Testing genetic association in pair-matched case-control design by incorporating Hardy-Weinberg disequilibrium

Abstract

The matched case-control design is widely used in genetic association studies to control potential confounding variables. A commonly used method for analyzing such data is the Mantel-Haenszel (MH) test which can be derived as the score test of a conditional logistic regression model and may not be powerful when the genetic model is mispecified. Trend test incorporating the underlying genetic model is known to be more powerful than the MH test. However, in the practice, the genetic model is usually unknown to the researchers. To circumvent this issue and to retain power of the trend test, MAX-type test that takes the maximum of the typical trend tests was proposed, which was shown to be robust efficient. Here another robust efficient method was proposed by incorporating information of deviation from Hardy-Weinberg equilibrium, which is referred to as the matched Hardy-Weinberg disequilibrium test (mHWD). Our method uses the trend test as the association test but the score of which is determined by the information from mHWD. In this sense, the proposed procedure is a data-driven trend test method. The critical values and p-values of the proposed test can be easily obtained using simple Monte-Carlo methods. Simulation studies show that the proposed test exhibits greater efficiency robustness than the existing tests across a class of scientifically plausible genetic models. The proposed method is illustrated by analyzing a real data set in sarcoidosis study.