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

 bmc bioinformatics. 2008 jun 24;9(1):292. [epub ahead of print]

abstract: 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. conclusions: we show that kernel machine estimation of the model components can be formulated using a logistic mixed model. estimation hence can proceed within a mixed model framework using standard statistical software. a score test based on a gaussian process approximation is developed to test for the genetic pathway effect. the methods are illustrated using a prostate cancer data set and evaluated using simulations. an extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.大连大学附属新华医院手足外科刘铎