Asymptotic powers for matched trend tests and robust matched trend tests in case-control genetic association studies

Abstract

The matched trend test (MTT), developed using a conditional logistic regression, has been proposed to test for association in matched case-control studies to control the bias of known confounding effects and reduce the potential impact of population stratification. The MTT requires a known genetic model. When the genetic model is unknown, a Monte Carlo robust test, MAX, has been proposed for the analysis of matched case-control studies. The MAX statistic takes the maximum of three MTTs optimal for three common genetic models. We derive the asymptotic power for MTTs and robust tests. In particular, we derive the asymptotic p -value for MAX. Using these analytical results, we conduct simulation studies to compare the performance of MAX and the two-degree-of-freedom Chi-square test for matched case-control studies, where the latter is implemented in most computing software. Our simulation results show that MAX is always asymptotically more powerful than the two-degree-of-freedom Chi-square test under common genetic models. Our results provide guidelines for the analysis of genetic association using matched case-control data. An illustration of our results to a real matched pair case-control etiologic study of sarcoidosis is given.