Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls

American Journal of Human Genetics

Volumn 92, Issue 6, 2013, Pages 854-865

  Zipp, Frauke ^a   Ivinson, Adrian J ^a   Haines, Jonathan L ^a   Sawcer, Stephen ^a   Dejager, Philip ^a   Hauser, Stephen L ^a   Oksenberg, Jorge R ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

CD48 ANTIGEN; HLA ANTIGEN; PROTEIN BCL 10; TRANSCRIPTION FACTOR REL; TUMOR NECROSIS FACTOR RECEPTOR ASSOCIATED FACTOR 3;

ARTICLE; CONTROLLED STUDY; GENE EXPRESSION; GENE INTERACTION; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENETIC ASSOCIATION; GENETIC SUSCEPTIBILITY; HUMAN; MAJOR CLINICAL STUDY; META ANALYSIS (TOPIC); MULTIPLE SCLEROSIS; PRIORITY JOURNAL;

CASE-CONTROL STUDIES; EPISTASIS, GENETIC; GENETIC LOCI; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LINKAGE DISEQUILIBRIUM; MODELS, GENETIC; MULTIPLE SCLEROSIS; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEIN INTERACTION MAPS; RISK;

EID: 84878883338     PISSN: 00029297     EISSN: 15376605     Source Type: Journal    
DOI: 10.1016/j.ajhg.2013.04.019     Document Type: Article

References (41)

