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

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)364-371
Number of pages8
JournalBiometrics
Volume72
Issue number2
DOIs
Publication statusPublished - 2016 Jun 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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