Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

ABSTRACT: The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included

SUBMITTER: Friedrichs S 

PROVIDER: S-EPMC5530424 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature