genetic variants

genome-wide association

association studies

complex traits

rare variants

association study

susceptibility loci

gene-gene interactions

Nature Genetics

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types