臺灣博碩士論文加值系統

美國昂飛（Affymetrix）公司推出新世代人種優化之Axiom單核苷酸多型性（SNP）生物晶片，為一個具備成本效益、高密度與高通量特性的基因型鑑定平台。然而，沒有公開免費軟體可針對此平台所產生的雜交強度及基因型資料提供整合性基因體分析。本研究開發了一套分析單核苷酸多型性探針雜交強度和基因型資料的統計方法與系統，整合了等位基因頻率（AF）、等位基因不平衡（AI）、異結合型丟失（LOH）、連續長片段同結合型（LCSH）、遺傳性拷貝數變異（CNV）與癌化性拷貝數異常（CNA）等基因體分析方法。本研究共分析了3,236個使用數個不同單核苷酸多型性平台進行基因型鑑定的樣本的資料。實際資料分析的研究結果顯示我們所提出的等位基因頻率校正方法大幅度地增加了等位基因頻率估計的準確度；所提出的快速版環狀二元分段法（Quick-CBS）能夠有效地減少原版環狀二元分段法約30–67 %的計算時間。模擬分析研究結果呈現我們所提出整合AI與LOH/LCSH的CNV/CNA偵測方法能夠良好地控制偽陽性率（FPR），並且提供令人滿意的真陽性率（TPR）。此外，即時定量聚合酶連鎖反應（real-time qPCR）實驗成功地驗證了由我們提出的方法在分析Axiom單核苷酸多型性資料後所偵測到的拷貝數變異結果。我們另外準備了Array 6.0平台資料，利用昂飛公司的GTC軟體來分析這些已獲得生物驗證的拷貝數變異區段，結果發現GTC的分析只能成功偵測到部分區段。我們將所有提出的這些統計方法封裝成一個整合分析軟體ALICE（AF/LOH/LCSH/AI/CNV/CNA Enterprise），不僅提供圖形使用者介面以方便使用者進行分析，所有方法皆能在多核心處理器的設備上以平行運算的模式增加執行效率。ALICE軟體與其建構的參考資料庫提供了分析Axiom與其他單核苷酸多型性平台基因體資料的有用分析資源，這些資源皆可於http://hcyang.stat.sinica.edu.tw/software/ALICE.html下載。

Affymetrix Axiom single nucleotide polymorphism (SNP) arrays provide a cost-effective, high-density, and high-throughput genotyping solution for population-optimized analyses. However, no public software is available for the integrated genomic analysis of hybridization intensities and genotypes for this new-generation population-optimized genotyping platform. A set of statistical methods was developed for an integrated analysis of allele frequency (AF), allelic imbalance (AI), loss of heterozygosity (LOH), long contiguous stretch of homozygosity (LCSH), and copy number variation or alteration (CNV/CNA) on the basis of SNP probe hybridization intensities and genotypes. This study analyzed 3,236 samples that were genotyped using different SNP platforms. Our proposed AF adjustment method considerably increased the accuracy of AF estimation. The proposed quick circular binary segmentation algorithm for segmenting copy number reduced the computation time of the original segmentation method by 30–67 %. The proposed CNV/CNA detection, which integrates AI and LOH/LCSH detection, had a promising true positive rate and well-controlled false positive rate in simulation studies. Moreover, our real-time quantitative polymerase chain reaction experiments successfully validated the CNV/CNA that were identified in the Axiom data analyses using the proposed methods; some of the validated CNV/CNA were not detected in the Affymetrix Array 6.0 data analysis using the Affymetrix Genotyping Console. All analysis functions that they can be efficiently implemented in parallel on multi-core devices are packaged into the ALICE (AF/LOH/LCSH/AI/CNV/CNA Enterprise) software. ALICE and the used genomic reference databases, which can be downloaded from http://hcyang.stat.sinica.edu.tw/software/ALICE.html, are useful resources for analyzing genomic data from the Axiom and other SNP arrays.

Acknowledgments i
Chinese Abstract iv
English Abstract v
Contents vi
List of Figures viii
List of Tables x
Chapter 1 Introduction 1
Chapter 2 Methods 4
2.1 Extraction of HI 4
2.2 Preprocessing of HI 4
2.3 Individual-level AF estimation with a CPA + LIM adjustment 8
2.4 Single-point index of AI detection 8
2.5 Single-point index of LOH/LCSH detection 9
2.6 Single-point index of CNV/CNA detection 10
2.7 Multipoint indices of AI, LOH/LCSH, and CNV/CNA detection 12
2.8 CN segmentation 13
Chapter 3 Real data analysis study 15
3.1 Sample materials and genotyping 15
3.2 Ethics, Consent and Permissions 16
3.3 Evaluation of proposed coefficient of preferential amplification or hybridization and linear interpolation method adjustment for AF estimation 16
3.4 Whole-genome AF (First panel in the graphical output of ALICE) 19
3.5 Detection of AI (Second panel in the graphical output of ALICE) 19
3.6 Detection of LOH/LCSH (Third panel in the graphical output of ALICE) 21
3.7 Detection of CNV/CNA (Fourth to sixth panels in the graphical output of ALICE) 21
3.8 Consistency in the results of Axiom and Array 6.0 in analyzing a pure tumor tissue sample 24
3.9 AF, AI, LOH/LCSH, and CNV/CNA analysis of admixed samples of tumor cells and corresponding normal cells 26
3.10 Paired-sample analysis 31
3.11 Real-time qPCR validation 36
3.11.1 Experimental procedure 36
3.11.2 Validation results 38
3.12 CN segmentation 41
Chapter 4 Simulation study 43
4.1 Simulation procedures 43
4.2 FPR of single-point CN detection 47
4.3 TPR of single-point CN detection 47
4.4 FPR of multipoint CN detection 47
4.5 TPR of multipoint CN detection 48
4.6 Effect of inter-marker spacing 50
Chapter 5 ALICE software 56
5.1 ALICE software 56
5.2 ALICE genomic reference databases 62
5.3 Availability of supporting data 62
Chapter 6 Conclusion and discussion 63
6.1 Conclusion 63
6.2 Discussion 63
References 68

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