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

Biometrics. 2016 Jun;72(2):364-71. doi: 10.1111/biom.12438. Epub 2015 Nov 17.


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.

Keywords: Bootstrap; Genetic marker-set association; Kernel machines; Permutation; Quantile regression; Semiparametric; Smoothing parameter; Testing.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biomarkers*
  • Biometry / methods
  • Clinical Trials as Topic
  • Computer Simulation
  • Genetic Association Studies
  • Homocysteine / blood
  • Humans
  • Linear Models
  • Models, Genetic*
  • Models, Statistical*
  • Polymorphism, Single Nucleotide
  • Regression Analysis*


  • Biomarkers
  • Homocysteine