# The Robust Kernel Association Test

## TL;DR

The paper introduces RobKAT, a flexible and robust kernel association test that handles various data distributions without assuming normality, improving detection of genetic associations in complex data.

## Contribution

RobKAT is a novel, general kernel association test that extends SKAT and QRKM, offering robustness and flexibility in analyzing diverse data distributions.

## Key findings

- RobKAT controls type I error across distributions.
- RobKAT has comparable or greater power than SKAT.
- RobKAT detects additional associations in clinical data.

## Abstract

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.