Distinct DNA methylation changes highly correlated with chronological age in the human brain

Abstract

Methylation at CpG sites is a critical epigenetic modification in mammals. Altered DNA methylation has been suggested to be a central mechanism in development, some disease processes and cellular senescence. Quantifying the extent and identity of epigenetic changes in the aging process is therefore potentially important for understanding longevity and age-related diseases. In the current study, we have examined DNA methylation at >27,000 CpG sites throughout the human genome, in frontal cortex, temporal cortex, pons and cerebellum from 387 human donors between the ages of 1 and 102 years. We identify CpG loci that show a highly significant, consistent correlation between DNA methylation and chronological age. The majority of these loci are within CpG islands and there is a positive correlation between age and DNA methylation level. Lastly, we show that the CpG sites where the DNA methylation level is significantly associated with age are physically close to genes involved in DNA binding and regulation of transcription. This suggests that specific age-related DNA methylation changes may have quite a broad impact on gene expression in the human brain.

Figures

Figure 1.

Manhattan plot showing association between methylation at individual CpG sites and chronological age. Plotted are P-values indicating strength of association between DNA methylation levels at >27 000 CpG sites and age in cerebellum (purple), frontal cortex (green), pons (blue) and temporal cortex (red). For each point, a positive association between DNA methylation and chronological age is indicated by upward pointing triangles; a negative association is indicated by downward pointing triangles.

Figure 2.

Covariate-adjusted methylation levels in cerebellum (purple), frontal cortex (green), pons (blue) and temporal cortex (red) for 10 CpG sites where methylation levels increase significantly with age in all four brain regions (based on a Bonferroni threshold for significance of P = 1.8 × 10−6). Notably for all 10 loci that met our conservative threshold for significance, methylation levels were positively associated with age.

Figure 3.

An excess of CpG sites where DNA methylation is positively associated with age exists in significant results.

Figure 4.

Ternary plots showing concordance of combined P-values from phase I analyses across all four brain regions stratified by CpG island or non-island status. (A–H) The combined magnitude of association per CpG site for all possible combinations of brain regions. The cumulative log10 P-value is the additive combination of P-values (transformed to a −log10 scale) across three brain regions spatially indicated in the ternary plots. For each side of the ternary plots, the 0–1 scale is related to the untransformed P-values, with each side labeled per brain region analyzed in the discovery phase of analyses. These plots show that most robust associations, with the largest combined P-values across multiple brain regions (on the −log10 scale) occur at CpG islands.

Figure 5.

Data from stage II comparing results from cerebellum and frontal cortex, showing that significant results across two or more tissues often occur at similar methylation levels across various tissues, seemingly robust to the magnitude of methylation. (A) The dispersion of all results in stage II across varying levels of methylation in both tissue types regardless of statistical significance, whereas (F) presents the same data but also overlays the subsets of the data shown in (B)–(E). (B)–(E) describe subsets of results significant across a number of brain regions evident in the panel labels within the figure. The cumulative log10 P-value is the additive combination of P-values (transformed to a −log10 scale) across brain regions for the same CpG site.

Figure 6.

A map of enriched functional clusters for genes proximal to age-associated CpG sites based on DAVID functional annotation clustering. This figure shows the inferred functional relatedness of clustered genes at a greater than 4-fold level of enrichment among significant results in phase I of this study, as described in Table 2. Line thickness connecting nodes indicates relative P-value of the term within the cluster, with the thickest line representing the most significant term (P = 1.6 × 10−19) and the thinnest lines representing the least significant term (P = 2.5 × 10−5). The numbers in the red ‘hub’ nodes denote the cluster number described in Table 2, with relative size of each node representing the count of each particular node from DAVID analyses, and nodes connected across clusters showing functional overlaps.