Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Directed Causal Network Construction Using Linkage Analysis with Metabolic Syndrome-Related Expression Quantitative Traits

  • Kim, Kyee-Zu (Graduate School of Public Health, Seoul National University) ;
  • Min, Jin-Young (Institute of Health & Environment, Seoul National University) ;
  • Kwon, Geun-Yong (Division of Epidemic Intelligence Service, Korea Centers for Disease Control and Prevention) ;
  • Sung, Joo-Hon (Graduate School of Public Health, Seoul National University) ;
  • Cho, Sung-Il (Graduate School of Public Health, Seoul National University)
  • Received : 2011.11.15
  • Accepted : 2011.11.30
  • Published : 2011.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we propose a novel, intuitive method of constructing an expression quantitative trait (eQT) network that is related to the metabolic syndrome using LOD scores and peak loci for selected eQTs, based on the concept of gene-gene interactions. We selected 49 eQTs that were related to insulin resistance. A variance component linkage analysis was performed to explore the expression loci of each of the eQTs. The linkage peak loci were investigated, and the "support zone" was defined within boundaries of an LOD score of 0.5 from the peak. If one gene was located within the "support zone" of the peak loci for the eQT of another gene, the relationship was considered as a potential "directed causal pathway" from the former to the latter gene. SNP markers under the linkage peaks or within the support zone were searched for in the database to identify the genes at the loci. Two groups of gene networks were formed separately around the genes IRS2 and UGCGL2. The findings indicated evidence of networks between genes that were related to the metabolic syndrome. The use of linkage analysis enabled the construction of directed causal networks. This methodology showed that characterizing and locating eQTs can provide an effective means of constructing a genetic network.

Keywords

References

  1. Arnold, S.M. and Kaufman, R.J. (2003). The noncatalytic portion of human UDP-glucose, glycoprotein glucosyltrans-ferase 1 confers UDP-glucose binding and transferase function to the catalytic domain. J. Biol. Chem. 278, 43320-43328. https://doi.org/10.1074/jbc.M305800200
  2. Avery, C.L., Freedman, B.I., Heiss, G., Kraja, A., Rice, T., Arnett, D., Miller, M.B., Pankow, J.S., Lewis, C.E., Myers, R.H., Hunt, S.C., Almasy, L., North, K.E., and Hypertension Genetic Epidemiology Network. (2004). Linkage analysis of diabetes status among hypertensive families, the Hypertension Genetic Epidemiology Network study. Diabetes 53, 3307-3312. https://doi.org/10.2337/diabetes.53.12.3307
  3. Bonnet, F., Patel, S., Laville, M., Balkau, B., Favuzzi, A., Monti, L.D., Lalic, N., and Walker, M. (2008). Influence of the ACE gene insertion/deletion polymorphism on insulin sensitivity and impaired glucose tolerance in healthy subjects. Diabetes Care 31, 789-794. https://doi.org/10.2337/dc07-1788
  4. Bouzakri, K., Zachrisson, A., Al-Khalili, L., Zhang, B.B., Koistinen, H.A., Krook, A., and Zierath, J.R. (2006). siRNAbased gene silencing reveals specialized roles of IRS-1/Akt2 and IRS-2/Akt1 in glucose and lipid metabolism in human skeletal muscle. Cell Metab. 4, 89-96. https://doi.org/10.1016/j.cmet.2006.04.008
  5. Cheung, V.G. and Spielman, R.S. (2007). Data for Genetic Analysis Workshop (GAW) 15, Problem 1, genetics of gene expression variation in humans. BMC Proc. 1 Suppl 1, S2. https://doi.org/10.1186/1753-6561-1-s1-s2
  6. Comuzzie, A.G., Funahashi, T., Sonnenberg, G., Martin, L.J., Jacob, H.J., Black, A.E., Maas, D., Takahashi, M., Kihara, S., Tanaka, S., Matsuzawa, Y., Blangero, J., Cohen, D., and Kissebah, A. (2001). The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome. J. Clin. Endocrinol. Metab. 86, 4321-4325. https://doi.org/10.1210/jc.86.9.4321
  7. de Jong, H. (2002). Modeling and simulation of genetic regulatory systems, a literature review. J. Comput. Biol. 9, 67-103. https://doi.org/10.1089/10665270252833208
  8. Foufelle, F. and Ferre, P. (2002). New perspectives in the regulation of hepatic glycolytic and lipogenic genes by insulin and glucose, a role for the transcription factor sterol regulatory element binding protein-1c. Biochem. J. 366, 377-391. https://doi.org/10.1042/BJ20020430
  9. Franke, L., van Bakel, H., Fokkens, L., de Jong, E.D., Egmont-Petersen, M., and Wijmenga, C. (2006). Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 1011-1025. https://doi.org/10.1086/504300
