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

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  • Dehan Kong
  • Arnab Maity
  • Fang-Chi Hsu
  • Jung-Ying Tzeng

Abstract

type="main" xml:lang="en"> We consider quantile regression for partially linear models where an outcome of interest is related to covariates and a marker set (e.g., gene or pathway). The covariate effects are modeled parametrically and the marker set effect of multiple loci is modeled using kernel machine. We propose an efficient algorithm to solve the corresponding optimization problem for estimating the effects of covariates and also introduce a powerful test for detecting the overall effect of the marker set. Our test is motivated by traditional score test, and borrows the idea of permutation test. Our estimation and testing procedures are evaluated numerically and applied to assess genetic association of change in fasting homocysteine level using the Vitamin Intervention for Stroke Prevention Trial data.

Suggested Citation

  • Dehan Kong & Arnab Maity & Fang-Chi Hsu & Jung-Ying Tzeng, 2016. "Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine," Biometrics, The International Biometric Society, vol. 72(2), pages 364-371, June.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:2:p:364-371
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    Citations

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    Cited by:

    1. Liu, Yang & Sun, Wei & Hsu, Li & He, Qianchuan, 2022. "Statistical inference for high-dimensional pathway analysis with multiple responses," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    2. Dengke Xu & Jiang Du, 2020. "Nonparametric quantile regression estimation for functional data with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(8), pages 977-990, November.
    3. Dehan Kong & Arnab Maity & Fang†Chi Hsu & Jung†Ying Tzeng, 2018. "Rejoinder to “A note on testing and estimation in marker†set association study using semiparametric quantile regression kernel machineâ€," Biometrics, The International Biometric Society, vol. 74(2), pages 767-768, June.
    4. Xiang Zhan & Michael C. Wu, 2018. "Reader Reaction: A note on testing and estimation in marker†set association study using semiparametric quantile regression kernel machine," Biometrics, The International Biometric Society, vol. 74(2), pages 764-766, June.

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