ArticleBMC medical genomics2015
Novel therapeutics for coronary artery disease from genome-wide association study data.
Article in BMC medical genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- The landscape of the methodology in drug repurposing using human genomic data: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Current trends and future prospects of drug repositioning in gastrointestinal oncology.Frontiers in pharmacology · 2023Review
- Drug repurposing-a promising approach for patients with angina but non-obstructive coronary artery disease (ANOCA).Frontiers in cardiovascular medicine · 2023Review
- Repurposing drugs to treat cardiovascular disease in the era of precision medicine.Nature reviews. Cardiology · 2022Review
- Considerations and challenges for sex-aware drug repurposing.Biology of sex differences · 2022Review
- Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing.Medical sciences (Basel, Switzerland) · 2022Review
- Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak.Viruses · 2020Review
- Turning genome-wide association study findings into opportunities for drug repositioning.Computational and structural biotechnology journal · 2020Review
- A Pooling Genome-Wide Association Study Identifies Susceptibility Loci and Signaling Pathways of Immune Thrombocytopenia in Chinese Han Population.International journal of genomics · 2020Article
- Drug repurposing: progress, challenges and recommendations.Nature reviews. Drug discovery · 2019Review
- Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records.Clinical and translational science · 2018Review
- Drug repositioning: computational approaches and research examples classified according to the evidence level.Discoveries (Craiova, Romania) · 2017Review
- Connecting the dots in translational bioinformatics: TBC 2014 collection.BMC medical genomics · 2015Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary 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.
methodsPreviously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://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 bioinformatic 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.
resultsOur 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).
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.