Genome‐wide identification of age‐related CpG sites for age estimation from blood DNA of Han Chinese individuals

Abstract

Age‐related CpG (AR‐CpG) sites are currently the most promising molecular markers for forensic age estimation. However, the AR‐CpG sites of Han Chinese population remains to be systematically characterized. In this study, we performed genome‐wide methylation analyses on 42 whole blood DNA from healthy Han Chinese volunteers (aged from 18 to 62 years) using the Illumina MethylationEPIC BeadChip microarray. As expected, both known and novel AR‐CpG sites were identified. Considering the sex difference in aging rate, we then separately selected AR‐CpG candidates and built pyrosequencing‐based multiple linear regression models for age estimation of males and females. The model constructed from the male sample group (n = 167, aged from 1.50 to 85.71 years) explained 95.22% of variation in age using five AR‐CpG sites (chr6:11044864 ELOVL2, chr1:207997068 C1orf132, cg19283806 CCDC102B, cg17740900, and chr10:73740306 CHST3) and yielded a mean absolute error (MAE) of 2.79 years. The model constructed from the female sample group (n = 141, aged from 3.33 to 80.38 years) explained 94.90% of variation in age with six AR‐CpG sites (chr6:11044867 ELOVL2, chr1:207997060 C1orf132, chr2:106015757 FHL2, cg26947034, chr16: 67184108 B3GNT9, and chr20:44658203 SLC12A5) and yielded an MAE of 2.53 years. Besides, the estimated age was highly correlated with the actual age (R > 0.97). The robustness of these AR‐CpG markers was demonstrated by 10‐fold cross‐validations. In conclusion, we updated the AR‐CpG sites of Han Chinese population and provided two sets of AR‐CpG sites for accurate age estimation.

Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm

“…It shall be noted that previous studies presented highly compact chronological epigenetic clocks, sometimes involving as few as three CpGs. Nonetheless, these compact models presented inferior accuracy, with median absolute errors of 5-21 years on our Illumina 450K and sequencing data 12,13,[16][17][18]47 . Conversely, we provide an epigenetic clock that is more accurate than commonly used models 1,2,10 , and at the same time compact enough to allow direct measurement using multiplex targeted PCR, making these models simpler and more accessible compared to DNA methylation arrays.…”

“…Few non-linear models were introduced, including those using neural networks 14,15 . The high cost of the Illumina BeadChip platforms hinders accessible use of such chronological age predictors, and thus several less-accurate clocks trained on small pyrosequencing datasets with few CpG sites were also proposed, mostly for forensic use [16][17][18] .…”

Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models

“…The bibliographic search strategies were developed according to the DNAm-based age prediction studies with MAD values less than 5 years between 2014 and 2021, and we collected a cohort of 7,084 individuals from 16 countries or populations ( Weidner et al, 2014 ; Bekaert et al, 2015 ; Xu et al, 2015 ; Zbieć-Piekarska et al, 2015a ; Zbieć-Piekarska, et al, 2015b ; Park et al, 2016 ; Zubakov et al, 2016 ; Cho et al, 2017 ; Feng et al, 2018 ; Alsaleh and Haddrill, 2019 ; Daunay et al, 2019 ; Jung et al, 2019 ; Li et al, 2019 ; Dias et al, 2020 ; Garali et al, 2020 ; Lau and Fung, 2020 ; Pan et al, 2020 ; Piniewska-Róg et al, 2021 ; Sukawutthiya et al, 2021 ; Woźniak et al, 2021 ; Xiao et al, 2021 ). The correlation coefficient ( r ) ranking of nine age-associated genes was obtained by meta-analyses ( Figure 1 and Supplementary Figure S1 ).…”

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

“… 23 , 24 , 25 The high cost of the Illumina BeadChip platforms, compared with targeted sequencing via targeted-PCR or capture panels, hinders accessible use of such chronological age predictors, and thus several less-accurate clocks trained on small pyrosequencing datasets with few CpG sites were also proposed, mostly for forensic applications. 22 , 26 , 27 , 28 , 29 Additionally, developments in single-cell DNA methylation assays recently led to novel epigenetic clocks for mice. 30 , 31 As these data are extremely sparse, typically with a whole-genome coverage of 0.25x per cell, estimating the methylation level of individual CpG sites is almost impossible and alternative approaches based on multiple CpG sites were developed in these works.…”