Analyzing genetic association studies with an extended propensity score approach.

Propensity scores are commonly used to address confounding in observational studies. However, they have not been previously adapted to deal with bias in genetic association studies. We propose an extension of our previous method (Zhao et al., 2009) that uses a multilevel propensity score approach and allows one to estimate the effect of a genotype under an additive model and also simultaneously adjusts for confounders such as genetic ancestry and patient and disease characteristics. Using simulation studies, we demonstrate that this extended genetic propensity score (eGPS) can adequately adjust and consistently correct for bias due to confounding in a variety of circumstances. Under all simulation scenarios, the eGPS method yields estimates with bias close to 0 (mean=0.018, standard error=0.01). Our method also preserves statistical properties such as coverage probability, Type I error, and power. We illustrate this approach in a population-based genetic association study of testicular germ cell tumors and KITLG and SPRY4 susceptibility genes. We conclude that our method provides a novel and broadly applicable analytic strategy for obtaining less biased and more valid estimates of genetic associations.