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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97984Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 廖振鐸 | zh_TW |
| dc.contributor.advisor | Chen-Tuo Liao | en |
| dc.contributor.author | 劉子捷 | zh_TW |
| dc.contributor.author | Zi-Jie Liu | en |
| dc.date.accessioned | 2025-07-23T16:20:52Z | - |
| dc.date.available | 2025-07-24 | - |
| dc.date.copyright | 2025-07-23 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-03 | - |
| dc.identifier.citation | Akdemir, D., Sanchez, J. I., & Jannink, J.-L. (2015). Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution, 47, 1-10.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97984 | - |
| dc.description.abstract | 基因型與環境交互作用 (Genotype-by-environment Interaction, G×E) 是植物育種中的常見現象，此因子會影響多環境試驗 (Multi-environment Trials, METs) 中的選拔準確性。將基因體最佳線性無偏預測模型 (Genomic Best Linear Unbiased Prediction Model, GBLUP Model) 納入G×E效應並進行基因體選種 (Genomic Selection, GS) ，能在多環境試驗之中提升預測準確性。本研究使用兩種決定係數 (Coefficient of Determination, CD) 指標應用於訓練集最佳化中的方法，並針對識別優良品種的能力，將其與隨機抽取訓練集之方法進行比較。本研究使用基因演算法 (Genetic Algorithm, GA) ，從三個作物資料集: 水稻 (Oryza sativa L.)、大麥 (Hordeum vulgare L.) 和玉米 (Zea mays L.) 中選出最佳訓練集。以三項評估指標: 標準化折扣累積增益 (Normalized Discounted Cumulative Gain, NDCG) 、Spearman等級相關係數 (Spearman’s Rank Correlation, SRC)、以及名次總和比率 (Rank Sum Ratio, RSratio) 評估上述三種方法的表現。結果顯示，基於 CD 指標選出的訓練集在三項評估指標上都表現得較好。將兩個CD指標的表現進行比較，結果顯示 CDmean(v2) 於SRC與RSratio兩項指標皆優於CDmean.MET，尤其是在使用較大的訓練集規模時。因此，本研究建議使用 CDmean(v2) 在多環境試驗中將訓練集最佳化。 | zh_TW |
| dc.description.abstract | Genotype-by-environment interaction (G×E) is a key factor in plant breeding, impacting multi-environment trials (METs) for accurate selection. Genomic selection (GS) can improve prediction accuracy across environments, especially with Genomic best linear unbiased prediction (GBLUP) models that account for G×E effects. This study evaluates training set optimization using two coefficient of determination (CD) criteria and compares them to random selection based on the ability to identify elite varieties. A genetic algorithm identified optimal training sets from three datasets of rice (Oryza sativa L.), barley (Hordeum vulgare L.), and maize (Zea mays L.), and their performance was assessed using normalized discounted cumulative gain (NDCG), Spearman’s rank correlation (SRC), and rank sum ratio (RSratio). CD-based training sets showed better performance among these evaluation metrics. The performance of the two CD criteria were compared. CDmean(v2) outperformed CDmean.MET in SRC and RSratio especially in larger training set sizes. Therefore, CDmean(v2) was highly recommended to select training sets in multi-environment trials. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-23T16:20:52Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-23T16:20:52Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書
Acknowledgement i 摘要 ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Materials 5 2.1 Tropical rice dataset 5 2.2 Barley dataset 5 2.3 DST2 maize dataset 6 Chapter 3 Methods 7 3.1 A multi-environment GS model 7 3.2 Coefficient of determination 11 3.3 Genetic algorithm 13 3.4 Evaluation metrics 14 3.4.1 Normalized discounted cumulative gain 15 3.4.2 Spearman’s rank correlation 16 3.4.3 Rank sum ratio 16 3.5 Simulation studies 17 3.6 Real data analyses 19 Chapter 4 Results 21 4.1 Simulation studies 21 4.1.1 Normalized discounted cumulative gain 22 4.1.2 Spearman’s rank correlation 23 4.1.3 Rank sum ratio 24 4.2 Real data analyses 26 4.2.1 Normalized discounted cumulative gain 26 4.2.2 Spearman’s rank correlation 27 4.2.3 Rank sum ratio 27 Chapter 5 Discussion 39 5.1 The performance of the three evaluation metrics 39 5.2 Training sets determined by CD criteria have high r2 40 5.3 Robustness of CD criteria against parameters 40 5.4 Correlation of genetic effects between environments 41 References 49 Appendix A – Var(g ̂_c) and Cov(g_c,g ̂_c) are equivalent mathematically 52 Appendix B – Supplementary Materials 54 | - |
| dc.language.iso | en | - |
| dc.subject | 決定係數 | zh_TW |
| dc.subject | 訓練集最佳化 | zh_TW |
| dc.subject | 基因體選種 | zh_TW |
| dc.subject | 基因型與環境交互作用 | zh_TW |
| dc.subject | 多環境試驗 | zh_TW |
| dc.subject | 基因體最佳線性無偏預測模型 | zh_TW |
| dc.subject | Genomic Best Linear Unbiased Prediction Models | en |
| dc.subject | Coefficient of Determination | en |
| dc.subject | Training Set Optimization | en |
| dc.subject | Genomic Selection | en |
| dc.subject | G×E Interaction | en |
| dc.subject | Multi-environment Trials | en |
| dc.title | 於多環境試驗中進行基因體選種之訓練集最佳化 | zh_TW |
| dc.title | Training Set Optimization in Genomic Selection for Multi-environment Trials | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡欣甫;高振宏 | zh_TW |
| dc.contributor.oralexamcommittee | Shin-Fu Tsai;Chen-Hung Kao | en |
| dc.subject.keyword | 訓練集最佳化,基因體選種,基因型與環境交互作用,多環境試驗,基因體最佳線性無偏預測模型,決定係數, | zh_TW |
| dc.subject.keyword | Training Set Optimization,Genomic Selection,G×E Interaction,Multi-environment Trials,Genomic Best Linear Unbiased Prediction Models,Coefficient of Determination, | en |
| dc.relation.page | 54 | - |
| dc.identifier.doi | 10.6342/NTU202501399 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-03 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 農藝學系 | - |
| dc.date.embargo-lift | 2030-06-30 | - |
| Appears in Collections: | 農藝學系 | |
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| ntu-113-2.pdf Restricted Access | 3.74 MB | Adobe PDF | View/Open |
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