  10. Groop, L. (2000). Genetics of the metabolic syndrome. Br. J. Nutr. 83 Suppl 1, S39-48.
  11. Hasty, J., McMillen, D., Isaacs, F., and Collins, J.J. (2001). Computational studies of gene regulatory networks, in numero molecular biology. Nat. Rev. Genet. 2, 268-279.
  12. Horenstein, R.B. and Shuldiner, A.R. (2004). Genetics of diabetes. Rev. Endocr. Metab. Disord. 5, 25-36. https://doi.org/10.1023/B:REMD.0000016122.84105.75
  13. Kantartzis, K., Peter, A., Machicao, F., Machann, J., Wagner, S., Konigsrainer, I., Konigsrainer, A., Schick, F., Fritsche, A., Haring, H.U., and Stefan, N. (2009). Dissociation between Fatty Liver and Insulin Resistance in Humans carrying a Variant of the Patatin-like Phospholipase 3 Gene. Diabetes. 58, 2616-2623. https://doi.org/10.2337/db09-0279
  14. Kelley, K.M., Oh, Y., Gargosky, S.E., Gucev, Z., Matsumoto, T., Hwa, V., Ng, L., Simpson, D.M., and Rosenfeld, R.G. (1996). Insulin-like growth factor-binding proteins (IGFBPs) and their regulatory dynamics. Int. J. Biochem. Cell Biol. 28, 619-637. https://doi.org/10.1016/1357-2725(96)00005-2
  15. Kershaw, E.E and Flier, J.S, (2004). Adipose Tissue as an Endocrine Organ. J. Clin. Endocrinol. Metab. 89, 2548-2556. https://doi.org/10.1210/jc.2004-0395
  16. Kopf, D., Cheng, L.S., Blandau, P., Hsueh, W., Raffel, L.J., Buchanan, T.A., Xiang, A.H., Davis, R.C., Rotter, J.I., and Lehnert, H. (2008). Association of insulin sensitivity and glucose tolerance with the c.825C>T variant of the G protein beta-3 subunit gene. J. Diabetes Complicat. 22, 205-209. https://doi.org/10.1016/j.jdiacomp.2006.12.005
  17. Kuiperij, H. (2004). Chapter 4; Expression profiling of cAMPregulated genes via MAPK-dependent and -independent pathways. Novel cAMP Targets in Cell Proliferation pp. 53-80.
