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dc.contributor.authorRaychaudhuri, S
dc.contributor.authorPlenge, RM
dc.contributor.authorRossin, EJ
dc.contributor.authorNg, ACY
dc.contributor.authorInternational Schizophrenia Consortium,
dc.contributor.authorPurcell, SM
dc.contributor.authorSklar, P
dc.contributor.authorScolnick, EM
dc.contributor.authorXavier, RJ
dc.contributor.authorAltshuler, D
dc.contributor.authorDaly, MJ
dc.date.accessioned2020-12-22T05:47:13Z
dc.date.available2020-12-22T05:47:13Z
dc.date.issued2009-06
dc.identifier.citationRaychaudhuri, 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.
dc.identifier.issn1553-7390
dc.identifier.urihttp://hdl.handle.net/11343/258357
dc.description.abstractTranslating 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/).
dc.languageeng
dc.publisherPublic Library of Science (PLoS)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleIdentifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pgen.1000534
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.source.titlePLoS Genetics
melbourne.source.volume5
melbourne.source.issue6
melbourne.source.pagese1000534-
dc.rights.licenseCC BY
melbourne.elementsid1193783
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694358
melbourne.contributor.authorStone, Jennifer
dc.identifier.eissn1553-7404
melbourne.accessrightsOpen Access


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