ArticleJournal of applied statistics2025
Pathway-based genetic association analysis for overdispersed count data.
Article in Journal of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.