Raychaudhuri 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-
PLoS Genetics; 5; 6;
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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