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研究生:施又瑄
研究生(外文):Yu-Hsuan Shih
論文名稱:以全基因組關聯探討思覺失調症之神經認知功能缺損:多基因分數策略
論文名稱(外文):A Genome-wide Association Study of Neurocognitive Impairments in Schizophrenia: Polygenic Score Approach
指導教授:陳為堅陳為堅引用關係
指導教授(外文):Wei J. Chen
口試委員:郭柏秀劉智民蕭朱杏賴文崧
口試委員(外文):Po-Hsiu KuoChih-Min LiuChuhsing Kate HsiaoWen-Sung Lai
口試日期:2014-07-09
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:流行病學與預防醫學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:32
中文關鍵詞:思覺失調症全基因組關聯內表型認知功能缺損多基因分數分析
外文關鍵詞:schizophreniaGWASendophenotypecognitive impairmentpolygenic score analysis
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思覺失調症在基因型及臨床表徵都具有很高的異質性,其中神經認知功能受損在病人中具有高遺傳度。因此,利用神經認知功能指標做為內表型,將可以找到與神經認知功能顯著相關之單核&;#33527;酸多型性 (single-nucleotide polymorphisms, SNPs),並發現潛在之多基因影響。本研究為全基因組關聯的單純病例研究,期望納入神經認知功能缺損做為量性變項 (Quantitative trait),可找到與思覺失調症及神經認知功能相關之修飾基因 (Modifier gene),此外,我們也利用多基因分數分析,探討神經認知功能缺損在思覺失調症的病人是否受到多基因的影響。本研究共納入165位有思覺失調症的病人,每位病人都會有642,832個SNPs資料以及14個來自於連續操作測驗 (Continuous Performance Test, CPT) 及威斯康辛卡分類測驗 (Wisconsin Card Sorting Test, WCST) 的神經認知功能指標。在經過質量管控 (Quality control) 後,共有564,110個SNPs資料留下做後續分析。首先,我們使用主成分分析,將14個指標化簡成2個分別解釋總變異36.11%及18.30%的主成分,並探討其與SNPs之相關,但由於樣本數的限制並未偵測到任何全基因組相關。此外,為了進行多基因分數分析,我們利用MaCH及MiniMac軟體進行缺失基因資料的填補,並將可能具有高度連鎖不平衡之SNPs去除,因此共240,579個被納入進行多基因分析。接著,利用隨機抽樣將165位個案分成探索組 (n=83) 與驗證組 (n=82),我們發現由25個SNPs所得到的多基因分數,可成功預測病人在注意力的表現。此外,由219個SNPs所計算的多基因分數,可有效預測病人在執行能力的表現。由此可證明,思覺失調症病人在神經認知功能的表現,可能不是由於單一基因所造成之影響,而是受到多基因共同影響所致。

Schizophrenia is considered to be genetically and phenotypically heterogeneous. The high heritability of neurocognitive impairments found in patients with schizophrenia suggests that these impairments may serve as an endophenotype resulting from a set of underlying genes in a mode of polygenic effects. In this study, a total of 165 schizophrenia patients were selected and these subjects were subjected to genotyping for 642,832 single nucleotide polymorphisms (SNPs). After quality control, 564,110 SNPs were left. We then performed principal component analysis (PCA) on 14 indices of the Continuous Performance Test (CPT) and the Wisconsin Card Sorting Test (WCST). Two orthogonal principal components (PC1 and PC2) identified with PCA explained 36.11% and 18.30% of the total variance, respectively. Then the two PCs were investigated for association with SNPs. Because of small sample size in this study, however, we were unable to detect SNPs associated with neurocognitive performance in schizophrenia with genome-wide significance. For exploring the polygenic effect, polygenic score analysis was conducted on 240,579 LD-based pruning SNPs (r2 > 0.5), which were imputed using MaCH and MiniMac. The sample was first randomly divided into a learning set (n = 83) and a test set (n = 82). Then the effect size (β) for the results of association test in the learning set was used to develop a linear model to generate scores in the test set. The sustained attention demonstrated a significant association (p = 0.03) with the PC1 polygenic score at a threshold of p = 0.0001 (25 markers), For the executive function, when polygenic scores in the test set was calculated based on the results of the best 0.1% of SNPs (219 markers) in the association study of learning set.

口試委員審定書 I
致謝 II
摘要 III
Abstract IV
Contents V
List of Tables VII
List of Figures VIII
Chapter 1. Introduction 1
Chapter 2. Materials and Methods 4
2.1 Participants 4
2.2 Neurocognitive Assessment 4
2.3 Statistical Analysis 6
2.3.1 Genotyping and Quality Control (QC) 6
2.3.2 Genotype Imputation 6
2.3.3 Principal Component Analysis (PCA) of Neurocognitive performance Indices 7
2.3.4 QT Association Analysis 7
2.3.5 Polygenic Score Analysis 8
Chapter 3. Results 9
3.1 Demographic Data and Clinical Characteristics 9
3.2 PCA of Neurocognitive Performance Indices 9
3.3 Genetic Analysis of QT GWAS 10
3.4 Polygenic Score Analyses 11
Chapter 4. Discussion 12
References 15


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