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dc.contributor.authorVitali, Francesca
dc.contributor.authorBerghout, Joanne
dc.contributor.authorFan, Jungwei
dc.contributor.authorLi, Jianrong
dc.contributor.authorLi, Qike
dc.contributor.authorLi, Haiquan
dc.contributor.authorLussier, Yves A
dc.date.accessioned2019-08-06T23:57:15Z
dc.date.available2019-08-06T23:57:15Z
dc.date.issued2019
dc.identifier.citationVitali, F., Berghout, J., Fan, J. W., Li, J., Li, Q., Li, H., & Lussier, Y. A. (2019, January). Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms. In PSB (pp. 308-319).en_US
dc.identifier.issn2335-6936
dc.identifier.pmid30864332
dc.identifier.doi10.1142/9789813279827_0028
dc.identifier.urihttp://hdl.handle.net/10150/633737
dc.description.abstractRepurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis-and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene -and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10(-8)). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.en_US
dc.description.sponsorshipUniversity of Arizona Health Sciences CB2; BIO5 Institute; UA Cancer Center; NIH [U01AI122275]en_US
dc.language.isoenen_US
dc.publisherWORLD SCIENTIFIC PUBL CO PTE LTDen_US
dc.rights© 2018 The Authors. Open Access chapter published by World Scientific Publishing Company, distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 Licenseen_US
dc.subjectDrug repurposingen_US
dc.subjectnetwork analysisen_US
dc.subjectdrug repositioningen_US
dc.subjecttranslational bioinformaticsen_US
dc.titlePrecision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphismsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Ctr Biomed Informat & Biostat CB2en_US
dc.contributor.departmentUniv Arizona, Dept Med COM Ten_US
dc.contributor.departmentUniv Arizona, Ctr Appl Genet & Genom Meden_US
dc.contributor.departmentUniv Arizona, Dept Biosyst Engnen_US
dc.contributor.departmentUniv Arizona, BIO5 Insten_US
dc.contributor.departmentUniv Arizona, UA Canc Ctren_US
dc.contributor.departmentUniv Arizona, UAHSen_US
dc.identifier.journalPACIFIC SYMPOSIUM ON BIOCOMPUTING 2019en_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
refterms.dateFOA2019-08-06T23:57:15Z


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