GRAIL: Gene Relationships Across Implicated Loci
GRAIL is 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.
Links
- SUBMIT Disease Associated (HapMap) SNPs or Genomic Regions to GRAIL
- Download software to visualize grail results
- Frequently asked questions
- Download gene-text vectors (December 2006)
- Download gene-text vectors (March 2009)
- Download gene-text vectors (May 2010)
- Download gene-text vectors (April 2011)
Reference
Raychaudhuri, 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., and Daly, M.J. Identifying Relationships Among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions. PLOS Genetics, 2009. 5(6):e1000534.
Any questions?
You can contact us at G R A I L at broad . mit . edu.