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Relevance Epistasis Network of Gastritis for Intra-chromosomes in the Korea Associated Resource (KARE) Cohort Study

  • Jeong, Hyun-hwan (Department of Information and Computer Engineering, Ajou University) ;
  • Sohn, Kyung-Ah (Department of Information and Computer Engineering, Ajou University)
  • Received : 2014.07.08
  • Accepted : 2014.11.11
  • Published : 2014.12.31

Abstract

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

Keywords

References

  1. Park KS. How much amount of socioeconomic loss is caused by digestive diseases? Korean J Gastroenterol 2011;58:297-299. https://doi.org/10.4166/kjg.2011.58.6.297
  2. Corvalan AH, Carrasco G, Saavedra K. The genetic and epigenetic bases of gastritis. In: Current Topics in Gastritis (Mozsik G, ed.). Rijeka: InTech, 2013. pp. 79-95.
  3. Marshall BJ, Warren JR. Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet 1984;1:1311-1315.
  4. Lee HW, Hahm KB, Lee JS, Ju YS, Lee KM, Lee KW. Association of the human leukocyte antigen class II alleles with chronic atrophic gastritis and gastric carcinoma in Koreans. J Dig Dis 2009;10:265-271. https://doi.org/10.1111/j.1751-2980.2009.00395.x
  5. Yuzhalin A. The role of interleukin DNA polymorphisms in gastric cancer. Hum Immunol 2011;72:1128-1136. https://doi.org/10.1016/j.humimm.2011.08.003
  6. Zendehdel K, Bahmanyar S, McCarthy S, Nyren O, Andersson B, Ye W. Genetic polymorphisms of glutathione S-transferase genes GSTP1, GSTM1, and GSTT1 and risk of esophageal and gastric cardia cancers. Cancer Causes Control 2009;20:2031-2038. https://doi.org/10.1007/s10552-009-9399-7
  7. Xue H, Liu J, Lin B, Wang Z, Sun J, Huang G. A meta-analysis of interleukin-8 -251 promoter polymorphism associated with gastric cancer risk. PLoS One 2012;7:e28083. https://doi.org/10.1371/journal.pone.0028083
  8. de Oliveira JG, Silva AE. Polymorphisms of the TLR2 and TLR4 genes are associated with risk of gastric cancer in a Brazilian population. World J Gastroenterol 2012;18:1235-1242. https://doi.org/10.3748/wjg.v18.i11.1235
  9. Coussens LM, Werb Z. Inflammation and cancer. Nature 2002;420:860-867. https://doi.org/10.1038/nature01322
  10. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534. https://doi.org/10.1038/ng.357
  11. Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000:418-429.
  12. Leem S, Jeong HH, Lee J, Wee K, Sohn KA. Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure. Comput Biol Chem 2014;50:19-28. https://doi.org/10.1016/j.compbiolchem.2014.01.005
  13. Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH. Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics 2011;12:364. https://doi.org/10.1186/1471-2105-12-364
  14. Goebel B, Dawy Z, Hagenauer J, Mueller JC. An approximation to the distribution of finite sample size mutual information estimates. In: 2005 IEEE International Conference on Communications, 2005 May 16-20, Seoul. Vol. 2. Seoul: ICC 2005, 2005. pp. 1102-1106.
  15. Hong KW, Kim SS, Kim Y. Genome-wide association study of orthostatic hypotension and supine-standing blood pressure changes in two korean populations. Genomics Inform 2013;11:129-134. https://doi.org/10.5808/GI.2013.11.3.129
  16. Lim JE, Oh B. Allelic frequencies of 20 visible phenotype variants in the korean population. Genomics Inform 2013;11:93-96. https://doi.org/10.5808/GI.2013.11.2.93
  17. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575. https://doi.org/10.1086/519795
  18. Liang KC, Wang X. Gene regulatory network reconstruction using conditional mutual information. EURASIP J Bioinform Syst Biol 2008:253894.
  19. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006;7 Suppl 1:S7.
  20. Culverhouse R, Suarez BK, Lin J, Reich T. A perspective on epistasis: limits of models displaying no main effect. Am J Hum Genet 2002;70:461-471. https://doi.org/10.1086/338759
  21. Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, et al. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007;31:306-315. https://doi.org/10.1002/gepi.20211
  22. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007;2:2366-2382. https://doi.org/10.1038/nprot.2007.324
  23. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
  24. Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 1988;75:800-802. https://doi.org/10.1093/biomet/75.4.800
  25. Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, et al. Using graph theory to analyze biological networks. BioData Min 2011;4:10. https://doi.org/10.1186/1756-0381-4-10
  26. Uchino S, Tsuda H, Noguchi M, Yokota J, Terada M, Saito T, et al. Frequent loss of heterozygosity at the DCC locus in gastric cancer. Cancer Res 1992;52:3099-3102.
  27. Sun J, Zhao Z. A comparative study of cancer proteins in the human protein-protein interaction network. BMC Genomics 2010;11 Suppl 3:S5.
  28. Liu Z, Zhang J, Gao Y, Pei L, Zhou J, Gu L, et al. Large-scale characterization of DNA methylation changes in human gastric carcinomas with and without metastasis. Clin Cancer Res 2014;20:4598-4612. https://doi.org/10.1158/1078-0432.CCR-13-3380
  29. Taniuchi T, Mortensen ER, Ferguson A, Greenson J, Merchant JL. Overexpression of ZBP-89, a zinc finger DNA binding protein, in gastric cancer. Biochem Biophys Res Commun 1997;233:154-160. https://doi.org/10.1006/bbrc.1997.6310
  30. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 2014;42:D199-D205. https://doi.org/10.1093/nar/gkt1076

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