A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

ABSTRACT: Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nui

SUBMITTER: Marceau R 

PROVIDER: S-EPMC4544636 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature