STATISTICAL METHODS FOR CONTROLLING POPULATION STRATIFICATION AND GENE-BASED ASSOCIATION STUDIES

This dissertation includes three papers with each distributed in one chapter. In chapter 1, we use extensive simulation studies and real data studies to evaluate the performance of using the linkage disequilibrium score regression (LDSC) for controlling population stratification. In chapter 2, we propose a gene-based statistical method that leverage gene expression (GE) measurements and polygenic risk scores (PRS) to identify genes that are associated with a phenotype of interest. In simulation studies, the proposed method has correct type I error rates and can boost power comparing to other methods that use either gene expression or PRS in association tests. The real data analysis based on UK Biobank data for the asthma disease shows that the proposed method is also applicable to GWAS. In chapter 3, we analytically derive the distribution of TOW test statistics and modify TOW to utilize GWAS summary statistics (TOW-S). Simulation studies show that TOW-S has correct type I error rates and can retain power among all scenarios