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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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The sequence kernel association test for the proportional odds model.

Jingxin Yan^1,2, Xiaoyu Zhang¹, Shuying Wang³

¹State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Bioinformatics (Oxford, England)

|June 27, 2025

Summary

This summary is machine-generated.

We developed a new method, the Sequence Kernel Association Test for the Proportional Odds Model (POM-SKAT), to analyze gene-phenotype associations with ordered categorical traits. This powerful tool effectively identifies relevant gene variants in complex diseases.

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Sequence Kernel Association Test (SKAT) is widely used for gene-phenotype association studies but primarily for continuous or binary traits.
Ordered categorical phenotypes are common in genetic studies, necessitating specialized analytical methods.
Existing SKAT extensions do not adequately address ordered categorical phenotypes within a proportional odds model framework.

Purpose of the Study:

To develop a novel SKAT-type test for analyzing associations between genetic variants and ordered categorical phenotypes using a proportional odds model.
To introduce the Sequence Kernel Association Test for the Proportional Odds Model (POM-SKAT) to fill this methodological gap.

Main Methods:

Developed POM-SKAT, a score test utilizing quasi-likelihood to assess the variance of coefficients of interest.
Employed the Pearson Type III distribution to approximate the asymptotic distribution of the test statistic for P-value calculation.
Implemented the method with publicly available code.

Main Results:

Simulation studies confirmed that POM-SKAT performs effectively and demonstrates high statistical power in detecting gene-phenotype associations.
Applied POM-SKAT to rheumatoid arthritis data from Genetic Analysis Workshop 16, successfully identifying multiple relevant gene variants.
The proposed method shows promise for analyzing complex genetic data with ordered categorical outcomes.

Conclusions:

POM-SKAT provides a robust and powerful approach for genetic association studies involving ordered categorical phenotypes.
The method enhances the applicability of SKAT-type tests to a broader range of phenotypic data.
POM-SKAT is a valuable tool for identifying genetic variants associated with diseases exhibiting ordered categorical traits, as demonstrated in the rheumatoid arthritis analysis.