ReviewComputational and structural biotechnology journal2020
Turning genome-wide association study findings into opportunities for drug repositioning.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- mLeveraging genetic correlations to prioritize drug groups for repurposing in type 2 diabetes.The pharmacogenomics journal · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- Reversal gene expression assessment for drug repurposing, a case study of glioblastoma.Journal of translational medicine · 2025Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- Promising candidate drug target genes for repurposing in cervical cancer: A bioinformatics-based approach.Narra J · 2024Article
- Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma.Research square · 2024Article
- Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design.ACS omega · 2024Article
- Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation.Molecular psychiatry · 2024Article
- Article
- Mapping drug biology to disease genetics to discover drug impacts on the human phenome.Bioinformatics advances · 2024Article
- Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease.HGG advances · 2023Article
- Repurposing drugs to treat cardiovascular disease in the era of precision medicine.Nature reviews. Cardiology · 2022Review
- From GWAS to drug screening: repurposing antipsychotics for glioblastoma.Journal of translational medicine · 2022Article
- Identification of Druggable Genes for Asthma by Integrated Genomic Network Analysis.Biomedicines · 2022Article
- Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.PloS one · 2022Article
- StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit.Frontiers in genetics · 2022Article
- Combining Human Genetics of Multiple Sclerosis with Oxidative Stress Phenotype for Drug Repositioning.Pharmaceutics · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases.Cardiovascular drugs and therapy · 2021Review
- Using common genetic variants to find drugs for common epilepsies.Brain communications · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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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.