陳秀熙臺灣大學：預防醫學研究所吳怡瑩Wu, Yi-YingYi-YingWu2007-11-282018-06-292007-11-282018-06-292007http://ntur.lib.ntu.edu.tw//handle/246246/59235除一般遺傳機制外，尚有基因外機制如：DNA甲基化，可在沒有DNA突變的情況下改變基因的表現。如：c-erbB2是一個已知的乳癌基因外因子。和乳癌有關的危險因子一共包含遺傳因子與環境因素，先前也有很多預測模式針對乳癌風險的估計。其缺點在於這些模式被分類為二階段模式而非可以描述疾病進展的三階段模式。故在本研究中，我們建立了三階段馬可夫模式來描述乳癌從正常、臨床前期到臨床期的自然史，也整合了遺傳、基因外因素與環境因子來描述疾病的進展。我們利用模擬的實証資料設計一個實驗來達成這個目標。而研究人員可應用三階段模式於遺傳諮詢上，在給定父母所處之疾病狀態時，估計子代帶有BRCA1或BRCA2基因的機率。最後，在處理疾病狀態位於臨床前期或臨床期不明的情況，可使用EM演算法來處理。In addition to genome influence, epigenetic mechanism, like DNA methylation, is an alterative genetic mechanism which may lead to heterogeneous genotype expression in the absence of DNA mutation. It was well known that c-erbB2 is one of epigenetic factors responsible for breast cancer. The risk factors in association with breast cancer include genetic and environmental factors. There are a number of predicted models for estimating the risk of breast cancer. The drawback is that they are all classified as two-state models rather than multi-state models which can delineate multi-states disease progression in related to genetic and environmental factors. In the present study, we constructed a three-state Markov model to depict breast cancer nature history from normal, preclinical, and to clinical phases. Genetic, epigenetic, and environmental factors obtained from literature are incorporated into model to assess their influences on disease progression. By simulating empirical data, we designed a hypothetical study to show how to achieve this object. The application of three-state model in genetic counseling enables one to estimate the probability of carrying BRCA1 or BRCA2 given information on parents’ disease states. Finally, to deal with the uncertainty about whether disease status is in pre-clinical or clinical phase EM algorithm was applied.口試委員會審定書 誌謝 中文摘要 英文摘要 第一章　前言與研究目的 1 第二章 文獻探討 3 2.1 遺傳因素與環境因子於乳癌之影響 3 2.2 風險模式的種類 3 2.2.1 估計乳癌得病機率 3 2.2.2計算帶有突變基因的機率 6 第三章 研究方法 9 3.1 步驟一：建立三階段模式 10 3.2 步驟二：利用電腦微模擬建立文獻所得實証世代 12 3.3 步驟三：乳癌三階段研究設計與應用 19 3.4 步驟四：家族資料應用於多階段模式 19 3.5 無法分類發病個案位於疾病進展階段之資料處理方法 22 第四章 結果 24 4.1 模擬資料樣本數 24 4.2 模擬結果與假設參數比對 26 4.3 三階段模式研究設計與應用 26 4.4 家族資料應用於三階段模式 40 4.5 利用EM algorithm處理”無法分類”疾病階段資料之結果 44 第五章 討論 461011525 bytesapplication/pdfen-US遺傳基因外因素多階段預測模式geneticepigeneticmulti-state predicted modelEM algorithm[SDGs]SDG3thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/59235/1/ntu-96-R94846004-1.pdf