Abstract

BackgroundAge-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis.ResultsIn this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample.ConclusionTargeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.

Highlights

  • During aging, DNA methylation (DNAm) is continuously lost or gained at specific CG nucleotides (CpG sites) of our genome

  • Selection of age-associated CpGs for blood To select age-associated CpGs for further comparison, we used a training set of 973 DNAm profiles of healthy human blood samples (1 to 101 years old), which are derived from seven different studies and based on the 450 k Illumina BeadChip platform (Additional file 1: Figure S1A)

  • This model provided a high correlation with chronological age in the training set (R2 = 0.95; median error = 3.0 years; Fig. 1b) and in an independent validation set of 3674 blood samples of five different studies (R2 = 0.82; median error = 3.3 years; Fig. 1c)

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Summary

Introduction

DNA methylation (DNAm) is continuously lost or gained at specific CG nucleotides (CpG sites) of our genome. Epigenetic clocks raise hopes as a biomarker in forensic medicine, to determine the donor age of an unknown specimen or of a person with an allegedly unknown age [2]. To translate epigenetic biomarkers into an approved medical test, it is advantageous to select a manageable set of informative genomic regions, which can be targeted by DNA methylation assays that are sufficiently fast, cheap, robust, and widely available for clinical diagnostics [11, 12]. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis

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