Zhan Xiang, Wu Michael C
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania 17033, U.S.A.
Biometrics. 2018 Jun;74(2):764-766. doi: 10.1111/biom.12785. Epub 2017 Nov 2.
Kong et al. (2016, Biometrics 72, 364-371) presented a quantile regression kernel machine (QRKM) test for robust analysis of genetic marker-set association studies. A potential limitation of QRKM is the permutation-based test design may be unscalable for the massive sizes of modern datasets. In this article, we present an alternative strategy for p-value calculation of QRKM, which is capable of speeding up the QRKM testing procedure dramatically while maintaining the same testing performance as QRKM. The effectiveness of our approach is demonstrated via simulation studies.
孔等人(2016年,《生物统计学》72卷,第364 - 371页)提出了一种分位数回归核机器(QRKM)检验,用于对遗传标记集关联研究进行稳健分析。QRKM的一个潜在局限性是,基于排列的检验设计对于现代大规模数据集可能无法扩展。在本文中,我们提出了一种计算QRKM p值的替代策略,该策略能够在保持与QRKM相同检验性能的同时,显著加快QRKM检验过程。通过模拟研究证明了我们方法的有效性。