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Links Between the Sequence Kernel Association and the Kernel-Based Adaptive Cluster Tests

Author

Listed:
  • Weiming Zhang

    (Colorado School of Public Health)

  • Michael P. Epstein

    (Emory University School of Medicine)

  • Tasha E. Fingerlin

    (National Jewish Health)

  • Debashis Ghosh

    (Colorado School of Public Health)

Abstract

Two recently developed methods for the analysis of rare variants include the sequence kernel association test (SKAT) and the kernel-based adaptive cluster test (KBAC). While SKAT represents a type of variance component score test, and KBAC computes a weighted integral representing the difference in risk between variants, they appear to be developed using different initial principles. In this note, we show in fact that the KBAC can be modified to yield a test statistic with operating characteristics more similar to SKAT. Such a development relies on U- and V-statistic theory from mathematical statistics. Some simulation studies are used to evaluate the new proposed tests.

Suggested Citation

  • Weiming Zhang & Michael P. Epstein & Tasha E. Fingerlin & Debashis Ghosh, 2017. "Links Between the Sequence Kernel Association and the Kernel-Based Adaptive Cluster Tests," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 246-258, June.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9175-7
    DOI: 10.1007/s12561-016-9175-7
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    References listed on IDEAS

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    1. Wensheng Zhu & Yuan Jiang & Heping Zhang, 2012. "Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 1-11, March.
    2. Dajiang J Liu & Suzanne M Leal, 2010. "A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-14, October.
    3. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
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