單核苷酸多態性(Single Nucleotide Polymorphisms, SNP)在人類全基因組內分布廣且密度高其位點數超過300萬個，對於雙極症(Bipolar Disorder, BP)這類型的複雜疾病通常由多個遺傳變異位點與環境因子所成，發病原因複雜，因此使用SNPs與疾病進行相關性研究可為科學家在尋找影響疾病之致病基因上提供一種方向，如全基因組關聯研究(Genome-Wide Association Studies, GWAS)即是應用人類基因組中的SNPs來找出與疾病相關的易感基因。雙極症是一種嚴重失能的精神疾病，有關於雙極症亞型中第一型雙極症(Bipolar I Disorder, BP-I)與第二型雙極症(Bipolar II Disorder, BP-II)的各種臨床表徵與研究，至今仍有許多爭議，因此在臨床上雙極症有著一定機率的錯誤診斷與不當治療，因此對於診斷雙極症的方式不能單靠一種方法來確診，隨著科學技術的重大進展，也可利用SNPs來建立一套診斷技術以作為互補原診斷方式之一種客觀方法。本研究欲應用資料探勘手法於全基因組數據中，期望能夠找出可以區別兩亞型之預測SNPs子集，並以ROC(Receiver Operating Characteristic, ROC)曲線分析來評估不同SNPs組合下之診斷效力以供日後臨床實務診斷之基礎參考，提升醫療品質，另也藉由基因本體論(Gene Ontology, GO)與路徑分析(Pathway Analysis)探究功能性基因並找出可能的致病機轉以提高分析結果之可信度。據分析結果證明本研究流程可成功找出雙極症的致病基因，也降低搜尋易感基因之所需花費的時間與其他成本，而在識別出的SNPs裡也可能含有未知的判別基因。

Genome-Wide Association Studies (GWAS) is a method which uses Single Nucleotide Polymorphism (SNP) to identify susceptibility genes associated with diseases. Bipolar disorder (BP) is a recurrent and chronic psychiatric illness. Clinically, BP-I and BP-II are the most severe subtypes. Due to their unclear pathological symptoms, it seems difficult to provide appropriate diagnosis and treatments immediately in clinical patients. In the current study, we apply data mining technology to whole genome data of BP (including BP-I and BP-II) and identify the gene that can distinguish two subtypes of BP in order to achieve a better treatment. The clinical dataset of Han-Chinese was used to validate the proposed data mining model. The results show that this study can identify BASP1 (brain abundant membrane-attached signal protein 1) which was justified by previous studies. Moreover, other SNPs we identified may be used to distinguish two subtypes of BP by building statistical classifier. But these results need further investigation via human-body functional validation because complex diseases may cause by the gene-gene or gene- environment interactions. Consequently, the proposed model can address time-consuming gene identification and also quantify the effect of gene-gene interactions.

摘要 I
EXTENDED ABSTRACT II
誌謝 VII
表目錄 XIII
圖目錄 XV
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程與論文架構 4
第二章 文獻探討 6
2.1 雙極症的遺傳流行病學研究 6
2.2 鑑定易感基因策略 7
2.2.1 全基因組關聯研究 (Genome-Wide Association Studies, GWAS) 7
2.2.2 功能克隆方法 (Functional Cloning Approach) 9
2.2.3 候選基因方法 (Candidate Gene Approach) 9
2.2.4 連鎖分析 (Linkage Analysis) 9
2.3 PLINK軟體 10
2.4 SNP質量控制 (SNP QUALITY CONTROL, SNP QC) 10
2.5連鎖不平衡 (LINKAGE DISEQUILIBRIUM, LD) 13
2.6 資料探勘策略探討 14
2.6.1 跨產業資料探勘標準作業程序 (Cross Industry Standard Process for Data Mining, CRISP-DM) 15
2.6.2 最小絕對壓縮挑選機制(Least Absolute Shrinkage And Selection Operator, LASSO) 18
2.6.3 AUC-RF (the area under ROC curve with RF) 19
2.6.4 AdaBoost 21
2.7 小結 23
第三章 研究方法 24
3.1 資料前處理 26
3.2 單變量統計檢定 26
3.3 資料探勘變數篩選 28
3.3.1 基因分型缺失數據填補 28
3.3.2 AUC-RF、AdaBoost與LASSO-LR之變數篩選 28
3.3.3 交叉驗證 31
3.3.4投票法 (Voting) 31
3.3.5 多重共線性(Multi-Collinearity) 32
3.3.5.1 多重共線性診斷 32
3.3.5.2 解決多重共線性方法 34
3.4 評估與比較 36
3.4.1 ROC Curve Analysis 36
3.4.1.1 ROC Curve衡量指標 36
3.4.1.2 ROC Curve建構方式 38
3.4.1.3 ROC Space 40
3.4.1.4 ROC Curve Cut-off Value 41
3.4.2 基因本體論(Gene Ontology, GO) 41
3.4.3 Pathway Analysis 42
第四章 案例分析─候選易感位點 44
4.1資料前處理 44
4.1.1 原始資料概述─tfam與tped檔案 44
4.1.2資料合併轉換與編碼 46
4.1.3 SNP質量控制 47
4.2 單變量統計檢定 47
4.3 重要SNP篩選 48
4.3.1 PLINK-LR 48
4.3.2 資料探勘變數篩選 52
4.3.2.1 基因分型缺失數據填補 52
4.3.2.2 AUC-RF 53
4.3.2.3 AdaBoost 55
4.3.2.4 LASSO-LR 57
4.3.3 投票法 (Voting) 58
4.3.4 多重共線性診斷 61
4.4 小結 63
4.4.1 Manhattan Plot 63
4.4.2 文獻討論 63
第五章 案例分析─評估與比較 66
5.1 MAPPING SNPS TO GENES 66
5.1.1 文獻探討：Mapping SNPs to Genes 66
5.1.2 Mapping距離門檻的敏感度分析 67
5.2 ROC CURVE ANALYSIS 75
5.2.1 訓練資料分析結果 77
5.2.2 測試資料分析結果 78
5.2.3 小結 79
5.3 GENE ONTOLOGY AND PATHWAYANALYSIS 82
5.3.1 分析工具說明 82
5.3.2 分析數據說明 83
5.3.3 參數設定 84
5.3.4 分析結果 85
5.3.4.1 第一組基因數據 (±20 kb) 85
5.3.4.2 第二組基因數據 (±200 kb) 92
5.3.4.3 第三組基因數據 (±500 kb) 100
5.3.5 分析結果小結 108
第六章 結論與未來研究及建議 113
6.1 CONTRIBUTIONS 113
6.2 結論 115
6.3 未來研究及建議 116
參考文獻 118
自傳 126

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