发表机构
Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对面板计数数据开发了一组基于集合的遗传关联检验，引入加权V统计量框架与小样本校正，模拟显示其效能优于现有区间删失结局检验，经龋齿GWAS数据集验证了实用性。
AI 中文摘要
现有多标记生存检验聚焦于事件时间结局，但现实临床与生物医学研究中频繁事件十分常见，尤其在慢性与复发性疾病研究领域。本文在统一加权V统计量框架下，开发了一组针对面板计数结局的基于集合的遗传关联检验方法，这些检验可有效解释面板计数数据中的遗传效应异质性。此外，本文为这些检验开发了小样本校正方法，以提升小样本下检验的准确性。模拟研究表明，所提检验在不同场景下的型一错误率与检验效能表现良好，且相比现有针对区间删失结局的基于集合检验具有更高的效能。通过分析一个龋齿GWAS数据集，进一步说明了这些检验的实用性。
英文摘要
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.
