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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.accessioned2018-05-10T08:56:43Z
dc.date.available2018-05-10T08:56:43Z
dc.date.issued2018-05-02en
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. Available: http://dx.doi.org/10.1101/311654.en
dc.identifier.doi10.1101/311654en
dc.identifier.urihttp://hdl.handle.net/10754/627824.1
dc.description.abstractPurpose: An increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its 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 variants underlying oligogenic diseases in individual whole exome or whole genome sequences. Methods: Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical research can provide useful information and improve variant prioritization for Mendelian diseases. Additionally, background knowledge about interactions between genes can be utilized to guide and restrict the selection of candidate disease modules. Results: We developed OligoPVP, an algorithm that can be used to identify variants in oligogenic diseases and their interactions, 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. Conclusions: Our results show that OligoPVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.en
dc.description.sponsorshipThis 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 and FCC/1/1976-08-01. 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.en
dc.publisherCold Spring Harbor Laboratoryen
dc.relation.urlhttps://www.biorxiv.org/content/early/2018/05/02/311654en
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectoligogenic diseaseen
dc.subjectvariant prioritizationen
dc.subjectartificial intelligenceen
dc.subjectphenotype similarityen
dc.titleOligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variantsen
dc.typeWorking Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division*
dc.contributor.departmentComputer Science Program*
dc.contributor.departmentComputational Bioscience Research Center (CBRC)*
dc.eprint.versionPre-printen
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/1/3454-01-01en
kaust.grant.numberFCC/1/1976-08-01en
refterms.dateFOA2018-06-13T15:41:23Z


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