Pathway-based genetic association analysis for overdispersed count data

Yang Liu

Journal of Applied Statistics, 2025, vol. 52, issue 12, 2306-2320

Abstract: Overdispersion is a common phenomenon in genetic data, such as gene expression count data. In genetic association studies, it is important to investigate the association between a gene expression and a set of genetic variants from a pathway. However, existing approaches for pathway analysis are primarily designed for continuous and binary outcomes and are not applicable to overdispersed count data. In this paper, we propose a hierarchical approach to analyze the association between an overdispersed count response and a set of low-frequency genetic variants in negative binomial regression. We derive score-type test statistics for both fixed and random effects of genetic variants, and further introduce a novel procedure for efficiently combining these two statistics for global testing. Through simulation studies, we demonstrate that the proposed method tends to be more powerful than existing methods under a wide range of scenarios. Additionally, we apply the proposed method to a colorectal cancer study, demonstrating its power in identifying associations between gene expression and somatic mutations.

Date: 2025
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DOI: 10.1080/02664763.2025.2460073

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