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Novel therapeutics for cononary artery disease from genome-wide association study data

Grover, Mani P., Ballouz, Sara, Mohanasundaram, Kaavya A., George, Richard A., Goscinski, Andrezej, Crowely, Tamsyn M., Sherman, Craig D. H. and Wouters, Merridee 2015, Novel therapeutics for cononary artery disease from genome-wide association study data, BMC medical genomics, vol. 8, no. Supplement 2, pp. 1-11, doi: 10.1186/1755-8794-8-S2-S1.

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Title Novel therapeutics for cononary artery disease from genome-wide association study data
Author(s) Grover, Mani P.
Ballouz, Sara
Mohanasundaram, Kaavya A.
George, Richard A.
Goscinski, Andrezej
Crowely, Tamsyn M.
Sherman, Craig D. H.ORCID iD for Sherman, Craig D. H. orcid.org/0000-0003-2099-0462
Wouters, Merridee
Journal name BMC medical genomics
Volume number 8
Issue number Supplement 2
Start page 1
End page 11
Total pages 11
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2015
ISSN 1755-8794
Summary Abstract
Background: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.
Methods: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatics modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.
Results: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).
Conclusions: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Language eng
DOI 10.1186/1755-8794-8-S2-S1
Field of Research 119999 Medical and Health Sciences not elsewhere classified
0604 Genetics
1101 Medical Biochemistry And Metabolomics
1112 Oncology And Carcinogenesis
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, BioMed Central
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077744

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.