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Reader Reaction: A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.

ABSTRACT: 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.

SUBMITTER: Zhan X

PROVIDER: S-EPMC5932287 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Reader Reaction: A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.

Zhan Xiang X Wu Michael C MC

Biometrics 20171102 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 ...[more]

PMID: 29096048

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