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

Dehan Kong, Arnab Maity, Fang Chi Hsu, Jung Ying Tzeng

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)364-371
頁數8
期刊Biometrics
72
發行號2
DOIs
出版狀態Published - 2016 6月 1

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 生物化學、遺傳與分子生物學 (全部)
  • 免疫學與微生物學 (全部)
  • 農業與生物科學 (全部)
  • 應用數學

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