We have shown that applying stratified LD score regression to sets of specifically expressed genes identifies disease-relevant tissues and cell types. Our approach, LDSC-SEG, allows us to take advantage of the large amount of gene expression data available—including fine-grained data for which we do not currently have a comparable chromatin counterpart—to ask questions ranging in resolution from whether a trait is brain-related to whether excitatory or inhibitory neurons are more important for disease etiology. Our results improve our understanding of the phenotypes studied here, highlight the power of GWAS as a source of biological insight, and may also be useful for choosing the relevant tissue or cell type for in-vitro experiments to further elucidate molecular mechanisms underlying genome-wide significant loci identified in genome-wide association studies.
