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PreprintarXiv licencearXiv

Multimarker genetic association tests for panel count data

  • Kun Xia
  • Jianrui Zhang
  • Qing Lu
  • Chenxi Li
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Abstract

The existing multimarker survival tests focus on time to event outcomes. However, recurrent events are common in real world clinical and biomedical studies, especially in the research of chronic and recurrent diseases. In this paper, we develop a suite of set based genetic association tests for panel count outcomes under a unified weighted V statistic framework. These tests can effectively account for genetic effect heterogeneity in panel count data. Additionally, we develop small sample corrections to the tests to enhance the accuracy of the tests under small samples. Simulation studies show that the proposed tests perform well in terms of size and power across various scenarios and have a bigger power than the existing set based tests for interval censored outcomes. A dental caries GWAS data set is analyzed to illustrate the utility of the tests.

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Cite

BibTeXRIS
BibTeX
@misc{xia2026multimarker,
  title = {{Multimarker genetic association tests for panel count data}},
  author = {Kun Xia and Jianrui Zhang and Qing Lu and Chenxi Li},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.03113},
  eprint = {2609.03113},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.03113},
}
RIS
TY  - UNPB
TI  - Multimarker genetic association tests for panel count data
AU  - Kun Xia
AU  - Jianrui Zhang
AU  - Qing Lu
AU  - Chenxi Li
PY  - 2026
DA  - 2026/09/02
PB  - arXiv
DO  - 10.48550/arxiv.2609.03113
UR  - https://arxiv.org/abs/2609.03113
AB  - The existing multimarker survival tests focus on time to event outcomes. However, recurrent events are common in real world clinical and biomedical studies, especially in the research of chronic and recurrent diseases. In this paper, we develop a suite of set based genetic association tests for panel count outcomes under a unified weighted V statistic framework. These tests can effectively account for genetic effect heterogeneity in panel count data. Additionally, we develop small sample corrections to the tests to enhance the accuracy of the tests under small samples. Simulation studies show that the proposed tests perform well in terms of size and power across various scenarios and have a bigger power than the existing set based tests for interval censored outcomes. A dental caries GWAS data set is analyzed to illustrate the utility of the tests.
ER  -