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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖振鐸(Chen-Tuo Liao) | |
| dc.contributor.author | Pei-Hsien Wang | en |
| dc.contributor.author | 王珮仙 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:42:36Z | - |
| dc.date.copyright | 2022-09-30 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-01 | |
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Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture. Theor. Appl. Genet., 131(2), 299–317. Zhao, Y., Zeng, J., Fernando, R., Reif, J.C. (2013). Genomic prediction of hybrid wheat performance. Crop Sci. 53, 802. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86212 | - |
| dc.description.abstract | 基因體預測 (Genomic prediction) 可有效降低育種成本及縮短所需時間，因此在作物育種中，已成為一項評估子代雜交表現強而有力的工具。本次研究中共使用兩筆作物資料，分別為具有142個品系的C. Maxima南瓜資料和24個品系的玉米資料。本研究提出一個同時考慮加性及顯性效應的混合線性模型，以預測雜交後代組合的表現。我們先使用有限制最大概似(restricted maximum likelihood, REML)估計法，來估計出加性效應和顯性效應的變方成份 (variance components)，再利用Henderdon’s 方程式獲得做為訓練集資料之部分雜交後代個體的加性效應和顯性效應，最後結合基因體關聯性矩陣(genomic relationship matrix)，利用基因體最佳線性不偏預測模型(genomic best linear unbiased prediction model , GBLUP model)預測雜交後代表現的育種價(GEBVs)，並進行優良品種的基因組選拔(GS)。而利用育種價得到雜交後代的特殊組合力(SCA)及其親本的一般組合力(GCA)，則可以用來計算雜交優勢(Midparent heterosis, MPH)以及優於親本表現的雜交優勢(Better-parent heterosis, BPH)。根據我們的研究結果，發現在玉米資料中Mo17, NC350, B73, B97和 OH7B為較具潛力的親本，而P026, P227, P236, P028和P235 則為南瓜資料中較具潛力的親本。 | zh_TW |
| dc.description.abstract | Genomic prediction has become an increasingly popular tool for hybrid performance evaluation in plant breeding mainly because it can reduce cost and accelerate a breeding program. We used two different crop data sets, one is the pumpkin (C. Maxima) data set consisting of 142 parental lines with 4521 filtered single nucleotide polymorphism (SNP) markers, and the other is the maize data set consisting of 24 parental lines with 46,134 filtered SNP markers. In this study, we propose a systematic procedure to predict hybrid performance using a linear mixed effects model that takes both additive and dominance marker effects into account. We first estimated the variance components of additive and dominance effects through restricted maximum likelihood estimation (REML), and used Henderdon’s equation to obtain the values of additive and dominance effects of hybrid lines which were used to build training data sets. Finally, we predict genomic estimated breeding values (GEBVs) for individual hybrid combinations and their parental lines through the genomic relationship matrix. The GEBV-based specific combining ability (SCA) for each hybrid and general combining ability (GCA) for its parental lines were then derived to quantify the degree of midparent heterosis (MPH) or better-parent heterosis (BPH) of the hybrid. According to our result, Mo17, NC350, B73, B97 and OH7B are the most potential parental lines in the maize data set; and P026, P227, P236, P028 and P235 are the most potential parental lines in the pumpkin data set. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:42:36Z (GMT). No. of bitstreams: 1 U0001-3108202213494900.pdf: 1445863 bytes, checksum: ac035d1142d2f7d7a4b30cf39ed206b8 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………….…………………… ⅰ 摘要……………………………………………….…………………… ⅱ Abstract….…………………………………………..…………….….. ⅲ List of Tables….……………………………………………………..... ⅵ List of Figures….………………………………..……………………. ⅶ 1. Introduction……………………………………………………………….. 1 2. Materials and Methods………………………………………….. 4 2.1 Pumpkin data set……………………………………………….... 4 2.1.1 Phenotype data……………………………………………… 4 2.1.2 Genotype data………………………………………………. 5 2.2 Maize data set…………………………………………………… 6 2.2.1 Phenotype data……………………………………………… 6 2.2.2 Genotype data………………………………………………. 6 2.3 Statistical models………………………………………………... 9 2.4 Estimation for marker effects…………………………………… 10 2.5 Prediction for GEBVs…………………………………………… 11 3. Result……………………………………………………………... 14 3.1 Pumpkin data analysis…………………………………………… 14 3.1.1 Prediction of potential hybrids and parental lines…………... 14 3.1.2 Variance components and heritability.………………………. 17 3.2 Maize data analysis…………………………………………….... 18 3.2.1 Prediction of potential hybrids and parental lines…………... 18 3.2.2 Variance components and heritability…………...………….. 20 4. Discussion…………………………………………………..…….. 21 References…………………………………………………………….. 27 Appendix - Rcode…………………………………………………..….31 | |
| dc.language.iso | en | |
| dc.subject | 特殊組合力 | zh_TW |
| dc.subject | 育種價 | zh_TW |
| dc.subject | 基因組預測 | zh_TW |
| dc.subject | 一般組合力 | zh_TW |
| dc.subject | 基因體最佳線性不偏預測模型 | zh_TW |
| dc.subject | specific combining ability | en |
| dc.subject | general combining ability | en |
| dc.subject | Genomic prediction | en |
| dc.subject | genomic estimated breeding values | en |
| dc.subject | genomic best linear unbiased prediction model | en |
| dc.title | 利用基因體預測評估雜交組合的表現 | zh_TW |
| dc.title | Hybrid performance evaluation in plant breeding via genomic prediction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 高振宏(Chen-Hung Kao),董致韡(Chih-Wei Tung) | |
| dc.subject.keyword | 基因組預測,基因體最佳線性不偏預測模型,育種價,特殊組合力,一般組合力, | zh_TW |
| dc.subject.keyword | Genomic prediction,genomic best linear unbiased prediction model,genomic estimated breeding values,specific combining ability,general combining ability, | en |
| dc.relation.page | 35 | |
| dc.identifier.doi | 10.6342/NTU202203014 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-09-01 | |
| 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 |
| dc.date.embargo-lift | 2022-09-30 | - |
| 顯示於系所單位： | 農藝學系 | |
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