  18. Kulp, D.C. and Jagalur, M. (2006). Causal inference of regulator- target pairs by gene mapping of expression phenotypes. BMC Genomics 7, 125. https://doi.org/10.1186/1471-2164-7-125
  19. Lindgren, C.M., Mahtani, M.M., Widen, E., McCarthy, M.I., Daly, M.J., Kirby, A., Reeve, M.P., Kruglyak, L., Parker, A., Meyer, J. Almgren, P., Lehto, M., Kanninen, T., Tuomi, T., Groop, L.C., and Lander, E.S. (2002). Genomewide search for type 2 diabetes mellitus susceptibility loci in Finnish families, the Botnia study. Am. J. Hum. Genet. 70, 509-516. https://doi.org/10.1086/338629
  20. Ma, L., Hanson, R.L., Que, L.N., Cali, A.M., Fu, M., Mack, J.L., Infante, A.M., Kobes, S., Bogardus, C., Shuldiner, A.R. and Baier, L.J. (2007). Variants in ARHGEF11, a candidate gene for the linkage to type 2 diabetes on chromosome 1q, are nominally associated with insulin resistance and type 2 diabetes in Pima Indians. Diabetes 56, 1454-1459. https://doi.org/10.2337/db06-0640
  21. Markou, M. and Singh, S. (2003a). Novelty detection, a review- part 1, statistical approaches. Singnal Processing 83, 2481-2497. https://doi.org/10.1016/j.sigpro.2003.07.018
  22. Markou, M. and Singh, S. (2003b). Novelty detection, a review- part 2, neural network based approaches. Singnal Processing 83, 23.
  23. Matise, T.C., Chen, F., Chen, W., De La Vega, F.M., Hansen, M., He, C., Hyland, F.C., Kennedy, G.C., Kong, X., Murray, S.S. Ziegle, J.S., Stewart, W.C., and Buyske, S. (2007). A second-generation combined linkage physical map of the human genome. Genome Res. 17, 1783-1786. https://doi.org/10.1101/gr.7156307
  24. Mercado, M.M., McLenithan, J.C., Silver, K.D., and Shuldiner, A.R. (2002). Genetics of insulin resistance. Curr. Diab. Rep. 2, 83-95. https://doi.org/10.1007/s11892-002-0063-9
  25. Morley, M., Molony, C.M., Weber, T.M., Devlin, J.L., Ewens, K.G., Spielman, R.S., and Cheung, V.G. (2004). Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743-747. https://doi.org/10.1038/nature02797
  26. Mussig, K., Staiger, H., Machicao, F., Kirchhoff, K., Guthoff, M., Schafer, S.A., Kantartzis, K., Silbernagel, G., Stefan, N., Holst, J.J. Gallwitz, B., Haring, H.U., and Fritsche, A. (2009). Association of type 2 diabetes candidate polymorphisms in KCNQ1 with incretin and insulin secretion. Diabetes 58, 1715-1720. https://doi.org/10.2337/db08-1589
  27. Pietzsch, J., Julius, U., Nitzsche, S., and Hanefeld, M. (1998). In vivo evidence for increased apolipoprotein A-I catabolism in subjects with impaired glucose tolerance. Diabetes 47, 1928-1934. https://doi.org/10.2337/diabetes.47.12.1928
  28. Previs, S.F., Withers, D.J., Ren, J.M., White, M.F., and Shulman, G.I. (2000). Contrasting effects of IRS-1 versus IRS-2 gene disruption on carbohydrate and lipid metabolism in vivo. J. Biol. Chem. 275, 38990-38994. https://doi.org/10.1074/jbc.M006490200
  29. Reaven, G. (2002). Metabolic syndrome, pathophysiology and implications for management of cardiovascular disease. Circulation 106, 286-288. https://doi.org/10.1161/01.CIR.0000019884.36724.D9
  30. Reaven, G.M. (1988). Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37, 1595-1607. https://doi.org/10.2337/diabetes.37.12.1595
  31. Rockman, M.V. and Kruglyak, L. (2006). Genetics of global gene expression. Nat. Rev. Genet. 7, 862-872. https://doi.org/10.1038/nrg1964
  32. Ronti, T, Lupattelli, G, and Mannarino, E. (2006). The endocrine function of adipose tissue, an update. Clin. Endocrinol. 64, 4, 355-365.
