Robust and powerful gene-environment interaction tests using rare genetic variants in case-control studies

Many association analysis methods have been developed to detect disease related rare genetic variants or gene-environment interactions. Most of them are based on prospectively likelihood, so they are robust but might not be powerful enough. On the other hand, retrospective likelihood based methods assuming gene-environment independence can effectively improve the association test power, but they suffer from type‑I error rate inflation if the independence assumption is violated. The aim of this paper is to develop novel test methods to balance power and robustness by appropriately weighting the above retrospective likelihood based tests and the existing prospective likelihood based tests. The desired finite sample performances of the proposed methods are demonstrated through simulation studies and the application to a real dataset.