University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne Medical School
  • Medicine and Radiology
  • Medicine and Radiology - Research Publications
  • View Item
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne Medical School
  • Medicine and Radiology
  • Medicine and Radiology - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Annotating pathogenic non-coding variants in genic regions

    Thumbnail
    Download
    published version (737.5Kb)

    Citations
    Scopus
    Web of Science
    Altmetric
    32
    33
    Author
    Gelfman, S; Wang, Q; McSweeney, KM; Ren, Z; La Carpia, F; Halvorsen, M; Schoch, K; Ratzon, F; Heinzen, EL; Boland, MJ; ...
    Date
    2017-08-09
    Source Title
    Nature Communications
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Petrovski, Slave
    Affiliation
    Medicine and Radiology
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Gelfman, S., Wang, Q., McSweeney, K. M., Ren, Z., La Carpia, F., Halvorsen, M., Schoch, K., Ratzon, F., Heinzen, E. L., Boland, M. J., Petrovski, S. & Goldstein, D. B. (2017). Annotating pathogenic non-coding variants in genic regions. NATURE COMMUNICATIONS, 8 (1), https://doi.org/10.1038/s41467-017-00141-2.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256735
    DOI
    10.1038/s41467-017-00141-2
    Abstract
    Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [45770]
    • Medicine and Radiology - Research Publications [2347]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors