Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via log

Background: Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect. Results: In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Dawei Liu, Debashis Ghosh, Xihong Lin
• Keywords: BMCBI 2008, Genetic Pathway, Kernel Machine, Pathway Effects