. Walking through the statistical black boxes of plant breeding.Theoretical and Applied Genetics, 129(10):1933–1949, 2016. [9] M. M. Shariati, P. Sørensen, and L. Janss. A two step bayesian approach for genomic prediction of breeding values.BMC Proceedings, 6(2):S12, 2012. [10] C. R. Henderson. Estimation of genetic parameters.Annals of Mathematical Statistics, 21(3):309–310, 1950. [11] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm.Journal of the Royal Statistical Society.Series B(Methodology), 39(1):1–38, 1977. [12] S. German and D. German. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6):721–741, 1984. [13] S. R. Searle and A. I. Khuri. Matrix Algebra Useful for Statistics, chapter 10 and chapter 13,pages 261,355. Wiley, 1982. [14] J. H. Holland. Genetic algorithms and adaptation, NATO Conference Series, 16(1):317–333, 1975. [15] D. Whitley. A genetic algorithm tutorial, Statistics and Computing, 4(2):65–85,1994. [16] D. Akdemir and J. I. Sánchez. Design of training populations for selective phenotyping in genomic prediction, Scientific Reports, 9(1):1446, 2019. [17] R. Rincent, A. Charcosset, and L. Moreau. 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, 2017 [18] J. Isidro, J. L. Jannink, D. Akdemir, J. Poland, N. Heslot, and M. E. Sorrells. Training set optimization under population structure in genomic selection, Theoretical and Applied Genetics, 128(1):145–158, 2015. [19] K. Zhao, C. W. Tung, G. C. Eizenga, M. H. Wright, M. L. Ali, A. H. Price, G. J.Norton, M. R. Islam, A. Reynolds, J. Mezey, A. M. McClung, C. D. Bustamante,and S. R. McCouch. Genomewide association mapping reveals a rich genetic architecture of complex traits in oryza sativa, Nature Communications, 2(467), 2011 [20] J. H. Ou and C. T. Liao. Training set determination for genomic selection, Theoretical and Applied Genetics, 132(10):2781–2792, 2019 [21] P. Perez and G. Campos. Genome wide regression and prediction with the bglr statistical package, Genetics, 198(2):483–495, 2014 [22] G. Morota, P. Boddhireddy, N. Vukasinovic, D. Gianola, and S. DeNise. Kernelbased variance component estimation and wholegenome prediction of precorrected phenotypes and progeny tests for dairy cow health traits. Front Genet, 5(56):10–3389, 2014 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79896 | - |
| dc.description.abstract | "雖然次世代定序 (Next Generation Sequencing) 技術目前可協助降低基因型獲得 (genotyping) 的成本，但表現型獲得 (phenotyping) 於育種領域的執行成本上仍是一大考驗。因此，基因體選拔 (genomic selection) 可藉由篩選出使預測準確率最大化的特定訓練集 (training set) 資料來降低該訓練集於表現型獲得所需的成本並建立預測模型。在基因體選拔的 過程中，較佳的訓練集能協助我們建立預測測試集數量性狀較為精準的模型。而從候選集 (candidate set) 篩選對應每組測試集 (testing set) 的最佳訓練集過 程中，本論文各採用以r-score和mspe-score作為目標函數 (objective function) 的基因演算法 (genomic algorithms, GA) 來求之。透過基因演算法選出的訓練集在表現型獲得後，將可用來估計測試集個體的育種價 (genomic estimated breeding values, GEBVs)。基因演算法中採用的目標函數r-score和mspe-score可分別由測試集的表現型值與育種價間的皮爾森相關係數 (Pearson's correlation) 與均方預測誤差 (mean squared prediction error) 推導而得。此外，本論文以Tropical rice和 rice44k兩組資料作為範例，並採用一般常見的皮爾森相關係數及均方根誤差來評估預測模型的準確度；其中，由於rice44k資料的水稻個體共含六種次族群 (subpopulations) 結構，在建模過程除了比較測試集已知及未知外，還需考量次族群的影響。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:15:44Z (GMT). No. of bitstreams: 1 U0001-3007202115234900.pdf: 2132220 bytes, checksum: 1d7f74aa3d1858dea54c8bdd1d510552 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Acknowledgement(ii) 摘要(iii) Abstract(iv) Contents(v) List of figures(vi) List of tables(ix) Chapter 1 Introduction(1) 1.1 Whole genome regression(1) 1.2 Linear mixed effects model(2) Chapter 2 Methods(6) 2.1 r-score(7) 2.2 mspe-score(9) 2.3 The choice of lambda(10) 2.4 Genetic algorithm(13) Chapter 3 Results(16) 3.1 Real data analysis(18) 3.1.1 Tropical rice data(18) 3.1.2 Rice44K data(21) 3.2 Simulation study(26) 3.2.1 Tropical rice data(27) 3.2.2 Rice44K data(31) Chapter 4 Discussion(41) Chapter 5 Bibliography(45) Apendex A - Source code(49) A.1 Genetic algorithm(49) A.2 Simple exchange algorithm(62) A.3 r-score and mspe-score(71) | |
| dc.language.iso | en | |
| dc.subject | 限制最大概似估值 | zh_TW |
| dc.subject | 基因體選拔 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 全基因組迴歸模式 | zh_TW |
| dc.subject | 線性混合模型 | zh_TW |
| dc.subject | genetic algorithm | en |
| dc.subject | restricted maximum likelihood estimate | en |
| dc.subject | linear mixed effects model | en |
| dc.subject | whole genome regression | en |
| dc.subject | genomic selection | en |
| dc.title | 基因體選拔中兩種訓練集最佳化準則之比較 | zh_TW |
| dc.title | A Comparison of two criteria for training set optimization in genomic selection | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 高振宏(Hsin-Tsai Liu),蔡欣甫(Chih-Yang Tseng) | |
| dc.subject.keyword | 基因體選拔,基因演算法,全基因組迴歸模式,線性混合模型,限制最大概似估值, | zh_TW |
| dc.subject.keyword | genomic selection,genetic algorithm,whole genome regression,linear mixed effects model,restricted maximum likelihood estimate, | en |
| dc.relation.page | 71 | |
| dc.identifier.doi | 10.6342/NTU202101936 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-11 | |
| 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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