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Research Paper|Volume 10, Issue 5|pp 1015—1026

Age prediction of children and adolescents aged 6-17 years: an epigenome-wide analysis of DNA methylation

Chunxiao Li1, Wenjing Gao1, Ying Gao1, Canqing Yu1, Jun Lv1, Ruoran Lv2, Jiali Duan2, Ying Sun2, Xianghui Guo3, Weihua Cao1, Liming Li1
  • 1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
  • 2Beijing Center for Disease Control and Prevention, Beijing 100013, China
  • 3Chaoyang District Center for Disease Control and Prevention, Beijing 100021, China
Received: January 10, 2018Accepted: May 8, 2018Published: May 12, 2018
https://doi.org/10.18632/aging.101445

Copyright: © 2018 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The DNA methylation age, a good reflection of human aging process, has been used to predict chronological age of adults and newborns. However, the prediction model for children and adolescents was absent. 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. We identified 6,350 age-related CpGs from the epigenome-wide association analysis (N=179). 116 known age-related sites in children were confirmed. 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). The predictive accuracy in the testing dataset (N=89) was high (correlation=0.93, error=0.62 years). Among the 83 predictors, 49 sites were novel probes not existing on the Illumina 450K BeadChip. The top two predictors of age were on the PRKCB and REG4 genes, which are associated with diabetes and cancer, respectively. 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.