Literature DB >> 29096048

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

Xiang Zhan1,2, Michael C Wu1.   

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.
© 2017, The International Biometric Society.

Entities:  

Keywords:  Fast permutation test; Genetic marker-set association; Kernel machines; Quantile regression

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Substances:

Year:  2017        PMID: 29096048      PMCID: PMC5932287          DOI: 10.1111/biom.12785

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  1 in total

1.  Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.

Authors:  Dehan Kong; Arnab Maity; Fang-Chi Hsu; Jung-Ying Tzeng
Journal:  Biometrics       Date:  2015-11-17       Impact factor: 2.571

  1 in total
  1 in total

1.  Robust kernel association testing (RobKAT).

Authors:  Kara Martinez; Arnab Maity; Robert H Yolken; Patrick F Sullivan; Jung-Ying Tzeng
Journal:  Genet Epidemiol       Date:  2020-01-14       Impact factor: 2.135

  1 in total

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