<em>Novel therapeutics for complex diseases from genome-wide association data</em>     — ASN Events
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  2. Symposium 1A: Bioinformatics and Genomics
  3. Novel therapeutics for complex diseases from genome-wide association data    

Novel therapeutics for complex diseases from genome-wide association data     (#4)

Mani Grover 1 , Sara Ballouz 2 , K Mohanasundaram 1 , Tamsyn Crowley 1 , Craig Sherman 3 , Merridee Wouters 1
  1. School of Medicine, Deakin University, Geelong, Outside U.S. & Canada, Australia
  2. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, United States.
  3. School of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, VIC, Australia

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. 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 (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 452 candidate genes as therapeutic targets for the seven phenotypes of interest and 2,130 drugs feasible for repositioning against the predicted novel targets. All three drug databases made significant contributions to target identification, with the highest contribution from DrugBank (400), followed by TTD (156) and PharmGKB (61). We also found 428 novel therapeutic targets accounting for almost 94% of the identified targets. Potential drugs associated with novel targets may be used for further evaluation directly in phase II clinical trials

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.