Evidence for differences in DNA methylation between Germans and Japanese

Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing

Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

DNA methylation-based age estimation and quantification of the degradation levels of bisulfite-converted DNA

DNA methylation modifications are known to influence epigenetic phenomena and have been a focus of forensic science research for some time. Degraded DNA after bisulfite treatment is widely used in DNA methylation analysis. In this study, we analyzed methylation levels at 12 CpG sites of four selected genomic regions by pyrosequencing after bisulfite treatment. DNA was extracted from buccal swab samples collected from 102 Japanese individuals who were 21-77 years old. We also developed a simple method to quantify the degradation levels of bisulfite-converted DNA by real-time PCR, and evaluated the effect of DNA degradation on age estimation. We found that the methylation levels and chronological ages were highly correlated in the four selected regions, and the mean absolute deviation (MAD) between chronological and estimated ages was low at 3.88 years. These results indicated that pyrosequencing analysis at the 12 CpGs was useful for age estimation in the Japanese population. To develop a sensitive quantification method, we analyzed the amplification efficiency of short and long fragments from 10 regions by real-time PCR. The amplification efficiency was highest for CCDC102B, and the degradation levels of bisulfite-converted DNA for the 102 samples were categorized as moderately or heavily degraded. For the younger age groups (20-49 years), the MADs were lower for moderately degraded DNA than they were for heavily degraded DNA. This finding indicates that degradation levels affected the accuracy of age estimation in most of the samples; the exception was the samples from the 50-77 years age group.

Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA

Age estimation can be useful information for narrowing down candidates of unidentified donors in criminal investigations. Various age estimation models based on DNA methylation biomarkers have been developed for forensic usage in the past decade. However, many of these models using ordinary least squares regression cannot generate an appropriate estimation due to the deterioration in prediction accuracy caused by an increased prediction error in older age groups. In the present study, to address this problem, we developed age estimation models that set an appropriate prediction interval for all age groups by two approaches: a statistical method using quantile regression (QR) and a machine learning method using an artificial neural network (ANN). Methylation datasets (n = 1280, age 0-91 years) of the promoter for the gene encoding ELOVL fatty acid elongase 2 were used to develop the QR and ANN models. By validation using several test datasets, both models were shown to enlarge prediction intervals in accordance with aging and have a high level of correct prediction (>90 %) for older age groups. The QR and ANN models also generated a point age prediction with high accuracy. The ANN model enabled a prediction with a mean absolute error (MAE) of 5.3 years and root mean square error (RMSE) of 7.3 years for the test dataset (n = 549), which were comparable to those of the QR model (MAE = 5.6 years, RMSE = 7.8 years). Their applicability to casework was also confirmed using bloodstain samples stored for various periods of time (1-14 years), indicating the stability of the models for aged bloodstain samples. From these results, it was considered that the proposed models can provide more useful and effective age estimation in forensic settings.

Investigation of the impact of biogeographic ancestry on DNA methylation based age predictions comparing a Middle East and a Central European population

DNA methylation based age prediction is a new method in the toolbox of forensic genetics. Typically, the method is applied in the course of police investigation e.g. to predict the age of an unknown person that has left a biological trace at a crime scene. The method can also be used to answer other forensic questions, for example to estimate the age of unknown human bodies in the course of the identification process. In the present study, we tested for a potential impact of biogeographic ancestry (BGA) on age predictions using five age dependent methylated CpG sites within the genetic regions of ELOVL2, MIR29B2CHG, FHL2, KLF14 and TRIM59. We collected 102 blood samples each from donors living in Iraq, Middle East (ME) and Germany, Central Europe (EU). Both sample sets were matched in sex and age ranging from 18 to 68 years with exactly one male and female sample per year of age. All samples were analyzed by bisulfite pyrosequencing applying a multiplex pre-amplification strategy based on a single input of 35 ng converted DNA in the PCR. For the CpGs in MIR29B2CHG, FHL2 and KLF14, we observed significantly different methylation levels between the two populations. While we were able to train two highly accurate prediction models for the respective population with mean absolute deviations between predicted and actual ages (MAD) of 3.34 years for the ME model, and 2.72 years for the EU model, we found an absolute prediction difference between the two population specific models of more than 4 years. A combined model for both populations compensated the methylation difference between the two populations, providing MADs of prediction of only 3.81 years for ME and 3.31 years for EU samples. In total, the results of the present study strongly support the benefit of BGA information for more reliable methylation based age predictions.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age

Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from DNA of crime scene samples, to provide investigative leads to help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all of its three components, which we summarize in this review article. Appearance prediction from DNA has broadened beyond eye, hair and skin color to additionally comprise other traits such as eyebrow color, freckles, hair structure, hair loss in men, and tall stature. Biogeographic ancestry inference from DNA has progressed from continental ancestry to sub-continental ancestry detection and the resolving of co-ancestry patterns in genetically admixed individuals. Age estimation from DNA has widened beyond blood to more somatic tissues such as saliva and bones as well as new markers and tools for semen. Technological progress has allowed forensically suitable DNA technology with largely increased multiplex capacity for the simultaneous analysis of hundreds of DNA predictors with targeted massively parallel sequencing (MPS). Forensically validated MPS-based FDP tools for predicting from crime scene DNA i) several appearance traits, ii) multi-regional ancestry, iii) several appearance traits together with multi-regional ancestry, and iv) age from different tissue types, are already available. Despite recent advances that will likely increase the impact of FDP in criminal casework in the near future, moving reliable appearance, ancestry and age prediction from crime scene DNA to the level of detail and accuracy police investigators may desire, requires further intensified scientific research together with technical developments and forensic validations as well as the necessary funding.

An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review

The prediction of chronological age from methylation-based biomarkers represents one of the most promising applications in the field of forensic sciences. Age-prediction models developed so far are not easily applicable for forensic caseworkers. Among the several attempts to pursue this objective, the formulation of single-locus models might represent a good strategy. The present work aimed to develop an accurate single-locus model for age prediction exploiting ELOVL2, a gene for which epigenetic alterations are most highly correlated with age. We carried out a systematic review of different published pyrosequencing datasets in which methylation of the ELOVL2 promoter was analysed to formulate age prediction models. Nine of these, with available datasets involving 2298 participants, were selected. We found that irrespective of which model was adopted, a very strong relationship between ELOVL2 methylation levels and age exists. In particular, the model giving the best age-prediction accuracy was the gradient boosting regressor with a prediction error of about 5.5 years. The findings reported here strongly support the use of ELOVL2 for the formulation of a single-locus epigenetic model, but the inclusion of additional, non-redundant markers is a fundamental requirement to apply a molecular model to forensic applications with more robust results.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

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130 CpGs have been used by two or more aging clocks applied to human buccal tissue

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Common CpG genes are linked to the adaptive immune system and telomere maintenance

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Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

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Several compounds associated with common CpG genes regulate lifespan in animals

Epigenome-wide screening of CpG markers to develop a multiplex methylation SNaPshot assay for age prediction

Age prediction can provide important information about the contributors of biological evidence left at crime scenes. DNA methylation has been regarded as the most promising age-predictive biomarker. Measuring themethylation level at the genome-wide scaleis an important step to screen specific markers for forensic age prediction. In present study, we screened out five age-related CpG sites from the public EPIC BeadChip data and evaluated them in a training set (115 blood) by multiplex methylation SNaPshot assay. Through full subset regression, the five markers were narrowed down to three, namely cg10501210 (C1orf132), cg16867657 (ELOVL2), and cg13108341 (DNAH9), of which the last one was a newly discovered age-related CpG site. An age prediction model was built based on these three markers, explaining 86.8% of the variation of age with a mean absolute deviation (MAD) of 4.038 years. Then, the multiplex methylation SNaPshot assay was adjusted according to the age prediction model. Considering that bloodstains are one of the most common biological samples in practical cases, three validation sets composed of 30 blood, 30 fresh bloodstains and 30 aged bloodstains were used for evaluation of the age prediction model. The MAD of each set was estimated as 4.734, 4.490, and 5.431 years, respectively, suggesting that our age prediction model was applicable for age prediction for blood and bloodstains in Chinese Han population of 11-71 age. In general, this study describes a workflow of screening CpG markers from public chip data and presents a 3-CpG markers model for forensic age prediction.

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman's rank correlation. Differences in DNAm between the groups "healthy children" and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

How to Translate DNA Methylation Biomarkers Into Clinical Practice

Recent advances in sequencing technologies provide unprecedented opportunities for epigenetic biomarker development. Particularly the DNA methylation pattern-which is modified at specific sites in the genome during cellular differentiation, aging, and disease-holds high hopes for a wide variety of diagnostic applications. While many epigenetic biomarkers have been described, only very few of them have so far been successfully translated into clinical practice and almost exclusively in the field of oncology. This discrepancy might be attributed to the different demands of either publishing a new finding or establishing a standardized and approved diagnostic procedure. This is exemplified for epigenetic leukocyte counts and epigenetic age-predictions. To ease later clinical translation, the following hallmarks should already be taken into consideration when designing epigenetic biomarkers: 1) Identification of best genomic regions, 2) pre-analytical processing, 3) accuracy of DNA methylation measurements, 4) identification of confounding parameters, 5) accreditation as diagnostic procedure, 6) standardized data analysis, 7) turnaround time, and 8) costs and customer requirements. While the initial selection of relevant genomic regions is usually performed on genome wide DNA methylation profiles, it might be advantageous to subsequently establish targeted assays that focus on specific genomic regions. Development of an epigenetic biomarker for clinical application is a long and cumbersome process that is only initiated with the identification of an epigenetic signature.