Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/110394
Title: Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions
Authors: Raychaudhuri, Soumya
Plenge, Robert M.
Rossin, Elizabeth J.
Ng, Aylwin C. Y.
Purcell, Shaun M.
Sklar, Pamela
Scolnick, Edward M.
Xavier, Ramnik J.
Altshuler, David
Daly, Mark J.
Azevedo, M. Helena 
Macedo, António 
et al.
Issue Date: 2009
Publisher: Public Library of Science
Project: For 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 
Serial title, monograph or event: PLoS Genetics
Volume: 5
Issue: 6
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/).
URI: https://hdl.handle.net/10316/110394
ISSN: 1553-7404
DOI: 10.1371/journal.pgen.1000534
Rights: openAccess
Appears in Collections:FMUC Medicina - Artigos em Revistas Internacionais

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