  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.
