Analyzing population based genetic association studies with propensity score approach

In population based genetic association studies, confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Propensity scores are often used to address confounding in observational studies. However, they have not been adapted to correct bias due to PS in genetic association studies. Currently, genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors such as patient characteristics. We further propose an extended genomic propensity score (eGPS) approach that allows one to estimate a genotype effect under various genetic models in candidate gene studies. Finally, we propose a new approach that combines principal components analysis and the propensity score (PCAPS) to correct for bias due to PS in genome-wide association studies (GWAS). Simulations show that our approach can adequately adjust for bias due to confounding and preserve coverage probability, type I error and power. We illustrate these approaches in a case-control GWAS of testicular germ cell tumors. We provide a novel and broadly applicable strategy for obtaining less biased estimates of genetic associations.