GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database

We created a deeply extracted and annotated database of genome-wide association studies (GWAS) results. GRASP v1.0 contains 46.2 million SNP-phenotype association from among 1390 GWAS studies. We re-annotated GWAS results with 16 annotation sources including some rarely compared to GWAS results (e.g. RNAediting sites, lincRNAs, PTMs). Motivation: To create a high-quality resource to facilitate further use and interpretation of human GWAS results in order to address important scientific questions. Results: GWAS have grown exponentially, with increases in sample sizes and markers tested, and continuing bias toward European ancestry samples. GRASP contains > 100 000 phenotypes, roughly: eQTLs (71.5%), metabolite QTLs (21.2%), methylation QTLs (4.4%) and diseases, biomarkers and other traits (2.8%). c/s-eQTLs, meQTLs, mQTLs and MHC region SNPs are highly enriched among significant results. After removing these categories, GRASP still contains a greater proportion of studies and results than comparable GWAS catalogs. Cardiovascular disease and related risk factors pre-dominate remaining GWAS results, followed by immunological, neurological and cancer traits. Significant results in GWAS display a highly gene-centric tendency. Sex chromosome X (OR= 0.18[0.16-0.20]) and Y (OR= 0.003[0.001-0.01]) genes are depleted for GWAS results. Gene length is correlated with GWAS results at nominal significance (P <= 0.05) levels. We show this gene-length correlation decays at increasingly more stringent P-value thresholds. Potential pleotropic genes and SNPs enriched for multi-phenotype association in GWAS are identified. However, we note possible population stratification at some of these loci. Finally, via re-annotation we identify compelling functional hypotheses at GWAS loci, in some cases unrealized in studies to date. Conclusion: Pooling summary-level GWAS results and re-annotating with bioinformatics predictions and molecular features provides a good platform for new insights.