DNA methylation age of human tissues and cell types

Abstract

BackgroundIt is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.ResultsI developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.ConclusionsI propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

Reduced DNA methylation patterning and transcriptional connectivity define human skin aging

“…S7). On the same dataset, the published method (Horvath, 2013) achieved slightly inferior results in terms of correlation (ρ = 0.89, P  = 7.6e‐7, R ‐squared = 0.794), but again underestimated the biological age with an average absolute prediction error of 12.1 years (Fig. S7).…”

“…When we used the merged dataset ( N  = 108) to predict the age of our samples using a published predictor based on a set of 353 tissue‐independent probes (Horvath, 2013), we obtained a good correlation between chronological and predicted biological age (ρ = 0.91, P  < 2.2e‐16). However, when calculating the linear regression of the data, the regression curve showed a slope of only 0.55.…”

“…While the key features of epigenetic drift remain to be defined, several observations have been made that characterize age‐related methylation changes across human tissues. These include the hypermethylation of stem cell genes (Rakyan et al ., 2010; Teschendorff et al ., 2010), a general linear correlation between the methylation level of certain CpGs and the chronological age (Hannum et al ., 2013; Horvath, 2013; Weidner et al ., 2014), and the hypermethylation of CpG islands (Yuan et al ., 2015). The molecular and phenotypic consequences of these alterations remain a topic of active research.…”

Fountain of youth for squamous cell carcinomas? On the epigenetic age of non‐small cell lung cancer and corresponding tumor‐free lung tissues

“…In addition to Horvath's epigenetic clock, which relies on a comprehensive mathematical approach,4 additional age‐related changes in the DNA methylation values of multiple genes have been described 6. Given the alterations in the Horvath loci, we wondered whether DNA methylation patterns directly relate to age, indicating aberrant epigenome aging in NSCLC.…”

“…The epigenetic age (DNAm age) was calculated as described before4 using the DNAm age Calculator (accessible at https://dnamage.genetics.ucla.edu/).…”