A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis

摘要

ObjectivesThe logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of GWA case-control studies results from its computational efficiency and flexibility of incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately its form strongly influences the power and bias. Most authors have recommended the use of the 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 this kernel.