Developing a DNA methylation assay for human age prediction in blood and bloodstain

Age prediction of an individual based on biological traces remained in a crime scene is of ultimate importance for criminal investigation. Growing evidence indicates that some CpG sites may have age-related methylation changes and thus may be a promising tool for age prediction. In this study, we utilized the pyrosequencing approach to screen age-related CpG (AR-CpG) sites for age prediction. Five AR-CpGs were identified as age-related markers from thirty-eight candidates, among which three CpG sites, ITGA2B_1, NPTX2_3, and NPTX2_4 were never reported in previous studies. We fit a linear regression model for age prediction based on methylation assay for 89 blood samples from donors aged 9–75 years old. The model included four AR-CpG markers in three gene fragments ASPA, ITGA2B and NPTX2 and enabled the age prediction with R² = 0.819. The mean absolute deviation (MAD) from chronological age of the model was 7.870. We validated the linear regression model with a validation set of 40 blood samples, and the prediction MAD was 7.986. There was no statistically significant difference in age prediction between 20 pairs of blood samples and bloodstains. Six pairs of fresh and old bloodstains were analyzed using our assay. The obtained results showed the assay still performed an effective prediction on bloodstains after four-month storage in room conditions. This study indicates that our DNA methylation assay is a reliable and effective method for age prediction for forensic purposes.     

Development of a forensically useful age prediction method based on DNA methylation analysis

Forensic DNA phenotyping needs to be supplemented with age prediction to become a relevant source of information on human appearance. Recent progress in analysis of the human methylome has enabled selection of multiple candidate loci showing linear correlation with chronological age. Practical application in forensic science depends on successful validation of these potential age predictors. In this study, eight DNA methylation candidate loci were analysed using convenient and reliable pyrosequencing technology. A total number of 41 CpG sites was investigated in 420 samples collected from men and women aged from 2 to 75 years. The study confirmed correlation of all the investigated markers with human age. The five most significantly correlated CpG sites in ELOVL2 on 6p24.2, C1orf132 on 1q32.2, TRIM59 on 3q25.33, KLF14 on 7q32.3 and FHL2 on 2q12.2 were chosen to build a prediction model. This restriction allowed the technical analysis to be simplified without lowering the prediction accuracy significantly. Model parameters for a discovery set of 300 samples were R² = 0.94 and the standard error of the estimate = 4.5 years. An independent set of 120 samples was used to test the model performance. Mean absolute deviation for this testing set was 3.9 years. The number of correct predictions ±5 years achieved a very high level of 86.7% in the age category 2–19 and gradually decreased to 50% in the age category 60–75. The prediction model was deterministic for individuals belonging to these two extreme age categories. The developed method was implemented in a freely available online age prediction calculator.     

Application of droplet digital PCR method for DNA methylation-based age prediction from saliva

The recent studies reported that DNA methylation markers show changes with age, and expected that the DNA methylation markers can be effectively used for estimation of age in forensic genetics. In this study, we applied droplet digital PCR (ddPCR) method to investigate the DNA methylation pattern in the CpG sites, and we constructed an age prediction model based on the ddPCR method. The ddPCR is capable of highly sensitive quantitation of nucleic acid and detection of sequence variations in gene by separating the sample into large number of partitions and clonally amplifying nucleic acids in each partition. We extracted DNA from saliva samples collected from several age groups. The DNA was bisulfite converted and subjected to ddPCR using specifically designed primers and probes. The methylation ratio of each sample was calculated and correlation between the methylation ratio and the chronological age was analyzed. In the results, methylated DNA ratio at the 4 CpG sites (cg14361627, cg14361627, cg08928145 and cg07547549) showed strong correlation with chronological age. Percent-methylation values at 4 CpG markers and chronological ages of the 76 individuals were analyzed by multiple regression analysis, and we constructed an age prediction model. We observed a strong correlation (Spearman’s rho = 0.922) between predicted and chronological ages of 76 individuals with a MAD from chronological age of 3.3 years. Collectively, the result in this study showed the potential applicability of ddPCR to predict age from saliva.     

Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting

Age prediction with epigenetic information is now edging closer to practical use in forensic community. Many age-related CpG (AR-CpG) sites have proven useful in predicting age in pyrosequencing or DNA chip analyses. In this study, a wide range methylation status in the ELOVL2 and FHL2 promoter regions were detected with methylation-sensitive high resolution melting (MS-HRM) in a labor-, time-, and cost-effective manner. Non-linear-distributions of methylation status and chronological age were newly fitted to the logistic curve. Notably, these distributions were revealed to be similar in 22 living blood samples and 52 dead blood samples. Therefore, the difference of methylation status between living and dead samples suggested to be ignorable by MS-HRM. Additionally, the information from ELOVL2 and FHL2 were integrated into a logistic curve fitting model to develop a final predictive model through the multivariate linear regression of logit-linked methylation rates and chronological age with adjusted R² = 0.83. Mean absolute deviation (MAD) was 7.44 for 74 training set and 7.71 for 30 additional independent test set, indicating that the final predicting model is accurate. This suggests that our MS-HRM-based method has great potential in predicting actual forensic age.     

Platform-independent models for age prediction using DNA methylation data

Age prediction has been in the spotlight recently because it can provide an important information about the contributors of biological evidence left at crime scenes. Specifically, many researchers have actively suggested age-prediction models using DNA methylation at several CpG sites and tested the candidates using platforms such as the HumanMethylation 450 array and pyrosequencing. With DNA methylation data obtained from each platform, age prediction models were constructed using diverse statistical methods typically with multivariate linear regression. However, because each developed model is based on single-platform data, the prediction accuracy is reduced when applying DNA methylation data obtained from other platforms. In this study, bisulfite sequencing data for 95 saliva samples were generated using massively parallel sequencing (MPS) and compared with methylation SNaPshot data from the same 95 individuals. The predicted age obtained by applying MPS data to an age-prediction model built for methylation SNaPshot data differed greatly from the chronological age due to platform differences. Therefore, novel variables were introduced to indicate the platform type, and construct platform-independent age predictive models using a neural network and multivariate linear regression. The final neural network model had a mean absolute deviation (MAD) of 3.19 years between the predicted and chronological age, and the mean absolute percentage error (MAPE) was 8.89% in the test set. Similarly, the linear regression model showed 3.69 years of MAD and 10.44% of MAPE in the same test set. The platform-independent age-prediction model was made extensible to an increasing number of platforms by introducing platform variables, and the idea of platform variables can be applied to age prediction models for other body fluids.     

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic ‘DNA intelligence’ tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.     

Independent validation of DNA-based approaches for age prediction in blood

Numerous molecular biomarkers have been proposed as predictors of chronological age. Among them, T-cell specific DNA rearrangement and DNA methylation markers have been introduced as forensic age predictors in blood because of their high prediction accuracy. These markers appear highly promising, but for better application to forensic casework sample analysis the proposed markers and genotyping methods must be tested further. In the current study, signal-joint T-cell receptor excision circles (sjTRECs) and DNA methylation markers located in the ELOVL2, C1orf132, TRIM59, KLF14, and FHL2 genes were reanalyzed in 100 Korean blood samples to test their associations with chronological age, using the same analysis platform used in previous reports. Our study replicated the age association test for sjTREC and DNA methylation markers in the 5 genes in an independent validation set of 100 Koreans, and proved that the age predictive performance of the previous models is relatively consistent across different population groups. However, the extent of age association at certain CpG loci was not identical in the Korean and Polish populations; therefore, several age predictive models were retrained with the data obtained here. All of the 3 models retrained with DNA methylation and/or sjTREC data have a CpG site each from the ELOVL2 and FHL2 genes in common, and produced better prediction accuracy than previously reported models. This is attributable to the fact that the retrained model better fits the existing data and that the calculated prediction accuracy could be higher when the training data and the test data are the same. However, it is notable that the combination of different types of markers, i.e., sjTREC and DNA methylation, improved prediction accuracy in the eldest group. Our study demonstrates the usefulness of the proposed markers and the genotyping method in an independent dataset, and suggests the possibility of combining different types of DNA markers to improve prediction accuracy.     

Some tips on age estimation using DNA methylation in saliva samples as an index across the Japanese and Indonesian ethnicities

Age estimation of unidentified bodies is of marked importance in forensic medicine. In previous studies, the analysis of DNA methylation in body fluids led to the identification of several age-related CpG sites in genes such as EDARADD and FHL2. However, limited information is available on whether interethnic differences may affect the age prediction results. In the present study, we examined the effect of ethnicity on the age prediction method based on methylation scores, which were determined via methylation-sensitive high-resolution melting. We found that there was a significant difference in methylation scores between Japanese and Indonesian participants of early 20 s group, and that the nationality coefficient was significant for age estimation when applying the existing method for the analysis of the methylation status of EDARADD and FHL2. This suggests that when using certain biochemical indicators as a predictor of age, the effects of ethnicity on DNA methylation should be considered to improve the accuracy of the estimation.     

