Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Abstract

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

Figure 1

Estimates of Functional Enrichment under the Null We simulated a polygenic disease architecture in imputed data with no functional enrichment (see text). Simulated phenotypes were tested with the variance-component method (top) from 3,000 simulations and with p value enrichment (bottom) from 100 simulations. In the variance-component subplot, the thin line represents the median, boxes represent the first and third quartiles, and whiskers represent the 1.5× interquartile range from the first to the third quartile. A subplot of p value enrichment shows 1.96× SE as shaded regions.

Figure 2

Estimates of Functional Enrichment from a Single Causal Category We simulated a polygenic disease architecture in imputed data with causal SNPs drawn from a single functional category, corresponding to complete enrichment. Simulated phenotypes were tested with the variance-component method (top) from 200 simulations and with p value enrichment (bottom) from 100 simulations. In the variance-component subplot, the thin line represents the median, boxes represent the first and third quartiles, and whiskers represent the 1.5× interquartile range from the first to the third quartile. Subplots of p value enrichment show 1.96× SE across simulations as shaded regions. For each method, only the coding-causal and DHS-causal scenarios are shown (additional simulations appear in Figures S6 and S7).

Figure 3

Functional Partitioning of SNP Heritability across 11 Traits (Top panels) Joint estimates of the percentage of $h_g^2$ from six functional components are shown in filled bars (meta-analyzed over 11 traits). The null expectation, equal to the percentage of SNPs in each category, is shown by dashed, unfilled bars, and p values report the difference from this expectation. Fold enrichment relative to the null expectation is shown in parentheses below each category. The left panel shows results from analyses of genotyped SNPs only, and the right panel shows analysis of genotyped and 1000 Genomes imputed SNPs. Error bars show 1.96× SE after adjustment for shared controls. (Bottom panels) Partitioned $h_g^2$ in simulations of a “realistic” trait where DHS and coding variants explained 79% and 8% of $h_g^2$, respectively (with no enrichment elsewhere). Filled bars show the mean inferred percentage of $h_g^2$ from genotyped (left) and imputed (right) SNPs over 100 simulations. Patterned bars show the simulated true partition. Error bars show 1.96× SE (on average, SEs on imputed data were 2.2× higher than SEs on genotype data as a result of the abundance of new variants).

Figure 4

Enrichment from GWAS Summary Statistics (Left panel) Estimates of p value enrichment are averaged over 11 analyzed traits and are restricted to minimum p value thresholds (x axis) for which at least one association meeting the threshold was observed in every trait. (Middle panel) p value enrichment from a “realistic” simulation. (Right panel) Variance-component enrichment from a “realistic” simulation. Realistic traits were simulated with DHS and coding variants explaining 79% and 8% of $h_g^2$, respectively, and with computed GWAS statistics in a cohort of 32,000 samples. Shaded regions and error bars represent the SE from meta-analysis (left) and 50 replicates (middle and right).

Figure 5

Hierarchical Analysis of Functional Enrichment DHS variants were further partitioned into three subcategories: predicted enhancers (A), cell-type-specific DHSs (B), and DGF targets (C). Each block contains (on the top line) the functional category and fraction of the genome (in parentheses) and (on the bottom line) the fraction enriched in relation to the rest of the genome and the p value of enrichment in relation to the parent category (in parentheses). DHS enrichment of 4.7× nonsignificantly differed from 5.1× in Figure 3 as a result of additional free parameters.