# BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases

## TL;DR

BayesKAT is a new Bayesian method that improves detection of genetic associations in complex diseases by adaptively selecting optimal kernels.

## Contribution

BayesKAT introduces a Bayesian framework that adaptively selects optimal composite kernels for genetic association testing, improving power and type I error control.

## Key findings

- BayesKAT outperforms existing methods in detecting complex genetic associations and controlling type I errors.
- It provides mechanistic insights into human diseases by analyzing functionally related genetic variant groups.
- The method is scalable and effective for high-dimensional genetic data.

## Abstract

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.