gsSKAT: Rapid gene set analysis and multiple testing correction for rareâ variant association studies using weighted linear kernels

Abstract

Nextâ generation sequencing technologies have afforded unprecedented characterization of lowâ frequency and rare genetic variation. Due to low power for singleâ variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernelâ machine regression and adaptive testing methods for aggregative rareâ variant association testing have been demonstrated to be powerful approaches for pathwayâ level analysis, although these methods tend to be computationally intensive at highâ variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rareâ variant analysis using component geneâ level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for familyâ wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two caseâ control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide openâ source R code for public use to facilitate easy application of our methods to existing rareâ variant analysis results.