  33. Rui, L., Fisher, T.L., Thomas, J., and White, M.F. (2001). Regulation of insulin/insulin-like growth factor-1 signaling by proteasome-mediated degradation of insulin receptor substrate-2. J. Biol. Chem. 276, 40362-40367. https://doi.org/10.1074/jbc.M105332200
  34. Sale, M.M., Woods, J., and Freedman, B.I. (2006). Genetic determinants of the metabolic syndrome. Curr. Hypertens. Rep. 8, 16-22. https://doi.org/10.1007/s11906-006-0036-5
  35. Saltiel, A.R. and Kahn, C.R. (2001). Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799-806. https://doi.org/10.1038/414799a
  36. Schadt, E.E., Lamb, J., Yang, X., Zhu, J., Edwards, S., Guhathakurta, D., Sieberts, S.K., Monks, S., Reitman, M., Zhang, C., Lum, P.Y., Leonardson, A., Thieringer, R., Metzger, J.M., Yang, L., Castle, J., Zhu, H., Kash, S.F., Drake, T.A., Sachs, A., and Lusis, A.J. (2005). An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710-717. https://doi.org/10.1038/ng1589
  37. Schadt, E.E. and Lum, P.Y. (2006). Thematic review series, systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47, 2601-2613. https://doi.org/10.1194/jlr.R600026-JLR200
  38. Schulingkamp, R.J., Pagano, T.C., Hung, D., and Raffa, R.B. (2000). Insulin receptors and insulin action in the brain, review and clinical implications. Neurosci. Biobehav. Rev. 24, 855-872. https://doi.org/10.1016/S0149-7634(00)00040-3
  39. Schwartz, M.W., Baskin, D.G., Bukowski, T.R., Kuijper, J.L., Foster, D., Lasser, G., Prunkard, D.E., Porte, D., Jr., Woods, S.C., Seeley, R.J. and Weigle, D.S. (1996). Specificity of leptin action on elevated blood glucose levels and hypothalamic neuropeptide Y gene expression in ob/ob mice. Diabetes 45, 531-535. https://doi.org/10.2337/diabetes.45.4.531
  40. Stern, M.P. (2003). Genetics of insulin resistance syndrome. Endocr. Pract. 9 Suppl 2, 35-38. https://doi.org/10.4158/EP.9.S2.35
  41. Tu, Z., Wang, L., Arbeitman, M.N., Chen, T., and Sun, F. (2006). An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics 22, e489-496. https://doi.org/10.1093/bioinformatics/btl234
  42. Vasseur, F., Meyre, D., and Froguel, P. (2006). Adiponectin, type 2 diabetes and the metabolic syndrome, lessons from human genetic studies. Expert. Rev. Mol. Med. 8, 1-12.
  43. Vohradsky, J. (2001). Neural model of the genetic network. J. Biol. Chem. 276, 36168-36173. https://doi.org/10.1074/jbc.M104391200
  44. Wang, H., Zhang, H., Jia, Y., Zhang, Z., Craig, R., Wang, X., and Elbein, S.C. (2004). Adiponectin receptor 1 gene (ADIPOR1) as a candidate for type 2 diabetes and insulin resistance. Diabetes 53, 2132-2136. https://doi.org/10.2337/diabetes.53.8.2132
  45. Wessels, L.F., van Someren, E.P., and Reinders, M.J. (2001). A comparison of genetic network models. Pac. Symp. Biocomput. 508-519.
  46. Yang, W.S. and Chuang, L.M. (2006). Human genetics of adiponectin in the metabolic syndrome. J. Mol. Med. 84, 112-121. https://doi.org/10.1007/s00109-005-0011-7
  47. Yu, K., Li, Q., Bergen, A.W., Pfeiffer, R.M., Rosenberg, P.S., Caporaso, N., Kraft, P., and Chatterjee, N. (2009). Pathway analysis by adaptive combination of p-values. Genet. Epidemiol. 38, 700-709.
  48. Zhu, X., Cooper, R.S., Luke, A., Chen, G., Wu, X., Kan, D., Chakravarti, A., and Weder, A. (2002). A genome-wide scan for obesity in African-Americans. Diabetes 51, 541-544. https://doi.org/10.2337/diabetes.51.2.541

Cited by

  1. Systems genetics in “-omics” era: current and future development vol.132, pp.1, 2013, https://doi.org/10.1007/s12064-012-0168-x