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dc.contributor.authorCORVIN, AIDEN PETER
dc.contributor.authorGILL, MICHAEL
dc.contributor.authorMORRIS, DEREK
dc.date.accessioned2010-12-09T12:44:14Z
dc.date.available2010-12-09T12:44:14Z
dc.date.issued2009
dc.date.submitted2009en
dc.identifier.citationRaychaudhuri S, Plenge RM, Rossin EJ, Ng AC; International Schizophrenia Consortium, Purcell SM, Sklar P, Scolnick EM, Xavier RJ, Altshuler D, Daly MJ, Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions., PLoS Genetics, 5, 6, 2009, e1000534-en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/41247
dc.descriptionPUBLISHEDen
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/).en
dc.description.sponsorshipFor this project, SR was supported by a T32 NIH training grant (AR007530-23), an NIH Career Development Award (1K08AR055688-01A1), an American College of Rheumatology Bridge Grant, and through the BWH Rheumatology Fellowship program, directed by Simon Helfgott. MJD is supported by a U01 NIH grant (U01 HG004171). MJD and RJX are supported by an R01 NIH grant (R01 DK083759). ACYN is supported through Research Fellowship Award from the Crohn?s and Colitis Foundation of America. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.format.extente1000534en
dc.language.isoenen
dc.relation.ispartofseriesPLoS Genetics;
dc.relation.ispartofseries5;
dc.relation.ispartofseries6;
dc.rightsYen
dc.subjectGeneticsen
dc.titleIdentifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.en
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/mgill
dc.identifier.peoplefinderurlhttp://people.tcd.ie/acorvin
dc.identifier.peoplefinderurlhttp://people.tcd.ie/morrisdw
dc.identifier.rssinternalid64220


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