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation

In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450 K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a lack of research evaluating different high-dimensional variable selection methods of CpG sites with various models for age prediction. The aim of this study is to evaluate four variable selection methods (forward selection, LASSO, elastic net and SCAD) combined with a classical statistical model and sophisticated machine learning models based on the mean absolute deviation (MAD) and the root-mean-square error (RMSE). We used publicly available 450 K data set containing 991 whole blood samples (age 19–101 years). We found that the multiple linear regression model with 16 markers selected from the forward selection method performed very well in age prediction (MAD = 3.76 years and RMSE = 5.01 years). On the other hand, the highly advanced ultrahigh dimensional variable selection methods and sophisticated machine learning algorithms appeared unnecessary for age prediction based on DNA methylation.     

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 the methylation level at the genome-wide scale is 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.     

Improving the age estimation model for toenails

In a previous study, we have provided the first proof that chronological age can be estimated through DNA methylation (DNAm) patterns in fingernails and toenails. DNAm data of 15 CpGs located in 4 genetic markers (ASPA, EDARADD, ELOVL2 and PDE4C) were evaluated, of which variable selection yielded age prediction models with a mean absolute deviation (MAD) ranging from 7.68 to 9.36 years, depending on the sampling location. Three additional age-associated markers (KLF14, MIR29B2CHG and TRIM59) were assessed in the current study with the goal of increasing the prediction accuracy of the model initially constructed for toenails. This new and improved age estimation assay yielded an MAD of 4.82 and 5.61 years for the training and test set, respectively. The feasibility of the application for post-mortem cases was also demonstrated through testing a limited set of samples collected from deceased individuals.     

Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system

Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07 years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.     

The correlation between CpG island methylation of hTERT promoter and human age prediction

DNA methylation is an epigenetic modification that occurs during the life cycle of individuals. Its degree is closely associated with the methylation status of CpG sites in its promoter region. Based on the previous screening that the hTERT methylation is both related to tumors and age, we suspected that the age inference based on hTERT methylation would be disturbed by the disease of the tested person. Herein, eight CpG sites in the hTERT promoter region were analyzed by real-time methylation-specific PCR, and we found that CpG2, CpG5, and CpG8 were closely related to the tumor (P < 0.05). The remaining five CpG sites had a large error in predicting age alone. Combining them to establish a model yielded better results, with an average age error of 4.35 years. This study provides a reliable and accurate detection method for the DNA methylation status of multiple CpG sites on the hTERT gene promoter, which can be used for the prediction of forensic age and assistant diagnosis of clinical diseases.     

Development and inter-laboratory validation of the VISAGE enhanced tool for age estimation from semen using quantitative DNA methylation analysis

The analysis of DNA methylation has become an established method for chronological age estimation. This has triggered interest in the forensic community to develop new methods for age estimation from biological crime scene material. Various assays are available for age estimation from somatic tissues, the majority from blood. Age prediction from semen requires different DNA methylation markers and the only assays currently developed for forensic analysis are based on SNaPshot or pyrosequencing. Here, we describe a new assay using massively parallel sequencing to analyse 13 candidate CpG sites targeted in two multiplex PCRs. The assay has been validated by five consortium laboratories of the VISible Attributes through GEnomics (VISAGE) project within a collaborative exercise and was tested for reproducible quantification of DNA methylation levels and sensitivity with DNA methylation controls. Furthermore, DNA extracts and stains on Whatman FTA cards from two semen samples were used to evaluate concordance and mimic casework samples. Overall, the assay yielded high read depths (> 1000 reads) at all 13 marker positions. The methylation values obtained indicated robust quantification with an average standard deviation of 2.8% at the expected methylation level of 50% across the 13 markers and a good performance with 50 ng DNA input into bisulfite conversion. The absolute difference of quantifications from one participating laboratory to the mean quantifications of concordance and semen stains of remaining laboratories was approximately 1%. These results demonstrated the assay to be robust and suitable for age estimation from semen in forensic investigations. In addition to the 13-marker assay, a more streamlined protocol combining only five age markers in one multiplex PCR was developed. Preliminary results showed no substantial differences in DNA methylation quantification between the two assays, indicating its applicability with the VISAGE age model for semen developed with data from the complete 13-marker tool.     

