An Extended Propensity Score Approach for the Analysis of Population-Based Genetic Association Studies

• Activity Number: 418
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Huaqing Zhao*+ and Timothy R. Rebbeck and Nandita Mitra
• Companies: University of Pennsylvania School of Medicine and University of Pennsylvania School of Medicine and University of Pennsylvania School of Medicine
• Keywords: propensity score ; confounding ; population stratification ; genetic association study ; case-control study ; testicular cancer

Abstract

Propensity scores are often used to address confounding in observational studies. However, they have not been adapted to deal with confounding due to population stratification in genetic association studies. We propose an extended genomic propensity score (eGPS) approach that allows one to estimate a genotype effect under various genetic models and adjusts for genetic markers and patient covariates. We extend the Rosenbaum and Rubin approach to multi-level genotypes under weak ignorability assumptions. Simulations show that the eGPS can adequately adjust for bias due to confounding (mean bias=0.01, SE=0.02) and preserve coverage probability, type I error and power. We illustrate this approach in a case-control study of testicular cancer and KITLG and SPRY4 susceptibility genes. We provide a novel and broadly applicable strategy for obtaining less biased estimates of genetic associations.