In GWAS of complex traits, much of the heritability lies in single-nucleotide polymorphisms (SNPs) that do not reach genome-wide significance at current sample sizes [1, 2]. However, many current approaches that leverage functional information [3, 4] and GWAS data to inform disease biology use only SNPs in genome-wide significant loci [5–8], assume only one causal SNP per locus [9], or do not account for linkage disequilibrium (LD) [10]. We aim to improve power by estimating the proportion of genome-wide SNP-heritability [1] attributable to various functional categories, using information from all SNPs and explicitly modeling LD.
