SigMod: An exact and efficient method to identify a strongly interconnected diseaseassociated module in a gene network

Bioinformatics

Volumn 33, Issue 10, 2017, Pages 1536-1544

  Liu, Yuanlong ^a,b   Brossard, Myriam ^a,b   Roqueiro, Damian ^c   Margaritte Jeannin, Patricia ^a,b   Sarnowski, Chloé ^a,b   Bouzigon, Emmanuelle ^a,b   Demenais, Florence ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ PARIS DIDEROT   (France)

^c ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ASTHMA; BIOLOGY; GENE REGULATORY NETWORK; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; SOFTWARE;

ALGORITHMS; ASTHMA; COMPUTATIONAL BIOLOGY; GENE REGULATORY NETWORKS; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 85020308247     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx004     Document Type: Article

References (37)

1. Adamcsek, B. et al. (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics, 22, 1021-1023.
2. Azencott, C. A. et al. (2013) Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29, i171-i179.
3. Brossard, M. et al. (2013) Comparison of permutations strategies to assess gene-set significance in gene-set-enrichment analysis. Abstracts from the 22nd Annual Meeting of the International Genetic Epidemiology Society, Page 15.
4. Cabrera, C. P. et al. (2012) Uncovering networks from genome-wide association studies via circular genomic permutation. G3: Genes j Genomes j Genetics, 2, 1067-1075.
5. Cabusora, L. et al. (2005) Differential network expression during drug and stress response. Bioinformatics, 21, 2898-2905.
6. Çalişkan, M. et al. (2013) Rhinovirus wheezing illness and genetic risk of childhood-onset asthma. New Engl. J. Med., 368, 1398-1407.
7. Csardi, G., and Nepusz, T. (2006) The igraph software package for complex network research. InterJournal Complex Syst., 1695, 1-9.
8. Eichler, E. E. et al. (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet., 11, 446-450.
9. Farh, K. K. H. et al. (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature, 518, 337-343.
10. Gallo, G. et al. (1989) A fast parametric maximum flow algorithm and applications. SIAM J. Comput., 18, 30-55.
11. Goldberg, A. V., and Tarjan, R. E. (1988) A new approach to the maximumflow problem. J. ACM (JACM), 35, 921-940.
12. Huang, D. W. et al. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols, 4, 44-57.
13. Ideker, T. et al. (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl 1), S233-S240.
14. Jia, P. et al. (2011) dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics, 27, 95-102.
15. Jia, P. and Zhao, Z. (2014) Network-Assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum. Genet., 133, 125-138.
16. Klein, R. J. et al. (2005) Complement factor H polymorphism in age-related macular degeneration. Science, 308, 385-389.
17. Lamparter, D. et al. (2016) Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol., 12, e1004714.
18. Lee, I. et al. (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res., 21, 1109-1121.
19. Lee, Y. et al. (2013) Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. J. Am. Med. Informat. Assoc., 20, 619-629.
20. Li, M. X. et al. (2011) GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet., 88, 283-293.
21. Li, Z. C. et al. (2015) Identification of drug-target interaction from interactome network with "guilt-by-Association" principle and topology features. Bioinformatics, 32, 1057-1064.
22. Liu, J. Z. et al. (2010) A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet., 87, 139-145.
23. Maher, B. (2008) Personal genomes: The case of the missing heritability. Nature, 456, 18-21.
24. McLean, C. Y. et al. (2010) GREAT improves functional interpretation of cisregulatory regions. Nat. Biotechnol., 28, 495-501.
25. Moffatt, M. F. et al. (2010) A large-scale, consortium-based genomewide association study of asthma. New Engl. J. Med., 363, 1211-1221.
26. Oliver, S. (2000) Proteomics: guilt-by-Association goes global. Nature, 403, 601-603.
27. Rajagopalan, D., and Agarwal, P. (2005) Inferring pathways fromgene lists using a literature-derived network of biological relationships. Bioinformatics, 21, 788-793.
28. Raychaudhuri, S. et al. (2009) Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet., 5, e1000534.
29. Smit, L. et al. (2010) 17q21 variants modify the association between early respiratory infections and asthma. Eur. Respir. J., 36, 57-64.
30. Subramanian, A. et al. (2005) Gene set enrichment analysis: a knowledgebased approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545-15550.
31. Szklarczyk, D. et al. (2014) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res., page gku1003.
32. Taşan, M. et al. (2015) Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods, 12, 154-159.
33. Tomizono, S. (2013). elliplot: Ellipse Summary Plot of Quantiles. R package version 1.1.1.
34. Welter, D. et al. (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res., 42, D1001-D1006.
35. Wolfe, C. J. et al. (2005) Systematic survey reveals general applicability of "guilt-by-Association" within gene coexpression networks. BMC Bioinformatics, 6, 227.
36. Xiong, W. et al. (2014) The centrality of cancer proteins in human proteinprotein interaction network: a revisit. Int. J. Computat. Biol. Drug Design, 7, 146-156.
37. Yu, G. et al. (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. Omics, 16, 284-287.