“*” means also on the Illumina 450K Beadchip. It was a new probe on the 850K Beadchip and related to familial Meniere's disease and diabetes. The largest coefficient effect was observed for cg00497086 (coefficient value=10.0) located in the body of the PRKCB (protein kinase C beta) gene on chromosome 16 and in the open sea. All the absolute coefficient values of the upper most 20 sites were over two, and all of them located on autosomal chromosomes. The top 20 CpG sites with the largest predicted effect values were presented in Table 2. 49 of the 83 age-predictive CpG sites were newly identified probes not existing on the 450K BeadChip array. Nearly half markers in the model lay within or near genes with known functions, such as diabetes, cancer, neurons function, oxidative stress, DNA damage, and other age-related conditions. In the training data, chronological age and DNAm age were highly correlated in the training dataset: r = 0.99, median error = 0.23 years.Īmong the age predictive features, 21 CpG sites were positively correlated with age while 62 CpG sites were negatively correlated with age (Table S 3). Correlation between Chronological age and DNAm age. Therefore, in the present study, we aimed to develop an age prediction model for children and adolescents using DNA methylation data of over 850,000 CpG sites from the Chinese National Twin Registry.įigure 2a. The accurate age prediction among children could potentially be applied to understand the development mechanism of children and to predict the risk of age-related phenotypes and diseases in adulthood. The DNA methylation age (DNAm age) has been proved to be associated with cancer and mortality. It is unknown whether the accuracy and precision of age prediction model in adults would be affected when used among children and adolescents. DNA methylation studies should be matched carefully to age. It has been revealed that age-related DNA methylation changed more rapidly during childhood and adolescence. However, the age prediction model for children and adolescents using DNA methylation biomarkers was scarce. The age prediction model using a group of age-specific CpG sites has been widely used in adults and newborns for age prediction. Several studies have identified age-related CpG sites in blood, but the results are inconsistent. It has been shown that the methylation levels at specific age-related CpG sites represent stable and reproducible biomarkers of age. Ī growing body of evidence confirmed the presence of age-related epigenome-wide DNA methylation patterns. Previous evidence suggested that global levels of DNA methylation increased over the first few years of life and then decreased in late adulthood, suggesting that epigenetic modifications might play a vital role in the human’s aging process. The mostly studied epigenetic marker is DNA methylation, the presence of methyl groups at CpG dinucleotides. Our results suggest that the chronological age can be accurately predicted among children and adolescents aged 6-17 years by 83 newly identified CpG sites.Įpigenetics refers to the molecular mechanisms regulating gene expression without changing the DNA sequence. The top two predictors of age were on the PRKCB and REG4 genes, which are associated with diabetes and cancer, respectively. Among the 83 predictors, 49 sites were novel probes not existing on the Illumina 450K BeadChip. The predictive accuracy in the testing dataset (N=89) was high (correlation=0.93, error=0.62 years). 83 novel CpGs were selected as predictors from all age-related loci by elastic net regression and they could accurately predict the chronological age of the pediatric population, with a correlation of 0.99 and the error of 0.23 years in the training dataset (N=90). 116 known age-related sites in children were confirmed. We identified 6,350 age-related CpGs from the epigenome-wide association analysis (N=179). In this study, we aimed to generate a prediction model of chronological age for children and adolescents aged 6-17 years by using age-specific DNA methylation patterns from 180 Chinese twin individuals. However, the prediction model for children and adolescents was absent. The DNA methylation age, a good reflection of human aging process, has been used to predict chronological age of adults and newborns.
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