對 NIMH 之精神分裂症資料作全基因相關性分析 ─ 基因型設算及罕見變異基因型檢定

Abstract

精神分裂症是一種複雜的疾病，它同時受到基因遺傳及環境因素的影響。我們從 NIMH 取得 case-control 資料作全基因相關性研究，包含了 741 位精神分裂症患者和 751 位一般人。首先，因為我們認為不同症狀是由不同致病基因所導致，我們利用 regression extension of latent class analysis (RLCA) 將精神分裂症分成數種症狀。另外，由於最近的研究發現，疾病的發生可能起因於罕見變異的基因型。因此我們藉由引入 HapMap 3 對未觀察到的基因型作設算。最後我們對每一個 SNP 作相關性檢定並找出顯著與精神分裂症相關之 SNP。另外，我們使用 cumulative minor-allele test (CMAT) 對罕見變異基因型作檢定。本文主要在探討基因型設算以及 single marker 檢定。

Schizophrenia is a complex disease caused by both genetic inheritance and environment factors. We use the case-control genome-wide association study (GWAS) with 741 cases and 751 controls from the National Institute of Mental Health (NIMH) database to test SNP association with schizophrenia. We use the regression extension of latent class analysis (RLCA) to categorize schizophrenia into sub-symptoms since we consider the disease genes would be different between sub-symptoms. Recent studies conclude that common diseases may be caused by rare variants. We extract untyped and missing genotypes by imputation and use HapMap 3 as the reference panel. Then we perform single SNP test of association to find significant SNPs and apply the cumulative minor-allele test (CMAT) to test association between schizophrenia and rare SNPs. In this thesis, we focus on SNP imputation and single marker test of association for rare SNPs.