Poster Presentation 9th GeneMappers Conference 2012

Association of detailed Drug data with the predicted candidate genes in Gentrepid. (#121)

Mani P. Grover 1 , Kaavya A. Mohanasundaram 1 , Sara Ballouz 2 , Richard A. George 3 , Craig Sherman 1 , Merridee A. Wouters 1
  1. School of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, VIC, Australia
  2. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
  3. Victor Chang Cardiac Research Institute, Darlinghurst , NSW, Australia

Gene identification by candidate gene prediction systems and investigation of drug databases allow researchers to identify new drugs for candidate genes predicted. Gentrepid, a human candidate gene discovery platform utilizes two algorithms named Common Module Profiling and Common Pathway Scanning to prioritize candidate genes for human inherited disorders. Recently, we published several protocols for analyzing Genome Wide Association Studies (GWAS) using  the Welcome Trust Case Control Consortium (WTCCC) data set (Ballouz et al, 2011) on 7 complex diseases. At present, we are integrating drug databases to enable researchers to immediately associate potential therapeutics with candidate genes. For instance, Gentrepid predicted Peroxisome proliferator activated receptor delta (PPARD) as a candidate gene for Type II diabetes. After analyzing this gene in different drug databases, Drug Bank suggested 10 drugs to treat lipid and glucose metabolic diseases while Therapeutic Target Database (TTD) indicated 2 drugs to treat obesity and hyperlipidemia and Pharm-GKB database suggested 2 drugs to treat prostatic neoplasms. For another Gentrepid candidate gene for Type II diabetes named Carbohydrate (chondroitin 6) sulfotranferase3 (CHST3), Pharm-GKB suggested 2 drugs to treat prostatic neoplasms which also target PPARD gene. Thus, Gentrepid can be utilized as a platform to reposition drugs towards novel phenotypes in both mouse studies and clinical trials.

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  1. Ballouz, S., Liu, J., Oti, M., Gaeta, B., Fatkin, D., Bahlo, M. & Wouters, M. 2011. Analysis of genome-wide association study data using the protein knowledge base. BMC Genetics, 12, 98.
  2. George, R. A., Liu, J. Y., Feng, L. L., Bryson-Richardson, R. J., Fatkin, D. & Wouters, M. A. 2006. Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Research, 34, e130
  3. Chen, X, Ji, ZL & Chen, YZ 2002, 'TTD: Therapeutic Target Database', Nucleic Acids Research, vol. 30, no. 1, pp. 412-5.
  4. Wishart, DS, Knox, C, Guo, AC, Shrivastava, S, Hassanali, M, Stothard, P, Chang, Z & Woolsey, J 2006, 'DrugBank: a comprehensive resource for in silico drug discovery and exploration', Nucleic Acids Research, vol. 34, no. suppl 1, pp. D668-D72.
  5. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007, 447(7145):661-678.
  6. Hernandez-Boussard, T, Whirl-Carrillo, M, Hebert, JM, Gong, L, Owen, R, Gong, M, Gor, W, Liu, F, Truong, C, Whaley, R, Woon, M, Zhou, T, Altman, RB & Klein, TE 2008, The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge,Nucleic Acids Research 36(suppl 1): D913-D918