Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT)

Eugene Urrutia, Seunggeun Lee, Arnab Maity, Ni Zhao, Judong Shen, Yun Li, Michael C. Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Analysis of rare genetic variants has focused on regionbased 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.

Original languageEnglish (US)
Pages (from-to)495-505
Number of pages11
JournalStatistics and its Interface
Volume8
Issue number4
DOIs
StatePublished - 2015

Keywords

  • Perturbation
  • Rare variants
  • Sequence kernel association test
  • Sequencing association studies

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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