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dc.contributor.authorBoudellioua, Imene
dc.contributor.authorKulmanov, Maxat
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorGkoutos, Georgios V.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2018-11-25T06:48:16Z
dc.date.available2018-05-10T08:56:43Z
dc.date.available2018-11-25T06:48:16Z
dc.date.issued2018-10-02
dc.identifier.citationBoudellioua I, Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2018) OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants. Scientific Reports 8. Available: http://dx.doi.org/10.1038/s41598-018-32876-3.
dc.identifier.issn2045-2322
dc.identifier.doi10.1038/s41598-018-32876-3
dc.identifier.doi10.1101/311654
dc.identifier.urihttp://hdl.handle.net/10754/627824
dc.description.abstractAn increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene–phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.
dc.description.sponsorshipThe authors thank Dr. Nadia Schoenmakers and Professor Eamonn Maher for helpful comments on our manuscript. This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, FCC/1/1976-08-01, and FCS/1/3657-02-01. GVG acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS:1340112) as well as support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.
dc.publisherSpringer Nature
dc.relation.urlhttps://www.nature.com/articles/s41598-018-32876-3
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectoligogenic disease
dc.subjectvariant prioritization
dc.subjectartificial intelligence
dc.subjectphenotype similarity
dc.titleOligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalScientific Reports
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK
dc.contributor.institutionNIHR Biomedical Research Centre, B15 2TT, Birmingham, UK
dc.contributor.institutionNIHR Surgical Reconstruction and Microbiology Research Centre, B15 2TT, Birmingham, UK
dc.contributor.institutionNIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK
dc.contributor.institutionInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, B15 2TT, Birmingham, United Kingdom
dc.contributor.institutionCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, Birmingham, United Kingdom
kaust.personBoudellioua, Imene
kaust.personKulmanov, Maxat
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3454-01-01
kaust.grant.numberFCC/1/1976-08-01
kaust.grant.numberFCS/1/3657-02-01
refterms.dateFOA2018-06-13T15:41:23Z
dc.date.published-online2018-10-02
dc.date.published-print2018-12


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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