利用穩定表現型擴充SNP以找出影響阿茲海默症的最佳基因組合

Abstract

複雜疾病往往由基因與基因、或是基因與環境等多種因素共同組成，而並非單一 因子就能導致疾病的發生。在全基因組關聯分析過程中，由於基因數量過於龐大， 無法將每一個基因的關聯性一一求出，因此我們在分析基因之間的交互作用上， 引用了穩定表現型(endophenotype)的理念。這種替代性指標( surrogate endpoints)的想法，已經廣泛被應用在生物醫學上，而本論文以基因表現量(gene expression)當作穩定表現型，以擴充有高風險導致阿茲海默症的基因組，進而運算基因與基因之間的交互作用，找到最佳的組合模式。另一方面，本文也將以相同的手法，應用於家族資料(family data)以及病例對照資料(case-control data)的樣本上，比較所得到的結果。

Complex diseases are often formed by the combined effect of multiple factors, not a single gene. In the genome-wide association studies, it is not possible to include all genes for performing gene-gene interaction analysis due to the large amount of genes included in the study. This thesis advocates an approach that uses gene expression as endophenotypes to facilitate the identification of the effects of genetic interaction in the Alzheimer’s disease. Furthermore, we compare the different results between case-control samples and family samples in our proposed method.