Network statistics of genetically-driven gene co-expression modules in mouse crosses

Frontiers in Genetics

Volumn 4, Issue DEC, 2013, Pages

  Scott Boyer, Marie Pier ^a   Haibe Kains, Benjamin ^a   Deschepper, Christian F ^a  

^a CLIN RESEARCH INSTITUTE OF MONTREAL   (Canada)

Author keywords

Chromosome domain; Gene co expression modules; Genetics; Mouse recombinant inbred strains; Network inference

Indexed keywords

EID: 84892411742     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2013.00291     Document Type: Article

References (22)

1. Broman, K. W., Wu, H., Sen, S., and Churchill, G. A. (2003). R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889-890. doi: 10.1093/bioinformatics/btg112.
2. Califano, A., Butte, A. J., Friend, S., Ideker, T., and Schadt, E. (2012). Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat. Genet. 44, 841-847. doi: 10.1038/ng.2355.
3. Carro, M. S., Lim, W. K., Alvarez, M. J., Bollo, R. J., Zhao, X., Snyder, E. Y., et al. (2010). The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318-325. doi: 10.1038/nature08712.
4. Cho, D.-Y., Kim, Y.-A., and Przytycka, T. M. (2012). Chapter 5: network biology approach to complex diseases. PLoS Comput. Biol. 8:e1002820. doi: 10.1371/journal.pcbi.1002820.
5. Davis, R. C., Nas, A., van Castellani, L. W., Zhao, Y., Zhou, Z., Wen, P., et al. (2012). Systems genetics of susceptibility to obesity-induced diabetes in mice. Physiol. Genomics 44, 1-13. doi: 10.1152/physiolgenomics.00003.2011.
6. Dong, J., and Horvath, S. (2007). Understanding network concepts in modules. BMC Syst. Biol. 1:24. doi: 10.1186/1752-0509-1-24.
7. Farber, C. R. (2013). Systems-level analysis of genome-wide association data. G3 3, 119-129. doi: 10.1534/g3.112.004788.
8. Gargalovic, P. S., Imura, M., Zhang, B., Gharavi, N. M., Clark, M. J., Pagnon, J., et al. (2006). Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proc. Natl. Acad. Sci. U.S.A. 103, 12741-12746. doi: 10.1073/pnas.0605457103.
9. Lander, E., and Kruglyak, L. (1995). Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet. 11, 241-247. doi: 10.1038/ng1195-241.
10. Langfelder, P., and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559. doi: 10.1186/1471-2105-9-559.
11. Leduc, M. S., Blair, R. H., Verdugo, R. A., Tsaih, S.-W., Walsh, K., Churchill, G. A., et al. (2012). Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ × SM/J intercross. J. Lipid Res. 53, 1163-1175. doi: 10.1194/jlr.M025833.
12. Plomin, R., Haworth, C. M. A., and Davis, O. S. P. (2009). Common disorders are quantitative traits. Nat. Rev. Genet. 10, 872-878. doi: 10.1038/nrg2670.
13. Rhinn, H., Fujita, R., Qiang, L., Cheng, R., Lee, J. H., and Abeliovich, A. (2013). Integrative genomics identifies APOE ε 4 effectors in Alzheimer's disease. Nature 500, 45-50. doi: 10.1038/nature12415.
14. Schaefer, J., Opgen-Rhein, R., and Strimmer, K. (2006). Reverse engineering genetic networks using the genenet package. R News 6/5, 50-53. Available online at: http://uni-leipzig.de/strimmer/lab/publications/misc/GeneNet-Rnews2006.pdf.
15. Schröder, M. S., Culhane, A. C., Quackenbush, J., and Haibe-Kains, B. (2011). survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206-3208. doi: 10.1093/bioinformatics/btr511.
16. Scott-Boyer, M.-P., and Deschepper, C. F. (2013). Genome-wide detection of gene coexpression domains showing linkage to regions enriched with polymorphic retrotransposons in recombinant inbred mouse strains. G3 3, 597-605. doi: 10.1534/g3.113.005546.
17. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504. doi: 10.1101/gr.1239303.
18. Weiss, J. N., Karma, A., MacLellan, W. R., Deng, M., Rau, C. D., Rees, C. M., et al. (2012). "Good enough solutions" and the genetics of complex diseases. Circ. Res. 111, 493-504. doi: 10.1161/CIRCRESAHA.112.269084.
19. Whitlock, M. C. (2005). Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J. Evol. Biol. 18, 1368-1373. doi: 10.1111/j.1420-9101.2005.00917.x.
20. Woo, Y. H., Walker, M., and Churchill, G. A. (2010). Coordinated expression domains in mammalian genomes. PLoS ONE 5:e12158. doi: 10.1371/journal.pone.0012158.
21. Yang, X., Deignan, J. L., Qi, H., Zhu, J., Qian, S., Zhong, J., et al. (2009). Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat. Genet. 41, 415-423. doi: 10.1038/ng.325.
22. Zhang, B., and Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4:17. doi: 10.2202/1544-6115.1128.