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dc.contributor.authorJames, Regis A.
Campbell, Ian M.
Chen, Edward S.
Boone, Philip M.
Rao, Mitchell A.
Bainbridge, Matthew N.
Lupski, James R.
Yang, Yaping
Eng, Christine M.
Posey, Jennifer E.
Shaw, Chad A.
dc.date.accessioned 2017-05-23T19:32:18Z
dc.date.available 2017-05-23T19:32:18Z
dc.date.issued 2016
dc.identifier.citation James, Regis A., Campbell, Ian M., Chen, Edward S., et al.. "A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics." Genome Medicine, 8, no. 13 (2016) https://doi.org/10.1186/s13073-016-0261-8.
dc.identifier.urihttp://hdl.handle.net/1911/94365
dc.description.abstractBackground: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.
dc.language.iso eng
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics
dc.type Journal article
dc.citation.journalTitle Genome Medicine
dc.subject.keyworddisease gene discovery
exome
semantic similarity
variant prioritization
dc.citation.volumeNumber 8
dc.citation.issueNumber 13
dc.contributor.publisher BioMed Central
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1186/s13073-016-0261-8
dc.identifier.pmcid PMC4736244
dc.identifier.pmid 26838676
dc.type.publication publisher version


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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.