DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers

Hong, Sae Rom; Jung, Seong‐Ook; Lee, Eun Hee; Shin, Kyoung Jin; Yang, Woo Ick; Lee, Hwan Young

doi:10.1016/j.fsigen.2017.04.006

Cited by 132 publications

(118 citation statements)

References 38 publications

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“…For targeted analysis of specific age-associated CpGs, various studies described epigenetic agepredictors based on bisulfite pyrosequencing (Weidner et al 2014;Zbieć-Piekarska et al 2015) or by the Sequenom's EpiTYPER assay (Garagnani et al 2012). Single Base Primer Extension Assay (SNaPshot) was also used by many laboratories for epigenetic age prediction, but the accuracy is apparently lower (Lee et al 2015;Hong et al 2017). Recently, droplet digital PCR (ddPCR) was reported to enable precise DNAm measurements (Yu et al 2015;Zemmour et al 2018), and hence it might facilitate epigenetic age predictions without PCR bias.…”

Section: Introductionmentioning

confidence: 99%

New Targeted Approaches for Epigenetic Age Predictions

Han

Franzen

Stiehl

et al. 2019

Preprint

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Aging causes epigenetic modifications, which are utilized as a 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 highthroughput analysis. In this study, we utilized 4,650 Illumina BeadChip datasets of blood to select the best suited CpG sites for targeted analysis. DNA methylation analysis at these sites with either pyrosequencing or droplet digital PCR (ddPCR) revealed a high correlation with chronological age.In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower precision at individual CpGs. However, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often accompanied by a CTCF binding site at the peak. 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 an alternative model 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. Thus, the stochastic evolution of age-associated DNA methylation patterns, which seems to resemble epigenetic drift, enables epigenetic clocks for individual DNA strands.

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Section: Introductionmentioning

confidence: 99%

New Targeted Approaches for Epigenetic Age Predictions

Han

Franzen

Stiehl

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Several DNA methylation markers have been investigated in various tissues and body fluids using DNA-based methodologies such as bisulfite pyrosequencing (3)(4)(5)(6)(7)(8)(9), EpiTYPER technology (10), massively parallel sequencing (11)(12)(13), or SNaPshot assays (14)(15)(16).This allowed the identification of many CpG markers showing high correlations with chronological age, potentially useful as forensic age predictors. Thus, a number of high accurate age prediction models have been proposed for specific tissues, including blood (6,7,12), teeth (17), buccal swabs (8) or saliva (15), or as multi-tissue models (13,16,18,19).…”

mentioning

confidence: 99%

Age Estimation Based on DNA Methylation Using Blood Samples From Deceased Individuals

Dias

Cordeiro

Real

et al. 2019

Journal of Forensic Sciences

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Age estimation using DNA methylation levels has been widely investigated in recent years because of its potential application in forensic genetics. The main aim of this study was to develop an age predictor model (APM) for blood samples of deceased individuals based in five age‐correlated genes. Fifty‐one samples were analyzed through the bisulfite polymerase chain reaction (PCR) sequencing method for DNA methylation evaluation in genes ELOVL2, FHL2, EDARADD, PDE4C, and C1orf132. Linear regression was used to analyze relationships between methylation levels and age. The model using the highest age‐correlated CpG from each locus revealed a correlation coefficient of 0.888, explaining 76.3% of age variation, with a mean absolute deviation from the chronological age (MAD) of 6.08 years. The model was validated in an independent test set of 19 samples producing a MAD of 8.84 years. The developed APM seems to be informative and could have potential application in forensic analysis.

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“…A total of 319,607 probes (No Pruned Set) passed our quality control and 128,405 probes (Pruned Set) were retained after pruning based upon the pairwise correlation of probes (see next section). To test the performance of age predictors in non-blood tissues, we downloaded 13 cohorts from GEO database with accession ID GSE61431 (brain) 37 , GSE59685 (brain) 38 GSE80970 (brain), GSE101961 (breast) 39 , GSE108213 (breast), GSE48325 (liver) 40 , GSE61257 (adipose) 41 , GSE61258 (liver) 41 , GSE61259 (breast) 41 , GSE88883 (breast) 42 , GSE90060 (endometrium) 43 , GSE92767 (saliva) 44 , GSE99029 (saliva) 45…”

Section: Methodsmentioning

confidence: 99%

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

Zhang

et al. 2018

Preprint

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DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvath’s across-tissue age predictor.

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DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers

Cited by 132 publications

References 38 publications

New Targeted Approaches for Epigenetic Age Predictions

New Targeted Approaches for Epigenetic Age Predictions

Age Estimation Based on DNA Methylation Using Blood Samples From Deceased Individuals

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

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