Identification of novel therapeutics for complex diseases from genome-wide association data

Grover, M. P., Ballouz, S., Mohanasundaram, K. A., George, R. A., H Sherman, C. D., Crowley, T. M., Wouters, M. A. (2014) Identification of novel therapeutics for complex diseases from genome-wide association data. BMC Medical Genomics, 7 (SUPPL.). ISSN 17558794 (ISSN)

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URL: http://www.biomedcentral.com/1755-8794/7/S1/S8
DOI: 10.1186/1755-8794-7-S1-S8

Abstract

Background: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. Methods. We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. Results: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. Conclusions: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments. © 2014 Grover et al.; licensee BioMed Central Ltd.

Item Type: Paper
Uncontrolled Keywords: Candidate gene Complex disease Drug database Drug repositioning Drug target Genome-wide association study
Subjects: bioinformatics
therapies > gene therapy
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
therapies > personalised therapy
CSHL Authors:
Communities: CSHL labs > Gillis Lab
Depositing User: Matt Covey
Date Deposited: 27 May 2014 15:40
Last Modified: 27 May 2014 15:40
URI: http://repository.cshl.edu/id/eprint/30184

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