An Adaptive Genetic Association Test Using Double Kernel Machines.

ABSTRACT: Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares ker

SUBMITTER: Zhan X 

PROVIDER: S-EPMC4666603 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature