Many different types of data characterizing tissue- and cell-type-specific activity have been analyzed together with GWAS data to identify disease-relevant tissues and cell types, including histone marks4–8, DNase I hypersensitivity (DHS)9–12, eQTLs3,13, and gene expression data14–17. Of these data types, gene expression data (without genotypes or eQTLs) has the advantage of being available in the widest range of tissues and cell types. Previous studies have shown that gene expression data are informative for disease-relevant tissues and cell types, and have led to biological insights about the diseases and traits studied14–17. However, the methods applied in these studies restrict their analyses to subsets of SNPs that pass a significance threshold. To our knowledge, no previous study has modeled genome-wide polygenic signals to identify disease-relevant tissues and cell types systematically from GWAS and gene expression data.
