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Methods to extract information on pathways, genes and SNPs from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network based kernel.


To cite the package kangar00 itself use:

  • J. Manitz, S. Friedrichs, P. Burger, B. Hofner, N.T. Ha, S. Freytag, H. Bickeboeller (2017). kangar00: Kernel Approaches for Nonlinear Genetic Association Regression, R package version 1.0,

The size-adjusted kernel function is introduced in:

  • S. Freytag, H. Bickeboeller, C.I. Amos, T. Kneib, M. Schlather (2012). A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis. Human Heredity, 74, 97-108.

The network-based kernel function is introduced in:

  • S. Freytag, J. Manitz, M. Schlather, T. Kneib, C.I. Amos, A. Risch, J. Chang-Claude, J. Heinrich, H. Bickeboeller (2013). A Network-Based Kernel Machine Test for the Identifcation of Risk Pathways in Genome-Wide Association Studies. Human Heredity, 76, 64-75.

The kernel boosting method is introduced in:

  • S. Friedrichs, J. Manitz, P. Burger, C.I. Amos, A. Risch, J.C. Chang-Claude, H.E. Wichmann, T. Kneib, H. Bickeboeller, and B. Hofner (2017). Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Computational and Mathematical Methods in Medicine, 2017(6742763), 1-17. doi:10.1155/2017/6742763.

Use toBibtex(citation("kangar00")) in R to extract BibTeX references.