Genome-Wide Association for Schizophrenia in the NIMH Database－Patient Subgrouping by Latent Class Analysis

• Main Authors: Huang, Wan-Ting, 黃琬婷
• Other Authors: Huang, Guan-Hua
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/05216250386638207698

碩士 === 國立交通大學 === 統計學研究所 === 99 === 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.