Common variants implicated by genome-wide association studies (GWAS) of complex diseases are known to be enriched for coding and regulatory variants. We applied methods to partition the heritability explained by genotyped SNPs (h 2 g ) across functional categories (while accounting for shared variance due to linkage disequilibrium) to genotype and imputed data for 11 common diseases. DNaseI Hypersensitivity Sites (DHS) from 218 cell-types, spanning 16% of the genome, explained an average of 79% of h 2 g (5.1× enrichment; P < 10 −20 ); further enrichment was observed at enhancer and cell-type specific DHS elements. The enrichments were much smaller in analyses that did not use imputed data or were restricted to GWASassociated SNPs. In contrast, coding variants, spanning 1% of the genome, explained only 8% of h 2 g (13.8× enrichment; P = 5 × 10 −4 ). We replicated these findings but found no significant contribution from rare coding variants in an independent schizophrenia cohort genotyped on GWAS and exome chips.Recent work by ENCODE and other projects has shown that specific classes of non-coding variants can have complex and diverse impacts on cell function and phenotype 1-7 . With many potentially informative functional categories and competing biological hypotheses, quantifying the contribution of variants in these categories to complex traits would inform trait biology and focus fine-mapping. The availability of significantly associated variants from hundreds of genome-wide association studies (GWAS) 8 has opened one avenue for quantifying enrichment. Indeed, 11% of GWAS hits lie in coding regions 8 and 57% of GWAS hits lie in broadly-defined DHS (spanning 42% of the genome) 5 , with additional GWAS hits tagging these regions. The full distribution of GWAS association statistics exhibits enriched P-values in coding and untranslated regions (UTR) 9 . Analysis of DHS sub-classes and other histone marks has revealed a complex pattern of cell-type specific relationships with known disease associations 4 . However, the question of how much each functional category contributes to disease heritability remains unanswered 10 .Here, we jointly estimate the heritability explained by all SNPs (h 2 g ) in different functional categories, generalizing recent work using variance-component methods [11][12][13][14][15][16][17] . In contrast to analyses of top GWAS hits, this approach leverages the entire polygenic architecture of each trait and can obtain accurate estimates even in the face of pervasive linkage disequilibrium (LD) across functional categories, as we show via extensive simulations. We apply this approach to functional categories in GWAS and exome chip data from > 50, 000 samples.1
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