标题: 对NIMH之精神分裂症资料作全基因相关性分析-以潜在类别方法将病人分组
Genome-Wide Association for Schizophrenia in the NIMH Database-Patient Subgrouping by Latent Class Analysis
作者: 黄琬婷
Huang, Wan-Ting
黄冠华
Huang, Guan-Hua
统计学研究所
关键字: 全基因相关性分析;潜在类别方法;主成分分析;精神分裂症;病人分组;GWAS;the regression extension of latent class analysis;principal component analysis;schizophrenia;patient subgroup
公开日期: 2010
摘要: 精神分裂症是一种复杂的疾病,同时受到基因遗传及环境因素的影响。我们从NIMH取得case-control资料,其中包含741位精神分裂症患者和751位一般人,透过全基因相关性研究,希望可以找出导致精神分裂症的基因位置。因为不同症状的精神分裂症导致的基因位置可能不同,所以我们先以潜在类别方法依正性负性症状评量(PANSS)将病人分成数种症状。又为了避免不同种族造成的影响,在作关联性检定时,用主成分分析调整种族。另外藉由基因型设算可以估计出未观察到的基因。本文主要在探讨以潜在类别方法将病人分组和用主成分分析调整种族的部分。
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. Because different symptoms of schizophrenia may be caused by different genes, we use the regression extension of latent class analysis (RLCA) to classify patients based on their responses to Positive and Negative Syndrome Scale (PANSS) measures, and perform GWAS separately for each individual patient subgroup. To avoid potential population stratification, we apply principal component analysis (PCA) to adjust for the race effect when testing the association. We also extract the genotypes of rare SNPs by imputation to evaluate the contribution to the disease from multiple rare variants. In this thesis, we focus on schizophrenia patient subgroup identification by RLCA and population stratification adjustment by PCA.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079826501
http://hdl.handle.net/11536/47665
显示于类别:Thesis