Epigenetics & ChromatinDevelopmental BiologyPeer-reviewed journal article
Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns
Tamar ShahaliD, Elad SegeviD, Thomas KonstantinovskyiD, Yonit MarcusiD, Gabi SheferiD, Metsada Pasmanik‐ChoriD, Assaf BuchiD, Yuval EbensteiniD, Paul ZimmetiD, Naftali SterniD
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Abstract
BackgroundThe epigenetic age can now be extrapolated from one of several epigenetic clocks, which are based on age-related changes in DNA methylation levels at specific multiple CpG sites. Accelerated aging, calculated from the discrepancy between the chronological age and the epigenetic age, has shown to predict morbidity and mortality rate. We assumed that deconvolution of epigenetic age to its components could shed light on the diversity of epigenetic, and by inference, on inter-individual variability in the causes of biological aging. ResultsUsing the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the interpersonal variability in epigenetic aging, with CpG sites related to secretagogin and malin being the most variable. We show that equal epigenetic age in different subjects can result from variable contribution size of the same CpG sites to the total epigenetic age. In a healthy cohort, the most variable CpG sites are responsible for accelerated and decelerated epigenetic aging, relative to chronological age. ConclusionsOf the 353 CpG sites that form the basis for the Horvath epigenetic age, we have found the CpG sites that are responsible for accelerated and decelerated epigenetic aging in healthy subjects. However, the relative contribution of each site to aging varies between individuals, leading to variable personal aging patterns. Our findings pave the way to form personalized aging cards allowing the identification of specific genes related to CpG sites, as aging markers, and perhaps treatment of these targets in order to hinder undesirable age drifting.
- organism: human
- assay: DNA methylation
- research focus: deconvolution
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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