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Annotations of gene structures and regulatory elements can inform genome-wideassociation studies (GWAS). However, choosing the relevant annotations forinterpreting an association study of a given trait remains challenging. Wedescribe a statistical model that uses association statistics computed acrossthe genome to identify classes of genomic element that are enriched or depletedfor loci that influence a trait. The model naturally incorporates multipletypes of annotations. We applied the model to GWAS of 18 human traits,including red blood cell traits, platelet traits, glucose levels, lipid levels,height, BMI, and Crohn's disease. For each trait, we evaluated the relevance of450 different genomic annotations, including protein-coding genes, enhancers,and DNase-I hypersensitive sites in over a hundred tissues and cell lines. Weshow that the fraction of phenotype-associated SNPs that influence proteinsequence ranges from around 2% (for platelet volume) up to around 20% (for LDLcholesterol); that repressed chromatin is significantly depleted for SNPsassociated with several traits; and that cell type-specific DNase-Ihypersensitive sites are enriched for SNPs associated with several traits (forexample, the spleen in platelet volume). Finally, by re-weighting each GWASusing information from functional genomics, we increase the number of loci withhigh-confidence associations by around 5%.

Joint analysis of functional genomic data and genome-wide association studies of 18 human traits