# The sequence kernel association test for the proportional odds model

## TL;DR

This paper introduces a new statistical test for analyzing genetic data with ordered categorical outcomes, improving the detection of gene-phenotype associations.

## Contribution

The paper introduces POM-SKAT, a novel method for analyzing ordered categorical phenotypes using a proportional odds model.

## Key findings

- POM-SKAT performs well in simulations and detects gene-phenotype associations effectively.
- The method was successfully applied to rheumatoid arthritis data, identifying relevant gene variants.

## Abstract

The Sequence Kernel Association Test (SKAT) and its extensions are the most popular methods for studying the association between phenotypes and a set of single nucleotide polymorphisms. Their practical application is very wide, but most of these methods are designed for continuous and binary phenotypes. Ordered categorical phenotypes are also very common in practice, so there is an urgent need to develop SKAT-type tests for proportional odds model.

To accommodate ordered categorical phenotypes, we propose a test named the Sequence Kernel Association Test for the Proportional Odds Model (POM-SKAT). It constructs a score test for the variance of the coefficients of interest using a quasi-likelihood and the P-value is evaluated by approximating the asymptotic distribution of the test statistic with the Pearson Type III distribution. Simulation studies demonstrate that our method performs well and achieves high power in detecting gene-phenotype associations. We apply POM-SKAT to rheumatoid arthritis data provided by Genetic Analysis Workshop 16, identifying multiple relevant gene variants.

Code is available at GitHub (https://github.com/amss-stat/POM-SKAT).