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)
Summary
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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