A network-based kernel machine test for the identification of risk pathways in genome-wide association studies.
Saskia Freytag, Juliane Manitz, Martin Schlather, Thomas Kneib, Christopher I Amos, Angela Risch, Jenny Chang-Claude, Joachim Heinrich, Heike Bickeböller
Hum Hered | Published : 2013
Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are cr..View full abstract
Awarded by NCI NIH HHS
Awarded by NIAMS NIH HHS