Ensuring consistency between our proximity-based annotation results and prior work, we first replicated the Finucane et al. multi-tissue schizophrenia S-LDSC analyses (correlation of the total number of SNPs included for each tissue, r = 1, Supp. Table 3, and the correlation of the proportion of SNP heritability explained across all tissues, r > 0.99, Supp. Table 3). This small difference is most likely due to minor variability in reported GWAS summary statistics over time.
Next, we sought to examine differences in S-LDSC coefficients across prior and newly reported GWAS for the same phenotypes. While some traits were highly consistent, such as height (LDSC regression coefficient estimates based on the new vs. prior GWAS summary statistics r = 0.9968, Supp. Figure 1), comparisons of new vs. prior GWAS for other traits, such as Alzheimer’s disease (r = 0.3265, Supp. Figure 2), strongly differed (Supp. Table 4).
For tissue enrichment analyses, at least 100,000 SNPs were mapped to 46 of the 48 tissues when using an expression-based annotation (Supp. Table 2), with pancreas and whole blood falling below this threshold (83,239 and 87,720 SNPs, respectively), suggesting that for these 46 tissues, S-LDSC regression coefficients are likely well controlled for type 1 error. We found the expression-based annotation resulted in fewer identified tissues or brain regions that contribute significantly to \({h}_{SNP}^{2}\) in complex traits when compared to the physical proximity-based annotation. Across the multi-tissue analyses, of the 31 phenotypes examined, 18 had at least one significant tissue when employing a physical proximity annotation, whereas only seven phenotypes had at least one significant tissue when using an expression-based annotation (FDR < 5%, Supp. Figures 3–20, Supp. Table 5). All tissue and trait combinations with significant expression-based annotation enrichments were also identified using the physical proximity annotation with the single exception of ovary tissue in Tourette syndrome (Supp. Figure 19). Of the phenotypes examined, schizophrenia (both sets of published GWAS summary statistics), educational attainment, and intelligence identified all 13 brain regions as significant, representing the maximum individual tissues identified for any trait.
Within-brain analyses were conducted for the 16 phenotypes with significant \({h}_{SNP}^{2}\) contribution of at least one brain region identified in the multi-tissue analyses. We identified significant contributions of specific brain regions in nine and four phenotypes when using a physical proximity and expression-based annotation, respectively (FDR < 5%, Supp. Figures 21–30, Supp. Table 6). All significant expression-based annotations overlapped with a significant physical proximity annotation, with three exceptions: cortex in major depressive disorder (Supp. Figure 27) and cerebellum in the two schizophrenia datasets40,42 (Supp. Figures 28 and 29).
Permutation tests, to assess whether the expression-based annotation procedure led to increased specificity of heritable contribution relative to physical proximity-based annotation, suggested that SNP-gene annotations based on SNP expression effects do not differ from randomly chosen SNPs within the same regions. In all three instances examined, the strength of the regression coefficient using expression-based annotation was no different than when annotating SNPs to genes at random (all p > 0.32, Supp. Figures 31–33, Supp. Table 7).