| kangar00-package {kangar00} | R Documentation |
kangar00 package
Description
This package includes methods to extract information on pathways, genes and SNPs from online databases and to evaluate these data using the logistic kernel machine test (LKMT) (Liu et al. 2008).
We defined SNP sets representing genes and whole pathways using knowledge on gene membership and interaction from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa et al. 2014). SNPs are mapped to genes via base pair positions of SNPs and transcript start and end points of genes as documented in the Ensemble database (Cunningham et al. 2015).
In the LKMT, we employed the linear kernel (Wu et al. 2010) as well as two more advanced kernels, adjusting for size bias in the number of SNPs and genes in a pathway (size-adjusted kernels), and incorporating the network structure of genes within the pathway (pathway kernels), respectively (Freytag et al. 2012, 2014). P-values are derived in a variance component test using a moment matching method (Schaid, 2010) or Davies' algorithm (Davies, 1980).
Details
| Package: | kangar00 |
| Version: | 1.1 |
| Date: | 2017-08-07 |
| License: | GPL-2 |
Author(s)
Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut],
Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb],
Heike Bickeboeller [ctb]
Maintainer: Juliane Manitz <r@manitz.de>
References
Cunningham F, Ridwan Amode M, Barrell D, et al. Ensembl 2015. Nucleic Acids Research 2015 43 Database issue:D662-D669
Davies R: Algorithm as 155: the distribution of a linear combination of chi-2 random variables. J R Stat Soc Ser C 1980, 29:323-333.
Freytag S, Bickeboeller H, Amos CI, Kneib T, Schlather M: A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis. Hum Hered. 2012, 74(2):97-108.
Freytag S, Manitz J, Schlather M, Kneib T, Amos CI, Risch A, Chang-Claude J, Heinrich J, Bickeboeller H: A network-based kernel machine test for the identification of risk pathways in genome-wide association studies. Hum Hered. 2013, 76(2):64-75.
Friedrichs S, Manitz J, Burger P, Amos CI, Risch A, Chang-Claude JC, Wichmann HE, Kneib T, Bickeboeller H, Hofner B: 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.
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199-D205 (2014).
Liu D, Ghosh D, Lin X. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC Bioinformatics. 2008 9:292.
Schaid DJ: Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations. Hum Hered 2010, 70:109-131.
Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X: Powerful SNP-Set Analysis for Case-Control Genome-Wide Association Studies. Am J Hum Genet 2010, 86:929-42