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

PLoS genetics | Jun 26, 2009

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/).

Pubmed ID: 19557189 RIS Download

Mesh terms: Crohn Disease | Databases, Genetic | Gene Deletion | Genome, Human | Genome-Wide Association Study | Genomics | Humans | Meta-Analysis as Topic | Polymorphism, Single Nucleotide | Schizophrenia

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Associated grants

  • Agency: NIDDK NIH HHS, Id: P30 DK040561
  • Agency: NHGRI NIH HHS, Id: U01 HG004171
  • Agency: NIAMS NIH HHS, Id: K08 AR055688-01A1
  • Agency: NIAMS NIH HHS, Id: 1K08AR055688-01A1
  • Agency: NIGMS NIH HHS, Id: T32 GM007753
  • Agency: NIAMS NIH HHS, Id: T32 AR007530
  • Agency: NIDDK NIH HHS, Id: P30 DK040561-14
  • Agency: NIDDK NIH HHS, Id: R01 DK083759
  • Agency: NHGRI NIH HHS, Id: U01HG004171
  • Agency: NIAMS NIH HHS, Id: T32AR007530-23
  • Agency: NIAMS NIH HHS, Id: K08 AR055688

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Gene Relationships Across Implicated Loci

A tool to examine relationships between genes in different disease associated loci. Given several genomic regions or SNPs associated with a particular phenotype or disease, GRAIL looks for similarities in the published scientific text among the associated genes. As input, users can upload either (1) SNPs that have emerged from a genome-wide association study or (2) genomic regions that have emerged from a linkage scan or are associated common or rare copy number variants. SNPs should be listed according to their rs#''s and must be listed in HapMap. Genomic Regions are specified by a user-defined identifier, the chromosome that it is located on, and the start and end base-pair positions for the region. Grail can take two sets of inputs - Query regions and Seed regions. Seed regions are definitely associated SNPs or genomic regions, and Query regions are those regions that the user is attempting to evaluate agains them. In many applications the two sets are identical. Based on textual relationships between genes, GRAIL assigns a p-value to each region suggesting its degree of functional connectivity, and picks the best candidate gene. GRAIL is developed by Soumya Raychaudhuri in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, and the Broad Institute. GRAIL is described in manuscript, currently in preparation.


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