Pathway-based genetic association analysis for overdispersed count data

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.