1. L.A. Hindorff, P. Sethupathy, H.A. Junkins, E.M. Ramos, J.P. Mehta, F.S. Collins, and T.A. Manolio Potential etiologic and functional implications of genome-wide association loci for human diseases and traits Proc. Natl. Acad. Sci. USA 106 2009 9362 9367
2. W. Bodmer, and C. Bonilla Common and rare variants in multifactorial susceptibility to common diseases Nat. Genet. 40 2008 695 701
3. M.A. Eberle, P.C. Ng, K. Kuhn, L. Zhou, D.A. Peiffer, L. Galver, K.A. Viaud-Martinez, C.T. Lawley, K.L. Gunderson, R. Shen, and S.S. Murray Power to detect risk alleles using genome-wide tag SNP panels PLoS Genet. 3 2007 1827 1837
4. S.M. Purcell, N.R. Wray, J.L. Stone, P.M. Visscher, M.C. O'Donovan, P.F. Sullivan, P. Sklar International Schizophrenia Consortium Common polygenic variation contributes to risk of schizophrenia and bipolar disorder Nature 460 2009 748 752
5. W.S. Bush, S.J. Sawcer, P.L. de Jager, J.R. Oksenberg, J.L. McCauley, M.A. Pericak-Vance, J.L. Haines International Multiple Sclerosis Genetics Consortium (IMSGC) Evidence for polygenic susceptibility to multiple sclerosis - the shape of things to come Am. J. Hum. Genet. 86 2010 621 625
6. A. Torkamani, E.J. Topol, and N.J. Schork Pathway analysis of seven common diseases assessed by genome-wide association Genomics 92 2008 265 272
7. L. Wang, P. Jia, R.D. Wolfinger, X. Chen, and Z. Zhao Gene set analysis of genome-wide association studies: methodological issues and perspectives Genomics 98 2011 1 8
8. K. Wang, M. Li, and M. Bucan Pathway-based approaches for analysis of genomewide association studies Am. J. Hum. Genet. 81 2007 1278 1283
9. P. Holmans, E.K. Green, J.S. Pahwa, M.A. Ferreira, S.M. Purcell, P. Sklar, M.J. Owen, M.C. O'Donovan, N. Craddock Wellcome Trust Case-Control Consortium Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder Am. J. Hum. Genet. 85 2009 13 24
10. L.S. Chen, C.M. Hutter, J.D. Potter, Y. Liu, R.L. Prentice, U. Peters, and L. Hsu Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data Am. J. Hum. Genet. 86 2010 860 871
11. I. Lee, U.M. Blom, P.I. Wang, J.E. Shim, and E.M. Marcotte Prioritizing candidate disease genes by network-based boosting of genome-wide association data Genome Res. 21 2011 1109 1121
12. S.E. Baranzini, N.W. Galwey, J. Wang, P. Khankhanian, R. Lindberg, D. Pelletier, W. Wu, B.M. Uitdehaag, L. Kappos, C.H. Polman GeneMSA Consortium Pathway and network-based analysis of genome-wide association studies in multiple sclerosis Hum. Mol. Genet. 18 2009 2078 2090
13. P. Jia, and Z. Zhao Searching joint association signals in CATIE schizophrenia genome-wide association studies through a refined integrative network approach BMC Genomics 13 Suppl 6 2012 S15
14. R.M. Fernández, M. Bleda, R. Núñez-Torres, I. Medina, B. Luzón-Toro, L. García-Alonso, A. Torroglosa, M. Marbà, M.V. Enguix-Riego, and D. Montaner Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease Orphanet J. Rare Dis. 7 2012 103
15. Y.H. Lee, S.C. Bae, S.J. Choi, J.D. Ji, and G.G. Song Genome-wide pathway analysis of genome-wide association studies on systemic lupus erythematosus and rheumatoid arthritis Mol. Biol. Rep. 39 2012 10627 10635
16. M.K. Jensen, T.H. Pers, P. Dworzynski, C.J. Girman, S. Brunak, and E.B. Rimm Protein interaction-based genome-wide analysis of incident coronary heart disease Circ. Cardiovasc. Genet. 4 2011 549 556
17. P. Jia, S. Zheng, J. Long, W. Zheng, and Z. Zhao dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks Bioinformatics 27 2011 95 102
18. E.J. Rossin, K. Lage, S. Raychaudhuri, R.J. Xavier, D. Tatar, Y. Benita, C. Cotsapas, M.J. Daly International Inflammatory Bowel Disease Genetics Constortium Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology PLoS Genet. 7 2011 e1001273
19. A. Compston McAlpine's multiple sclerosis 2005 Churchill Livingstone Elsevier Philadelphia
20. S.L. Hauser, and D.S. Goodin Multiple Sclerosis and other demyelinating diseases D.I. Longo, Harrison's principles of internal medicine 2012 McGraw-Hill New York 3395 3409
21. D.A. Hafler, A. Compston, S. Sawcer, E.S. Lander, M.J. Daly, P.L. De Jager, P.I. de Bakker, S.B. Gabriel, D.B. Mirel, A.J. Ivinson International Multiple Sclerosis Genetics Consortium Risk alleles for multiple sclerosis identified by a genomewide study N. Engl. J. Med. 357 2007 851 862
