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Document Type |
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Article In Journal |
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Document Title |
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e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations |
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Document Language |
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English |
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Abstract |
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Background: Genome-wide association studies (GWAS) have become a mainstay of biological research concerned
with discovering genetic variation linked to phenotypic traits and diseases. Both discrete and continuous traits can
be analyzed in GWAS to discover associations between single nucleotide polymorphisms (SNPs) and traits of
interest. Associations are typically determined by estimating the significance of the statistical relationship between
genetic loci and the given trait. However, the prioritization of bona fide, reproducible genetic associations from
GWAS results remains a central challenge in identifying genomic loci underlying common complex diseases.
Evolutionary-aware meta-analysis of the growing GWAS literature is one way to address this challenge and to
advance from association to causation in the discovery of genotype-phenotype relationships.
Description: We have created an evolutionary GWAS resource to enable in-depth query and exploration of
published GWAS results. This resource uses the publically available GWAS results annotated in the GRASP2 database.
The GRASP2 database includes results from 2082 studies, 177 broad phenotype categories, and ~8.87 million SNPphenotype
associations. For each SNP in e-GRASP, we present information from the GRASP2 database for
convenience as well as evolutionary information (e.g., rate and timespan). Users can, therefore, identify not only
SNPs with highly significant phenotype-association P-values, but also SNPs that are highly replicated and/or occur
at evolutionarily conserved sites that are likely to be functionally important. Additionally, we provide an
evolutionary-adjusted SNP association ranking (E-rank) that uses cross-species evolutionary conservation scores and
population allele frequencies to transform P-values in an effort to enhance the discovery of SNPs with a greater
probability of biologically meaningful disease associations.
Conclusion: By adding an evolutionary dimension to the GWAS results available in the GRASP2 database, our e-GRASP
resource will enable a more effective exploration of SNPs not only by the statistical significance of trait associations, but
also by the number of studies in which associations have been replicated, and the evolutionary context of the associated
mutations. Therefore, e-GRASP will be a valuable resource for aiding researchers in the identification of bona fide,
reproducible genetic associations from GWAS results. This resource is freely available at http://www.mypeg.info/egrasp. |
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ISSN |
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1471-2164 |
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Journal Name |
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BMC genomics |
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Volume |
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17 |
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Issue Number |
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9 |
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Publishing Year |
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1437 AH
2016 AD |
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Article Type |
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Article |
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Added Date |
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Thursday, July 20, 2017 |
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Researchers
| Sajjad Karim | Karim, Sajjad | Researcher | Doctorate | |
| Hend Fakhri NourEldin | NourEldin, Hend Fakhri | Researcher | Doctorate | |
| Heba Abusamra | Abusamra, Heba | Researcher | Doctorate | |
| Nada Salem | Salem, Nada | Researcher | Doctorate | |
| Elham Alhathli | Alhathli, Elham | Researcher | Doctorate | |
| Joel Dudley | Dudley, Joel | Researcher | Doctorate | |
| Max Sanderford | Sanderford, Max | Researcher | Doctorate | |
| Laura B. Scheinfeldt | Scheinfeldt, Laura B. | Researcher | Doctorate | |
| Sudhir Kumar | Kumar, Sudhir | Researcher | Doctorate | |
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