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ABSTRACT

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent methylation changes occur regionally across clusters of CpG sites either stochastically or in a coordinated block-like manner. Deep learning of single-molecule patterns from two genomic loci predicts chronological age with a median accuracy of 1.36-1.7 years on held-out samples, dramatically improving current clocks. Predictions are robust to sex, smoking, BMI, and biological age measures. Longitudinal 10-year analysis shows that early deviations from predicted age persist throughout life, and subsequent changes faithfully record time. Strikingly, accurate chronological age predictions are possible using as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues and facilitate medical and forensic applications.

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