The increasing availability of functional annotation data (see above) can also be used to identify functional annotations that are enriched for polygenic signals of disease heritability. A recent study accomplished this using a Bayesian hierarchical model that splits the genome into blocks and incorporates both coarse-scale functional annotations at the level of blocks and fine-scale functional annotations at the level of SNPs59. This was the first study to quantify polygenic enrichments for cell-type-specific chromatin marks and DNase I hypersensitivity sites (DHS) across a broad set of complex traits and diseases. For example, polygenic signals for platelet volume and platelet count were enriched at DHS in CD34+ cells, which are on the cell lineage that lead to platelets, and polygenic signals for Crohn’s disease were depleted at repressed chromatin in LCL, an immune-related cell line. Functional enrichments can alternatively be estimated by stratified LD score regression89, which generalizes LD score regression85 to regress χ2 statistics for each SNP against LD scores with each functional category. Fine-mapping methods can also estimate functional enrichments, although these analyses are often restricted to disease-associated loci47,52,61.
