|Title||Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Kong, Dehan, Arnab Maity, Fang-Chi Hsu, and Jung-Ying Tzeng|
|Date Published||2016 Jun|
|Keywords||Algorithms, Biomarkers, Biometry, Clinical Trials as Topic, Computer Simulation, Genetic Association Studies, Homocysteine, Humans, Linear Models, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Regression Analysis|
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 Publication||Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.|
|PubMed Central ID||PMC4870165|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R00 ES017744 / ES / NIEHS NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
U01 HG005160 / HG / NHGRI NIH HHS / United States
Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine.