An optimal kernel-based method for gene set association analysis

Abstract

Single-variant based genome-wide association studies have successfully detected many genetic variants that are associated with many complex traits. However, their power is limited due to weak marginal signals and ignoring potential complex interactions among genetic variants. Set-based strategy was proposed to provide a remedy where multiple genetic variants in a given set (e.g., gene or pathway) are jointly evaluated, so that the systematic effect of the set is considered. Among many, the kernel-based testing (KBT) framework is one of the most popular and powerful methods in set-based association studies. Given a set of candidate kernels, method has been proposed to choose the one with the smallest p-value. Such a method, however, can yield inflated type I error, especially when the number of variants in a set is large. Alternatively one can get p-values by permutations which, however, could be very time consuming. In this work, we proposed an efficient testing procedure that can not only control type I error rate but also generate power close to the one obtained under the optimal kernel. Our method is built upon the KBT framework and is based on asymptotic results under a high-dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the advantages of the method compared with its counterparts.