Robust kernel association testing (RobKAT).

TitleRobust kernel association testing (RobKAT).
Publication TypeJournal Article
Year of Publication2020
AuthorsMartinez, Kara, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan, and Jung-Ying Tzeng
JournalGenet Epidemiol
Date Published2020 Apr
KeywordsAlgorithms, Computer Simulation, Genetic Association Studies, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Selection, Genetic

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the 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 a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic 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.

Alternate JournalGenet Epidemiol
Original PublicationRobust kernel association testing (RobKAT).
PubMed ID31943371
PubMed Central IDPMC7179838
Grant ListN01MH90001 / MH / NIMH NIH HHS / United States
T32 GM081057 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States