Multimarker genetic association tests for panel count data

2026-09-02

AI Summary

本文针对面板计数数据中的遗传关联问题，提出了一套基于加权V统计框架的多标记测试方法，并通过小样本修正提高准确性。

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.

Problem

• multimarker
• panel count data
• genetic association tests
• recurrent events

Innovation

• multimarker genetic association tests
• panel count data
• genetic effect heterogeneity
• weighted V statistic framework
• small sample corrections