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BMC Genomics. 2016 Oct 17;17(Suppl 9):770.

e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations.

Author information

1
Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
2
Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA.
3
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.
4
Department of Biology, Temple University, Philadelphia, PA, 19122, USA.
5
Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia. s.kumar@temple.edu.
6
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. s.kumar@temple.edu.
7
Department of Biology, Temple University, Philadelphia, PA, 19122, USA. s.kumar@temple.edu.

Abstract

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 SNP-phenotype 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 .

KEYWORDS:

Conservation; Disease; GRASP; GWAS; Phenotype; Polymorphism; SNP

PMID:
27766955
PMCID:
PMC5073857
DOI:
10.1186/s12864-016-3088-1
[Indexed for MEDLINE]
Free PMC Article

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