An open access database of genome-wide association results

Abstract

Background: The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results.

Methods: We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS.

Results: Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10(-14)), a finding which was not perturbed by a sensitivity analysis.

Conclusion: We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.

Figure 1

Genome-wide plots of available GWAS results for all associations P = 0.0001.

Figure 2

Genome-wide plots of available GWAS results for all associations 5 × 10^-8 ≤ P ≤ 1 × 10^-4.

Figure 3

Cumulative percent of GWAS association SNPs falling within specified distances from a protein-coding RefSeq gene. SNP frequency by distance is plotted for significant GWAS associations from studies that employed only Illumina of only Affymetrix platforms (in 10 kb bins).

Figure 4

Cumulative percent of arrayed SNPs falling within specified distances from a protein-coding RefSeq gene. SNP frequency by distance is plotted for arrayed SNPs on Affymetrix or Illumina arrays (in 10 kb bins). The gene-centric arrays from each company are not displayed since relatively few studies analyzed here have published results based on these arrays. The Illumina Hap240, Hap300 and Hap650 are omitted because they show a highly similar pattern to the Hap550 array.