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研究簡介
本實驗室主要研究方向是發展新的統計方法與軟體為致病基因定位,及尋找族群之間在演化上及遺傳上的差異。目前研究方向為:

  1. 應用統計方法與分析工具尋找與躁鬱症、糖尿病、高血壓、肥胖、葛瑞夫茲氏疾病、精神分裂症、乳癌、肝癌等複雜性疾病相關之致病基因。
  2. 發展新的基因交互作用模型,以探討基因交互作用模式於全基因體致病基因定位的可行性。
  3. 發展新的統計方法於整合單一核苷酸多態性標誌基因及染色體片段套數變異訊息,並探討此脫氧核糖核酸序列變異與其相對應訊息核糖核酸表現差異的關聯性。
  4. 發展新的多點相關性方法,藉由結合多點分數統計量與單套基因型分析方法,增加複雜性遺傳疾病致病基因定位的檢測力。
  5. 建構單一核苷酸多態性資料於漢人族群之人類白血球組織抗原基因型的預測模型,並將結果運用於致病基因篩撿。
  6. 利用基因路徑分析發展可結合生物訊息的內在生物表現型統計鑑別方法。

Research Description
The major research interest of our laboratory is using biostatistical methods in disease gene mapping, comparative genomics, population genetics, and phylogenetic analysis. We develop novel statistical methods/software and apply frequently used analytical tools to locate chromosomal positions of disease susceptibility genes. The ongoing projects are shown below:

  1. Apply statistical methodologies and analytical tools to identify genes that are associated with human complex diseases, such as bipolar disease, diabetes, hypertension, obesity, Graves’ disease, schizophrenia, breast cancer and liver cancer, etc.
  2. Develop new mathematical models to deal with gene-gene interactions and its application on GWA studies.
  3. Integrate single nucleotide polymorphism with copy number variation data to investigate correlations between DNA structure variations and mRNA gene expression differences in human populations.
  4. Develop new statistical methods for multilocus association mapping. These methods mainly use “longest significant run” based on independent data or dependent data assumptions.
  5. Predict HLA allele genotypes using SNP data for a Han Chinese population and its application in disease gene screening.
  6. Develop methodologies in identifying surrogate phenotypes or endophenotypes upon biological pathways.