We developed a new statistical method, stratified LD score regression, for identifying functional enrichment from GWAS summary statistics that uses genome-wide information from all SNPs and explicitly models LD. We applied this method to summary statistics from 17 traits with an average sample size of 73,599. Our method identified strong enrichment for conserved regions across all traits, and immunological disease-specific enrichment for FANTOM5 enhancers. Our cell-type-specific enrichment results confirmed previously known enrichments, such as liver enrichment for HDL levels and pancreatic islet enrichment for fasting glucose. In addition, we identified enrichments that would have been challenging to detect using existing methods, such as CNS enrichment for smoking behavior and educational attainment—traits with only one and three genome-wide significant loci, respectively [33, 34]. Stratified LD score regression represents a significant departure from previous methods that require raw genotypes [11], use only SNPs in genome-wide significant loci [5–8], assume only one causal SNP per locus [9], or do not account for LD [10] (see Online Methods and Figure 7 for a discussion of other methods and comparison on simulated data). Our method is also computationally efficient, despite the 53 overlapping functional categories analyzed.
