Boosting the Power of the Sequence Kernel Association Test by Properly Estimating Its Null Distribution

Abstract

The sequence kernel association test (SKAT) is probably the most popular statistical test used in rare-variant association studies. Its null distribution involves unknown parameters that need to be estimated. The current estimation method has a valid type I error rate, but the power is compromised given that all subjects are used for estimation. I have developed an estimation method that uses only control subjects. Named SKAT+, this method uses the same test statistic as SKAT but differs in the way the null distribution is estimated. Extensive simulation studies and applications to data from the Genetic Analysis Workshop 17 and the Ocular Hypertension Treatment Study demonstrated that SKAT+ has superior power over SKAT while maintaining control over the type I error rate. This method is applicable to extensions of SKAT in the literature.