DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples

Jung, Seong‐Ook; Lim, Seungyup; Hong, Sae Rom; Lee, Eun Hee; Shin, Kyoung Jin; Lee, Hwan Young

doi:10.1016/j.fsigen.2018.09.010

Cited by 127 publications

(151 citation statements)

References 23 publications

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“…Only the ELOVL2 gene showed highly significant age-correlation values for all the selected CpG sites reflecting a similar strength on the change in DNA methylation with chronological age. This result is in concordance with previous reports showing ELOVL2 as the most strong age predictor locus across different tissues (5,13,16,27). The remaining genes EDARADD, FHL2, PDE4C, and C1orf132 reflect lower correlations between DNA methylation and age, with several CpG sites revealing moderate or absence on age dependency.…”

Section: Discussionsupporting

confidence: 92%

“…The cross‐validation of the training set leads to a MAD of 7.22 years and evaluation of model performance using an independent test set of 19 individuals showed a MAD of 8.84 years. Previous forensic studies, most of them with blood samples of living individuals, using different loci and different number of markers, covering different age ranges and using different methodologies, gave values of MAD between 3.5 to 7.5 years . Thus, in our study the obtained MAD value, although less accurate, is in the range of other research approaches.…”

Section: Discussionmentioning

confidence: 46%

“…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).…”

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confidence: 99%

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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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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 46%

mentioning

confidence: 99%

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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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“…A total of three age-correlated genes were used for comparative purposes: ELOVL2, FHL2, and MIR29B2 -loci that have been commonly included in age prediction models analyzing bloodbased DNA samples (Garagnani et al, 2012;Bekaert et al, 2015;Zbieć-Piekarska et al, 2015;Freire-Aradas et al, 2016;Park et al, 2016;Zubakov et al, 2016;Hong et al, 2019;Jung et al, 2019). In the present study, these three genes were analyzed by three independent laboratories using four DNA methylation technologies: EpiTYPER R , pyrosequencing, MiSeq, and SNaPshot TM .…”

Section: Cpg Sites Selection and Dna Methylation Detectionmentioning

confidence: 99%

“…From a clinical point of view, biological age estimation may help to determine the life expectancy of the individual (Horvath and Raj, 2018). In order to infer the chronological age, several age prediction models have been developed based on data generated using different DNA methylation technologies, including EpiTYPER R (Xu et al, 2015;Freire-Aradas et al, 2016;Zubakov et al, 2016), pyrosequencing (Weidner et al, 2014;Bekaert et al, 2015;Zbieć-Piekarska et al, 2015), massively parallel sequencing (MPS) (Naue et al, 2017;Vidaki et al, 2017;Aliferi et al, 2018) and SNaPshot TM (Lee et al, 2015;Hong et al, 2017;Jung et al, 2019) systems. As DNA methylation is quantitative in nature, potential differences in DNA methylation levels can be detected by each technology.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

et al. 2020

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Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual’s lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3–4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER ® , pyrosequencing, MiSeq, and SNaPshot TM . The DNA methylation levels detected for CpG sites from ELOVL2 , FHL2 , and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER ® , pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshot TM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

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Genetic analyzer‐dependent DNA methylation detection and its application to existing age prediction models

Lee

2021

Electrophoresis

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DNA methylation is the most promising biomarker for estimating human age. There are various methods used for analyzing DNA methylation. Among those, the SNaPshot assay‐based method provides a semi‐quantitative measurement of DNA methylation using capillary electrophoresis on genetic analyzers. However, DNA methylation measures produced using different types of genetic analyzers have never been compared, although differences in methylation values can directly affect age estimates. To evaluate the differences between the results generated by different genetic analyzers, we analyzed the same blood, saliva, and control methylated DNA using three genetic analyzers—the Applied Biosystems 3130, 3500, and SeqStudio—and compared the methylation values at five CpG sites: ELOVL2, FHL2, KLF14, MIR29B2C, and TRIM59. The methylation value at each of the five CpG sites decreased in the order 3130, 3500, and SeqStudio. The differences in the results produced by the different genetic analyzers resulted in significant errors when applying the 3500 and SeqStudio data to a previous age estimation model constructed using the 3130 Genetic Analyzer data. Therefore, DNA methylation measurements from 3500 and SeqStudio were corrected using the regression functions obtained by plotting the DNA methylation data of one instrument versus the other to facilitate the application of DNA methylation data from one instrument to the age prediction model based on other instruments. The age prediction accuracy obtained by applying corrected 3500 and SeqStudio data to the existing age estimation model was as high as observed in the 3130 data.

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DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples

Cited by 127 publications

References 23 publications

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

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

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

Genetic analyzer‐dependent DNA methylation detection and its application to existing age prediction models

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