臺灣博碩士論文加值系統

全基因體範圍檢測與分子或外表型性狀(molecular or phenotypic traits)遺傳變異有關的QTL熱點，已是各類生物學研究中至為關鍵的一環，因為QTL熱點匯聚了與數量性狀遺傳機制相關的重要訊息，也可連結到控制數量性狀的諸多基因。目前已發展出多種檢測QTL熱點的統計方法，這些方法主要利用摘要QTL資料（summarized QTL data）或來自於遺傳基因體試驗(genetical genomics experiment)之個體層級資料（individual-level data;包含每個個體的基因型及外表型)進行排序檢定（permutation test）來檢測QTL熱點。本研究提出一個新的QTL熱點統計分析方法，利用公開資料庫所收集之摘要QTL（區間）資料進行QTL熱點檢測。我們首先應用均勻分布(uniform distribution)將QTL區間資料轉換為期望QTL頻度(expected QTL frequency; EQF)矩陣。再考慮性狀間的相關性，將有相關的性狀分群並計算各群性狀之EQF，獲得降維的EQF矩陣。接著提出一套置換演算法(permutation algorithm)對EQF或QTL區間進行置換，算出一序列從嚴格到寬鬆的門檻值，用以評估QTL熱點。性狀分群後的置換演算能夠產生較嚴格的門檻值，故可避免檢測到假的熱點。我們用實例分析及模擬研究說明我們的方法，評估方法在熱點檢測的表現，並與其他方法比較。結果顯示，我們的方法可以有效控制全基因體錯誤率在目標錯誤水準之內，對有相關性的資料也能提供適合的門檻值，並且在表現上可與使用個體層級資料的方法相提並論。在分析GRAMENE水稻資料庫資料時，本法依所使用的門檻值的不同，可檢測出超過100個熱點。我們進一步將檢測到的熱點與Q-TARO水稻資料庫所蒐集之已知基因(known genes)，從位置及功能上進行全基因體的比較分析。結果顯示，熱點與已知基因在位置上具有一致性，已知基因的功能與熱點上的主要性狀亦有相關。我們提出的熱點分析法可以解析生物研究上QTL熱點、基因及數量性狀所形成之網絡架構，進而剖析複雜性狀的遺傳結構。本方法所開發的R套件QHOT放置於全球資訊網http://www.stat.sinica.edu.tw/chkao/及R CRAN供下載使用，可以輸出數值及圖像的QTL熱點分析結果供相關研究。

Genome-wide detection of quantitative trait loci (QTL) hotspots underlying variation in many molecular and phenotypic traits has been a key step in various biological studies since the QTL hotspots are highly informative and can be linked to the genes for the quantitative traits. Several statistical methods have been proposed to detect QTL hotspots. These hotspot detection methods rely heavily on permutation tests performed on summarized QTL data or individual-level data (with genotypes and phenotypes) from the genetical genomics experiments. In this article, I proposed a statistical procedure for QTL hotspot detection by using the summarized QTL (interval) data collected in public web-accessible databases. First, a simple statistical method based on the uniform distribution is derived to convert the QTL interval data into the expected QTL frequency (EQF) matrix. And then, to account for the correlation structure among traits, the QTLs for correlated traits are grouped together into the same categories to form a reduced EQF matrix. Furthermore, a permutation algorithm on the EQF elements or on the QTL intervals is developed to compute a sliding scale of EQF thresholds, ranging from strict to liberal, for assessing the significance of QTL hotspots. With grouping, much stricter thresholds can be obtained to avoid the detection of spurious hotspots. Real example analysis and simulation study were carried out to illustrate our procedure, evaluate the performances and compare with other methods. It showed that our procedure can control the genome-wide error rates at the target levels, provide appropriate thresholds for correlated data and be comparable to the methods using individual-level data in hotspot detection. Depending on the thresholds used, more than 100 hotspots are detected in GRAMENE rice database. I also performed a genome-wide comparative analysis of the detected hotspots and the known genes collected in the Rice Q-TARO database. The comparative analysis revealed that the hotspots and genes were conformable in the sense that they co-localize closely and were functionally related to relevant traits. Our statistical procedure can provide a framework for exploring the networks among QTL hotspots, genes and quantitative traits in biological studies. The R package QHOT that produce both numerical and graphical outputs of QTL hotspot detection in the genome are available on the worldwide web http://www.stat.sinica.edu.tw/chkao/ and has been submitted to Comprehensive R Archive Network (CRAN).

TABLE OF CONTENT

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 MATERIALS AND METHODS 7
2.1 The QTL matrix 7
2.2 The expected QTL frequency 8
2.3 The EQF matrix 10
2.4 The permutation algorithm 12
2.5 Circular genomic permutation 14
CHAPTER 3 RESULTS 20
3.1 The GRAMENE rice dataset example 21
3.1.1 The GRAMENE database and Q-TARO database 21
3.1.2 QTL hotspots 24
3.1.3 Effects of bin sizes and QTL interval sizes 25
3.1.4 QTL hotspots, gene locations and gene functions 27
3.1.5 Hotspot regions potential for functional genes 31
3.2 Simulation study 32
3.2.1 Genome-wide error rates 33
3.2.2 Performance in QTL hotspot detection 37
3.2.3 Genetical genomics experiments 41
CHAPTER 4 CONCLUSION AND FUTURE RESEARCH 45
ABBREVIATIONS AND GLOSSARY 52
LITERATURE CITED 54

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