Assessment of cross-phenotype polygenic enrichmentWe assessed the Q-Q plots to investigate the presence of cross-trait polygenic enrichment. The observed enrichment is characterized by an upward and leftward deviation of the plots, indicating a stronger association between the subset of SNPs and the secondary phenotype26. We found that SNPs associations with hypertension were substantially enriched with increasing SNP associations with all epigenetic clocks, suggesting polygenic overlap (Fig. 1). Reverse conditional Q-Q plots were illustrated in Supplementary Fig. 1.Figure 1Cross-trait enrichment between hypertension and epigenetic aging. Quantile–quantile (Q-Q) plots illustrate cross-trait enrichment between hypertension and GrimAge (A), HannumAge (B), IEAA (C), PhenoAge (D). Conditional Q-Q plots of nominal versus empirical \(-{log}_{10}p\), in which p represents the p values corrected for inflation, in primary phenotypes below the GWAS significance threshold of \(p<5\times {10}^{-8}\) as a function of significance of association with the second phenotypes, at \(p<0.10\), \(p<0.01\), \(p<0.001\). The dashed lines indicate the null hypothesis. The blue lines indicate all SNPs.Estimating polygenicity and genetic overlapUnivariate MiXeR analysis revealed that hypertension was a polygenic phenotype, with about \(2.8\text{ K}\pm 78\) variants influencing this phenotype (Fig. 2, Supplementary Table 1). The polygenicity estimates for four epigenetic clocks were approximately 0.7 K variants for GrimAge (SD = 0.19 K), 0.2 K variants for HannumAge (SD = 38), 0.3 K variants for HannumAge (SD = 0.77 K), and 0.1 K variants for PhenoAge (SD = 30) (Fig. 2, Supplementary Table 1).Figure 2Polygenicity of hypertension and epigenetic aging measures. The number of ‘causal’ variants explaining 90% of the heritability.Bivariate MiXeR analysis demonstrated genetic overlap between hypertension and epigenetic clocks (Fig. 3). The number of trait-influenced variants shared between hypertension and GrimAge was 0.4 K \(\pm\) 0.1 K, of which 84% had concordant effect directions. For hypertension and HannumAge, the shared variants accounted for approximately 0.2 K ± 22, with 95% exhibiting concordant effect directions. Similarly, between hypertension and IEAA, the shared variants accounted for approximately 0.2 K ± 34, with 88% exhibiting concordant effect directions. Lastly, between hypertension and PhenoAge, the shared variants accounted for approximately 0.1 K ± 24, of which almost all (98%) had concordant effect directions (Fig. 3, Supplementary Table 2).Figure 3The number of genetic variants for genetic overlap, shared distinct loci and novel loci between hypertension and epigenetic clocks. The number of genetic variants for genetic overlap, shared distinct loci and novel loci between hypertension and GrimAge (A), HannumAge (B), IEAA (C), PhenoAge (D). The numbers displayed in the colored Venn diagrams represent the counts of shared and phenotype-specific trait-influencing variants, collectively explaining 90% of the heritability in thousands. \({r}_{g}\) represents genome-wide genetic correlation.The genetic overlap between hypertension and four epigenetic clocks, measured by the dice coefficient, was GrimAge (22%), HannumAge (11%), IEAA (10%), and PhenoAge (9%) (Supplementary Table 2). Given the differences in the polygenicity of secondary phenotypes, as calculated by Eq. (1), \({P}_{overlap}\) for hypertension and epigenetic clocks was GrimAge (51%), HannumAge (81%), IEAA (57%), and PhenoAge (95%). The minimum Akaike information criterion (AIC) scores between hypertension and HannumAge, and hypertension and PhenoAge are negative, suggesting that the number of overlapped variations between these two pairs of phenotypes may be lower than the estimated.Genetic correlationsWe estimated genetic correlations between hypertension and four epigenetic clocks (GrimAge, HannumAge, IEAA, PhenoAge), respectively. The single-trait LDSC analysis revealed heritability estimates of 0.11, 0.10, 0.12, 0.17, and 0.10 for hypertension, GrimAge, HannumAge, IEAA, and PhenoAge, respectively. No traits were excluded in the further pairwise LDSC analyses (All mean \({\chi }^{2}>1.02\)). Pairwise LDSC showed a strong genetic correlation between hypertension and PhenoAge (\({r}_{g}=0.157\), p = 0.001, Table 1). We also found a significant genetic correlation between hypertension and IEAA (\({r}_{g}=0.091\), p = 0.038, Table 1).
