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

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    Author
    Raychaudhuri, S; Plenge, RM; Rossin, EJ; Ng, ACY; International Schizophrenia Consortium; Purcell, SM; Sklar, P; Scolnick, EM; Xavier, RJ; Altshuler, D; ...
    Date
    2009-06
    Source Title
    PLoS Genetics
    Publisher
    Public Library of Science (PLoS)
    University of Melbourne Author/s
    Stone, Jennifer
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Raychaudhuri, S., Plenge, R. M., Rossin, E. J., Ng, A. C. Y., International Schizophrenia Consortium, Purcell, S. M., Sklar, P., Scolnick, E. M., Xavier, R. J., Altshuler, D. & Daly, M. J. (2009). Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.. PLoS Genet, 5 (6), pp.e1000534-. https://doi.org/10.1371/journal.pgen.1000534.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/258357
    DOI
    10.1371/journal.pgen.1000534
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694358
    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/).

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