• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. 
    •   QMRO Home
    • Wolfson Institute of Preventive Medicine
    • Centre for Psychiatry
    • Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.
    •   QMRO Home
    • Wolfson Institute of Preventive Medicine
    • Centre for Psychiatry
    • Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.

    View/Open
    Published version (554.1Kb)
    Volume
    5
    Pagination
    e1000534 - ?
    DOI
    10.1371/journal.pgen.1000534
    Journal
    PLoS Genet
    Issue
    6
    Metadata
    Show full item record
    Abstract
    Translating a set of disease regions into insight about pathogenic mechanisms requires not only the ability to identify the key disease genes within them, but also the biological relationships among those key genes. Here we describe a statistical method, Gene Relationships Among Implicated Loci (GRAIL), that takes a list of disease regions and automatically assesses the degree of relatedness of implicated genes using 250,000 PubMed abstracts. We first evaluated GRAIL by assessing its ability to identify subsets of highly related genes in common pathways from validated lipid and height SNP associations from recent genome-wide studies. We then tested GRAIL, by assessing its ability to separate true disease regions from many false positive disease regions in two separate practical applications in human genetics. First, we took 74 nominally associated Crohn's disease SNPs and applied GRAIL to identify a subset of 13 SNPs with highly related genes. Of these, ten convincingly validated in follow-up genotyping; genotyping results for the remaining three were inconclusive. Next, we applied GRAIL to 165 rare deletion events seen in schizophrenia cases (less than one-third of which are contributing to disease risk). We demonstrate that GRAIL is able to identify a subset of 16 deletions containing highly related genes; many of these genes are expressed in the central nervous system and play a role in neuronal synapses. GRAIL offers a statistically robust approach to identifying functionally related genes from across multiple disease regions--that likely represent key disease pathways. An online version of this method is available for public use (http://www.broad.mit.edu/mpg/grail/).
    Authors
    Raychaudhuri, S; Plenge, RM; Rossin, EJ; Ng, ACY; International Schizophrenia Consortium; Purcell, SM; Sklar, P; Scolnick, EM; Xavier, RJ; Altshuler, D
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/23565
    Collections
    • Centre for Psychiatry [767]
    Language
    eng
    Licence information
    This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Copyright statements
    (c) 2009 Raychaudhuri et al.
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.