22. S.E. Baranzini, J. Wang, R.A. Gibson, N. Galwey, Y. Naegelin, F. Barkhof, E.W. Radue, R.L. Lindberg, B.M. Uitdehaag, and M.R. Johnson Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis Hum. Mol. Genet. 18 2009 767 778
23. Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene) Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20 Nat. Genet. 41 2009 824 828
24. E. Jakkula, V. Leppä, A.M. Sulonen, T. Varilo, S. Kallio, A. Kemppinen, S. Purcell, K. Koivisto, P. Tienari, and M.L. Sumelahti Genome-wide association study in a high-risk isolate for multiple sclerosis reveals associated variants in STAT3 gene Am. J. Hum. Genet. 86 2010 285 291
25. S. Sanna, M. Pitzalis, M. Zoledziewska, I. Zara, C. Sidore, R. Murru, M.B. Whalen, F. Busonero, A. Maschio, and G. Costa Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis Nat. Genet. 42 2010 495 497
26. S. Nischwitz, S. Cepok, A. Kroner, C. Wolf, M. Knop, F. Müller-Sarnowski, H. Pfister, D. Roeske, P. Rieckmann, and B. Hemmer Evidence for VAV2 and ZNF433 as susceptibility genes for multiple sclerosis J. Neuroimmunol. 227 2010 162 166
27. M. Comabella, D.W. Craig, M. Camiña-Tato, C. Morcillo, C. Lopez, A. Navarro, J. Rio, X. Montalban, R. Martin BiomarkerMS Study Group Identification of a novel risk locus for multiple sclerosis at 13q31.3 by a pooled genome-wide scan of 500,000 single nucleotide polymorphisms PLoS ONE 3 2008 e3490
28. N.A. Patsopoulos, F. Esposito, J. Reischl, S. Lehr, D. Bauer, J. Heubach, R. Sandbrink, C. Pohl, G. Edan, L. Kappos Bayer Pharma MS Genetics Working Group Steering Committees of Studies Evaluating IFNβ-1b and a CCR1-Antagonist ANZgene Consortium GeneMSA International Multiple Sclerosis Genetics Consortium Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci Ann. Neurol. 70 2011 897 912
29. S. Sawcer, G. Hellenthal, M. Pirinen, C.C. Spencer, N.A. Patsopoulos, L. Moutsianas, A. Dilthey, Z. Su, C. Freeman, S.E. Hunt International Multiple Sclerosis Genetics Consortium Wellcome Trust Case Control Consortium 2 Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis Nature 476 2011 214 219
30. Wellcome Trust Case Control Consortium Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls Nature 447 2007 661 678
31. I.A. Adzhubei, S. Schmidt, L. Peshkin, V.E. Ramensky, A. Gerasimova, P. Bork, A.S. Kondrashov, and S.R. Sunyaev A method and server for predicting damaging missense mutations Nat. Methods 7 2010 248 249
32. J.Z. Liu, A.F. McRae, D.R. Nyholt, S.E. Medland, N.R. Wray, K.M. Brown, N.K. Hayward, G.W. Montgomery, P.M. Visscher, N.G. Martin, S. Macgregor AMFS Investigators A versatile gene-based test for genome-wide association studies Am. J. Hum. Genet. 87 2010 139 145
33. T. Ideker, O. Ozier, B. Schwikowski, and A.F. Siegel Discovering regulatory and signalling circuits in molecular interaction networks Bioinformatics 18 Suppl 1 2002 S233 S240
34. G. Dennis Jr., B.T. Sherman, D.A. Hosack, J. Yang, W. Gao, H.C. Lane, and R.A. Lempicki DAVID: Database for Annotation, Visualization, and Integrated Discovery Genome Biol. 4 2003 3
35. Y. Benita, Z. Cao, C. Giallourakis, C. Li, A. Gardet, and R.J. Xavier Gene enrichment profiles reveal T-cell development, differentiation, and lineage-specific transcription factors including ZBTB25 as a novel NF-AT repressor Blood 115 2010 5376 5384
36. P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker Cytoscape: a software environment for integrated models of biomolecular interaction networks Genome Res. 13 2003 2498 2504
37. M. Oti, B. Snel, M.A. Huynen, and H.G. Brunner Predicting disease genes using protein-protein interactions J. Med. Genet. 43 2006 691 698
38. S. Sawcer The complex genetics of multiple sclerosis: pitfalls and prospects Brain 131 2008 3118 3131
39. H.K. Tabor, N.J. Risch, and R.M. Myers Candidate-gene approaches for studying complex genetic traits: practical considerations Nat. Rev. Genet. 3 2002 391 397
40. M.A. Hauser, Y.J. Li, S. Takeuchi, R. Walters, M. Noureddine, M. Maready, T. Darden, C. Hulette, E. Martin, and E. Hauser Genomic convergence: identifying candidate genes for Parkinson's disease by combining serial analysis of gene expression and genetic linkage Hum. Mol. Genet. 12 2003 671 677
41. M. Ban, J.L. McCauley, R. Zuvich, A. Baker, L. Bergamaschi, M. Cox, A. Kemppinen, S. D'Alfonso, F.R. Guerini, and J. Lechner-Scott A non-synonymous SNP within membrane metalloendopeptidase-like 1 (MMEL1) is associated with multiple sclerosis Genes Immun. 11 2010 660 664