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Bioinformatics. 2014 Jun 15;30(12):i185-94. doi: 10.1093/bioinformatics/btu273.

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

Author information

1
Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USACardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA.
2
Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung and Blood Institute, The Framingham Heart Study, Framingham, MA 01702, University of Massachusetts Medical School, Worcester, MA 01655 and Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA.

Abstract

SUMMARY:

We created a deeply extracted and annotated database of genome-wide association studies (GWAS) results. GRASP v1.0 contains >6.2 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%). cis-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.

AVAILABILITY:

The GRASP database is available at http://apps.nhlbi.nih.gov/grasp.

PMID:
24931982
PMCID:
PMC4072913
DOI:
10.1093/bioinformatics/btu273
[Indexed for MEDLINE]
Free PMC Article

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