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

AbstractAs a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included “robust CpGs” prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for “robust” CpG markers—which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

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DNA methylation-based forensic age estimation in human bone

10.1101/801647 ◽

2019 ◽

Cited By ~ 2

Author(s):

Shyamalika Gopalan ◽

Jonathan Gaige ◽

Brenna M. Henn

Keyword(s):

Dna Methylation ◽

Soft Tissues ◽

Prediction Models ◽

Epigenetic Modification ◽

Skeletal Remains ◽

Forensic Identification ◽

Published Data ◽

Lasso Regression ◽

Potential Applications ◽

Age Prediction

AbstractDNA methylation is an epigenetic modification of cytosine nucleotides that represents a promising suite of aging markers with broad potential applications. In particular, determining an individual’s age from their skeletal remains is an enduring problem in the field of forensic anthropology, and one that epigenetic markers are particularly well-suited to address. However, all DNA methylation-based age prediction methods published so far focus on tissues other than bone. While high accuracy has been achieved for saliva, blood and sperm, which are easily accessible in living individuals, the highly tissue-specific nature of DNA methylation patterns means that age prediction models trained on these particular tissues may not be directly applicable to other tissues. Bone is a prime target for the development of DNA methylation-based forensic identification tools as skeletal remains are often recoverable for years post-mortem, and well after soft tissues have decomposed. In this study, we generate genome-wide DNA methylation data from 32 individual bone samples. We analyze this new dataset alongside published data from 133 additional bone donors, both living and deceased. We perform an epigenome-wide association study on this combined dataset to identify 108 sites of DNA methylation that show a significant relationship with age (FDR < 0.05). We also develop an age-prediction model using lasso regression that produces highly accurate estimates of age from bone spanning an age range of 49-112 years. Our study demonstrates that DNA methylation levels at specific CpG sites can serve as powerful markers of aging, and can yield more accurate predictions of chronological age in human adults than morphometric markers.

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Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation

Forensic Science International Genetics ◽

10.1016/j.fsigen.2018.02.008 ◽

2018 ◽

Vol 34 ◽

pp. 128-133 ◽

Cited By ~ 13

Author(s):

Inge Smeers ◽

Ronny Decorte ◽

Wim Van de Voorde ◽

Bram Bekaert

Keyword(s):

Dna Methylation ◽

Prediction Models ◽

Statistical Prediction ◽

Age Prediction

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

Electrophoresis ◽

10.1002/elps.202000312 ◽

2021 ◽

Author(s):

Moon Hyun So ◽

Hwan Young Lee

Keyword(s):

Dna Methylation ◽

Prediction Models ◽

Genetic Analyzer ◽

Age Prediction

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Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing

Scientific Reports ◽

10.1038/s41598-019-45197-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Antoine Daunay ◽

Laura G. Baudrin ◽

Jean-François Deleuze ◽

Alexandre How-Kit

Keyword(s):

Dna Methylation ◽

Prediction Models ◽

Methylation Analysis ◽

Dna Methylation Analysis ◽

Age Prediction

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Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter

Scientific Reports ◽

10.1038/s41598-020-72567-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Imene Garali ◽

Mourad Sahbatou ◽

Antoine Daunay ◽

Laura G. Baudrin ◽

Victor Renault ◽

...

Keyword(s):

Dna Methylation ◽

Prediction Models ◽

Single Locus ◽

Training Set ◽

Pooled Data ◽

Locus Model ◽

Age Dependent ◽

Statistical Approaches ◽

Age Prediction ◽

Testing Set

Abstract Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

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Age determination through DNA methylation patterns of fingernails and toenails

10.1101/2021.12.15.472741 ◽

2021 ◽

Author(s):

Kristina Fokias ◽

Lotte Dierckx ◽

Wim Van de Voorde ◽

Bram Bekaert

Keyword(s):

Dna Methylation ◽

Least Squares ◽

Prediction Models ◽

Methylation Status ◽

Age Determination ◽

Ordinary Least Squares ◽

Chronological Age ◽

Post Mortem ◽

Methylation Patterns ◽

Age Prediction

Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0 – 96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence limb-specific age prediction models were developed using ordinary least squares, weighted least squares and quantile regression analysis. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 6.71 to 8.48 years. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.

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