Here, we apply stratified LD score regression7, a method for partitioning heritability from GWAS summary statistics, to sets of specifically expressed genes to identify disease-relevant tissues and cell types across 48 diseases and traits with an average GWAS sample size of 169,331. We first analyze two gene expression data sets3,17,18 containing a wide range of tissues to infer system-level enrichments. We then analyze chromatin data from the Roadmap Epigenomics and ENCODE projects1,2 across the same set of diseases and traits to validate these results. Finally, we analyze gene expression data sets that allow us to achieve higher resolution within a system3,19–21, identifying enriched brain regions, brain cell types, and immune cell types for several brain- and immune-related diseases and traits; we validate several of our immune enrichments using independent chromatin data. Our results underscore that a heritability-based framework applied to gene expression data allows us to achieve high-resolution enrichments, even for very polygenic traits.
