臺灣博碩士論文加值系統

基因體選種 (genomic selection, GS) 是一種新興的分子標誌輔助選種方法 (marker-assisted selection, MAS)，利用覆蓋整個基因體的大量分子標誌計算個體育種價估計值 (genomic estimated breeding value, GEBV)，並依此選拔出優良個體。由於高通量基因型鑑定技術的演進及成本下滑，研究者期盼藉由高通量基因型資料取代部份的外表型調查工作，使得單位時間能產生更佳的選拔效率。本研究欲藉由模擬探討GS在水稻育種計畫中之遺傳增進效果，分別探討雙親本雜交 (bi-parental cross) 及輪迴選種 (recurrent selection) 兩種育種流程。在此利用327個秈稻 (Oryza sativa L. ssp. indica) 優良品系的產量、抽穗期及株高之性狀資料，及5,264個單核苷酸多型性分子標誌 (single nucleotide polymorphism, SNP) 的基因型資料，以十折交叉驗證 (10-fold cross-validation) 比較8個統計方法的預測準確度，選出各性狀最適合之統計方法。比較結果顯示產量和株高最適用之GS模型為RR-BLUP，抽穗期則以BayesB最佳，後續以這兩種方法預測個體之GEBV。從327品系中選出5個產量表現優異之品系作為雙親本雜交親本，在傳統外表型選種 (phenotypic selection, PS) 架構下，於F2及F6世代另外以GS選拔產量性狀，比較10個雜交組合之GS與PS結果。結果顯示九個雜交組合之GS平均產量優於PS，六個雜交組合中GS選拔之品系較PS早抽穗，八個雜交組合顯示GS選拔之品系株高較PS矮。輪迴選種以327品系中產量與抽穗期各前四名共八個品系作為親本，結果顯示，隨著循環數 (cycle) 增加，族群的產量與抽穗期皆有顯著改進，且對偶基因頻度於Cycle 2即大量固定，GS能有效改良族群並快速累積有利對偶基因頻度。本研究以模擬方式探討GS在水稻育種計畫中之應用，未來或可先以輪迴選種改良族群，再從中選出優良親本雜交，於F2早世代及F6以GS方式節省大量人力且較精確選拔出優良品系。

Genomic selection (GS) is a new marker-assisted selection (MAS) method. GS uses a large number of molecular markers covering the entire genome to estimate the genomic estimated breeding value (GEBV) based on which the selection is made. Thanks to the evolution of high-throughput genotyping techniques and the consequent decline in costs, researchers expect to replace part of the phenotype survey by high-throughput genotypic data, resulting in a better selection efficiency per unit time. In this study, we wanted to explore the genetic improvement of GS in rice breeding program, and to explore the breeding methods of bi-parental cross and recurrent selection. The data set comprised of yield, flowering time (FL) and plant height (PH) from 327 indica rice (Oryza sativa L. ssp. indica) and 5,264 single nucleotide polymorphisms (SNP). Ten-fold cross-validation was used to assay the prediction accuracy of eight statistical models. The results showed that RR-BLUP was the most suitable model for yield and PH while BayesB for FL. In the traditional phenotypic selection (PS) framework, five lines with excellent yield were selected as parents from the initial 327 varieties. GS was applied in F2 and F6 in order to compare GS and PS in 10 bi-parental crosses. In recurrent selection scheme, the top four lines in yield and the earliest four liens in FL from the initial 327 varieties, were used as a parent. The results showed that the yield and FL of the population were significantly improved with the increase of cycle number, and the allele frequency is almost fixed in Cycle 2. GS can effectively improve the population and quickly accumulate favorable alleles. Based on the present study, a possible breeding scheme using GBS could be population improvement by recurrent selection then purify potential lines or select parents for bi-parental cross. Using GS in F2 early generation and F6 can be a way to save much manpower and precisely select candidate lines.

口試委員審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
表目錄 vii
圖目錄 viii
中英對照表 ix
一、前言 1
1.1基因體選種簡介 1
1.2 統計方法 3
1.3 目前GS研究趨勢 5
1.4 研究目的 7
二、材料與方法 8
2.1 植物材料之外表型資料及基因型資料 8
2.2 變方分析與外表型資料校正 9
2.3 族群結構分析與全基因體關聯分析 10
2.4 GS模型之分子標誌 10
2.5 建立預測模型 11
2.5.1 RR-BLUP 11
2.5.2 BRR 12
2.5.3 Bayesian LASSO 13
2.5.4 BayesA、BayesB與BayesC 13
2.5.5 RKHS 14
2.5.6 Random Forests 15
2.6 十折交叉驗證 15
2.7 雙親本雜交 16
2.8 輪迴選種 17
三、結果 18
3.1 外表型資料 18
3.2 族群結構分析與全基因體關聯分析 18
3.3 統計模型比較 19
3.4 雙親本雜交之GS與PS比較 19
3.5 輪迴選種 20
四、討論 21
4.1 統計模型預測準確度 21
4.1.1 影響預測準確度因子 21
4.1.2 性狀遺傳結構與統計模型關係 22
4.1.3 利用歷史資料建立預測模型 24
4.2 雙親本雜交 24
4.2.1 探討雙親本雜交產量性狀GS效率較PS高之原因 24
4.2.2 實際育種計畫之應用 25
4.3 輪迴選種 27
4.3.1 族群多樣性下降 27
4.3.2 初始親本選拔方式 28
4.4 雙親本雜交與輪迴選種 29
五、結論 30
參考文獻 62
附錄 69

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