Abstract
Since it has been claimed that rare variants with extremely small allele frequency play a crucial role in complex traits, there is great demand for the development of a powerful test for detecting these variants. However, due to the extremely low frequencies of rare variants, common statistical testing methods do not work well, which has motivated recent extensive research on developing an efficient testing procedure for rare variant effects. Many studies have suggested effective testing procedures with reasonably high power under some presumed assumptions of parametric statistical models. However, if the parametric assumptions are violated, these tests are possibly under-powered. In this paper, we develop an optimal, powerful statistical test called the aggregated conditional score test (ACST) for simultaneously testing M rare variant effects without restrictive parametric assumptions. The proposed test uses a test statistic aggregating the conditional score statistics of effect sizes of M rare variants. In simulation studies, ACST generally performed well compared with the two most commonly used tests, the optimal sequence kernel association test (SKAT-O) and Kullback-Leibler distance test. Finally, we demonstrate the performance and practical utility of ACST using the Dallas Heart Study data.