Table 1 Genome-wide genetic correlation between hypertension and epigenetic age acceleration.Shared loci between hypertension and epigenetic agingAfter identifying polygenic overlap, we conducted bi-directional cross-trait enrichment using conjFDR analysis to detect shared genomic loci, enhancing statistical power. We identified a total of 32 distinct genomic loci shared between hypertension and epigenetic clocks—two with GrimAge, seven with HannumAge, 21 with IEAA, and four with PhenoAge (Figs. 3 and 4, Table 2, Supplementary Tables 3–6). Specially, among these loci, we found that the novel locus rs1849209 was also a shared loci jointly associated with three epigenetic clocks (HannumAge, IEAA, PhenoAge) (Table 2, Supplementary Tables 4–6). Two distinct shared loci between hypertension and GrimAge exhibit concordant effect directions. Besides, distinct shared loci between hypertension and HannumAge (2/7, 28.6%), hypertension and IEAA (12/21, 57.1%), as well as hypertension and PhenoAge (2/4, 50%) had concordant effect directions (Table 2, Supplementary Tables 3–6). Further, we identified 25 novel loci for hypertension (Table 2).Figure 4Shared loci between hypertension and epigenetic clocks at conjFDR < 0.05. Common genetic variants jointly associated with hypertension and GrimAge (A), HannumAge (B), IEAA (C), PhenoAge (D) at conjFDR < 0.05. Independent lead SNPs are circled in black.Table 2 All distinct loci associated with hypertension at conjFDR < 0.05.In validation datasets (i.e., SBP and DBP), we checked the p-values and beta of the distinct loci we identified as associated with hypertension. If these distinct loci had beta > 0 and p < 5e−08 in the validation datasets, we considered the conclusion that these loci were associated with hypertension to be relatively more robust. Totally, nine of 32 distinct loci were validated as hypertension-associated in SBP and DBP validation datasets (rs1982200, rs2859868, rs11944870, rs7223364, rs117778193, rs12940887, rs1849209, rs6456686, and rs7119934, Supplementary Table 7). Particularly, five of the nine were identified as hypertension-associated in both validation datasets (i.e., rs1982200, rs2859868, rs7223364, rs12940887, and rs1849209). In addition, we conducted perturbation experiments by randomly extracting the same number of loci (n = 32) 10,000 times to verify the reproduction in validation datasets. The result performed that the nine validated loci were significantly higher than random occurrence (p < 0.0001).Functional annotationsFunctional annotation revealed that most of the candidate SNPs located in intergenic or intronic region (Supplementary Tables 8–11). Among these loci, we detected a total of 92 candidate SNPs had CADD > 12.37, indicating their detrimental effects (Supplementary Tables 8–11). Distinct loci were mapped to 545 protein coding genes using the three-way gene mapping strategy (Supplementary Tables 12–15). Totally, we detected 42 gene-sets significantly enriched with the genes mapped to the loci shared between hypertension and GrimAge (Supplementary Table 16), HannumAge (Supplementary Table 17), and IEAA (Supplementary Table 18). GO analysis of genes located in loci shared between hypertension and IEAA revealed significant enrichment in biological processes and molecular function associated with the sensory perception of smell, nervous system processes, and odorant binding (Supplementary 18). The gene-set analysis of shared loci between hypertension and PhenoAge is underpowered. Finally, we analyzed the differential gene expression patterns of the mapped genes across 54 GTEx tissue types (Supplementary Figs. 2–5). Tissue enrichment for hypertension and HannumAge indicated that the differential gene expression patterns of the mapped genes were significantly up-regulated in bladder and colon sigmoid (Supplementary Fig. 3). Otherwise, we did not find any other significant tissue enrichment between hypertension and the other three epigenetic clocks (Supplementary Figs. 2, 4, and 5).
Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci
