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研究生:潘佳菁
研究生(外文):China-Ching Pan
論文名稱:矯正混合去氧核糖核酸實驗中核酸不等放大率以進行有效的致病基因定位
論文名稱(外文):Adjustment of preferential amplification for better disease-gene mapping in a DNA-pooling experiment
指導教授:范盛娟范盛娟引用關係
指導教授(外文):Cathy SJ Fann
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
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:2004
畢業學年度:92
語文別:中文
中文關鍵詞:混合去氧核糖核酸實驗
外文關鍵詞:DNA pooling
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  • 被引用被引用:0
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  • 下載下載:6
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尋找疾病可能的致病基因在後基因體時代是件十分重要的工作。傳統上,利用個人基因型鑑定資料的分析方法雖很常用,但是非常昂貴;相對上,混合去氧核糖核酸樣本的實驗方式,將受鑑定者的去氧核糖核酸混合後一起鑑定,則有利於降低鑑定成本和節省核酸樣本。
在混合實驗中,精準的等位基因頻率估計是有效定位致病基因的關鍵,然而,核酸不等放大率的現象會干擾等位基因頻率估計。Hoogendoorn et al. (2000) 針對此問題,提出一個簡單的矯正方法,優點是概念十分簡單,但卻可能產生估計偏誤,並且沒有理論性質討論。本論文主要是利用平均估計和比率估計的概念,提出一些新的校正方法,並且利用重抽法估算其標準誤。
除了矯正方法的探討外,本論文也評估在矯正程序中所需要的樣本數,並對其他可能影響混合實驗的變異來源加以討論,在所提出的矯正方法下,發展適當的致病基因定位方法、單套型基因頻率估計和連鎖不平衡係數估計,完整地建構整套混合實驗研究的流程與分析方法。
為了評估所提出方法的表現,本論文採用模擬分析研究法,比較各個矯正估計的良窳,並且探討其對致病基因定位的影響。結果顯示,相較於過去的矯正法,我們所提出的矯正方法的確有較低的估計偏誤和誤差均方根,矯正的改進也反應在較為精準的等位基因頻率估計上。在致病基因定位的表現方面,檢定結果深受實驗變異的影響,實驗變異會造成統計檢定力的降低。比較各個不同矯正方法的結果後發現,一般而言,過去的矯正方法表現不錯,而所提出的比率偏差矯正估計則一致優於過去的方法。文末並以一組國家基因型鑑定中心所提供的資料為例,說明所提出的新方法的使用。
In the post-genome era, disease gene mapping using dense genetic markers has become an important tool for dissecting complex inheritable diseases. Locating disease susceptibility genes using DNA pooling experiments is an economical alternative to those involving individual genotyping. The foundation of a successful pooling association test is a precise and accurate estimation of allele frequency. In this paper, we propose some new adjustment methods, based on the concept of ratio estimator, that correct for preferential amplification of nucleotides when estimating the allele frequency of single nucleotide polymorphisms. We also discuss the sample size, the estimation of coefficient of linkage disequilibrium and new association test when calibrating unequal allelic amplification. Complete the whole pipeline of disease-gene mapping using a DNA pooling design.
We have conducted simulation studies to assess the performance of different adjustment procedures and find that our proposed adjustments are more reliable with respect to the estimation bias and root mean squared error compared with current approach (Hoogendoorn et al. 2000). The improved performance not only benefits the estimation of allele frequency but leads to more powerful disease gene mapping. An authentic data set from National Genotyping Center Core is used for illustration of the proposed methods.
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