The Robust Kernel Association Test (RobKAT)

Kara Martinez, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan, Jung Ying Tzeng

Research output: Contribution to journalArticlepeer-review


Testing the association between SNP effects and a response is a common task. Such tests are often carried out through kernel machine methods based on least squares, such as the Sequence Kernel Association Test (SKAT). However, these least squares procedures assume a normally distributed response, which is often violated. Other robust procedures such as the Quantile Regression Kernel Machine (QRKM) restrict choice of loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with flexible choice of loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power to SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust kernel association test on data from the CATIE clinical trial to detect associations between selected genes on chromosome 6, including the Major Histocompatibility Complex (MHC) region, and neurotrophic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP-sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jan 27 2019


  • Genetic association test
  • Kernel machine regression
  • Multi-marker hypothesis test
  • Robust regression
  • Schizophrenia
  • Semiparametric

ASJC Scopus subject areas

  • General

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