A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

ABSTRACT:

Objectives

The logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of case-control genome-wide association studies results from its computational efficiency and flexibility in incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately, its form strongly influences the test's power and bias. Most authors have recommended the use of a simple linear kernel. We demonstrate via a simulation that the probability of rejecting the null hypothesis of no association just by chance increases with the number of SNPs or genes in the pathway when applying a simple linear kernel.

Methods

We propose a novel kernel that includes an appropriate standardi

SUBMITTER: Freytag S 

PROVIDER: S-EPMC3779069 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature