title: Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).
abstract: Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.
authors.affiliation: Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Urrutia
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA.
authors.forename: Seunggeun
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695, USA.
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
authors.forename: Ni
authors.initials: N
authors.lastname: Zhao
authors.affiliation: Quantitative Sciences, R&D, GlaxoSmithKline, 5 Moore Drive, Research Triangle Park, NC 27709, USA.
authors.forename: Judong
authors.initials: J
authors.lastname: Shen
authors.affiliation: Department of Genetics and Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Wu
keywords: Perturbation; Rare variants; Sequence kernel association test; Sequencing association studies