Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models.

ABSTRACT: We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using

SUBMITTER: Liu D 

PROVIDER: S-EPMC2665800 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature