  Annotations of gene structures and regulatory elements can inform genome-wide
association studies (GWAS). However, choosing the relevant annotations for
interpreting an association study of a given trait remains challenging. We
describe a statistical model that uses association statistics computed across
the genome to identify classes of genomic element that are enriched or depleted
for loci that influence a trait. The model naturally incorporates multiple
types 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 of
450 different genomic annotations, including protein-coding genes, enhancers,
and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We
show that the fraction of phenotype-associated SNPs that influence protein
sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL
cholesterol); that repressed chromatin is significantly depleted for SNPs
associated with several traits; and that cell type-specific DNase-I
hypersensitive sites are enriched for SNPs associated with several traits (for
example, the spleen in platelet volume). Finally, by re-weighting each GWAS
using information from functional genomics, we increase the number of loci with
high-confidence associations by around 5%.