Age-related DNA methylation analysis for forensic age estimation using post-mortem blood samples from Japanese individuals

As one of external visible characteristics (EVCs) in forensic phenotyping, age estimation is essential to providing additional information about a sample donor. With the development of epigenetics, age-related DNA methylation may be used as a reliable predictor of age estimation. With the aim of building a feasible age estimation model for Japanese individuals, 53 CpG sites distributed between 11 candidate genes were selected from previous studies. The DNA methylation level of each target CpG site was identified and measured on a massive parallel platform (synthesis by sequencing, Illumina, California, United States) from 60 forensic blood samples during the initial training phase. Multiple linear regression and quantile regression analyses were later performed to build linear and quantile age estimation models, respectively. Four CpG sites on four genes— ASPA, ELOVL2, ITGA2B, and PDE4C —, were found to be highly correlated with chronological age in DNA samples from Japanese individuals (|R| > 0.75). Subsequently, an independent validation dataset (n = 30) was used to verify and evaluate the performance of the two models. Comparison of mean absolute deviation (MAD) with other indicators showed that both models provide accurate age predictions (MAD: linear = 6.493 years; quantile = 6.243 years). The quantile model, however, can provide the changeable prediction intervals that grow wider with increasing age, and this tendency is consistent with the natural aging process in humans. Hence, the quantile model is recommended in this study.     

Age determination through DNA methylation patterns in fingernails and toenails

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 both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. 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.     

Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression

The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, ELOVL2, F5, GRM2, HOXC4, KLF14, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, TRIM59, ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18–69). In the case of KLF14, LDB2, SAMD10, and GRM2, neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers (ELOVL2, F5, KLF14, and TRIM59) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm.Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.     

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person’s lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2–90 years) analysed with Illumina’s genome-wide methylation platforms (27 K/450 K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R² = 0.92, mean absolute error (MAE) = 4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R² = 0.96) with a MAE = 3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE = 7.1 years) and a cohort of 1011 disease state individuals (MAE = 7.2 years). Furthermore, we highlighted the age markers’ potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R² = 0.96) with a MAE = 3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq^® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE = 7.5 years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.     

A forensic case study for body fluid identification using DNA methylation analysis

Recently, a method of identifying body fluids using DNA methylation has been developed (Frumkin et al., 2011). An existing multiplex assay using 9 CpG markers could differentiate 5 body fluids: semen, blood, saliva, menstrual blood, and vaginal fluid. To validate this technique, we evaluated the previously described body fluid identification method by means of single base extension (SBE). DNA methylation was applied to 22 samples in 18 forensic cases; seven of these were semen, three were blood, eight were saliva, three were vaginal fluid, and one was menstrual blood. Total of 18 samples were tested, the DNA methylation profiles were coincident from preliminary tests (acid phosphatase (AP), leucomalachite green (LMG, Sigma Aldrich, St Louis, MO, USA) and SALIgAE®) except one sample which displayed an all-negative result. After applying the DNA methylation method to forensic samples, we determined that it could be very useful for differentiating vaginal secretions from menstrual blood, for which there is no conventional preliminary testing method.     

Epigenetic age signatures in the forensically relevant body fluid of semen: a preliminary study

To date, DNA methylation has been regarded as the most promising age-predictive biomarker. In support of this, several researchers have reported age predictive models based on the use of blood or even across a broad spectrum of tissues. However, there have been no publications that report epigenetic age signatures from semen, one of the most forensically relevant body fluids. In genome-wide DNA methylation profiles of 36 body fluids including blood, saliva, and semen, the previous age predictive models showed considerable prediction accuracy in blood and saliva but not in semen. Therefore, we selected CpG sites, whose methylation levels are strongly correlated with age in 12 semen profiles obtained from individuals of different ages, and investigated DNA methylation changes at these CpGs in 68 additional semen samples obtained from individuals aged 20 to 73 years using methylation SNaPshot reaction. Among the selected age-related CpG candidates, outstanding age correlation was obtained at cg06304190 in the TTC7B gene. Interestingly, the region around the TTC7B gene has been reported to show age-related DNA methylation alteration in the sperm methylome of 2 samples collected from individuals at certain time intervals. The age-predictive linear regression model trained with 3 CpGs (cg06304190 in the TTC7B gene, cg06979108 in the NOX4 gene and cg12837463) showed a high correlation between the predicted age and the chronological age, with an average absolute difference of approximately 5 years. These selected epigenetic age signatures are expected to be useful for considerably accurate age estimation in the forensically relevant body fluid of semen. However, because the findings were limited by small sample size, it will be necessary to further evaluate the age correlation of the selected CpGs and to encourage further investigation.