DNA Methylation-based Age Prediction Research Articles

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons.

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

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.

Evaluation of DNA Methylation-Based Age-Prediction Models from Saliva and Buccal Swab Samples Using Pyrosequencing Data

In forensic genetics, the identification of an individual is often carried out by comparing unknown DNA profiles obtained in a case against databases or references. When no match is found, investigators need new tools in order to obtain additional leads. The latest technical advances now make it possible to predict externally visible characteristics. With this objective, predicting the age of an individual through DNA methylation analysis remains one of the last challenges. The prediction models have to account for the specific constraints of this field, including tissue specificity and DNA availability (i.e., low DNA amounts or low-quality DNA). Jung and colleagues have recently produced models from blood, saliva and buccal cells by using a single base extension sequencing method. With the goal of evaluating these models in our own analytical conditions, saliva and buccal cell samples from 115 French individuals between the ages of 0 and 88 years old were collected and analyzed. After having determined the optimal analysis conditions, including the DNA quantity for bisulfite conversion (75 ng), some differences were highlighted in the measured methylation rates between the two studies. Despite these discrepancies, the prediction performance levels remain very similar, our study showing mean absolute errors of 3.5 years, 3.9 years and 3.2 years, respectively, for the saliva, buccal swab and multitissue model, with limitations observed for the oldest and youngest individuals. Furthermore, we propose the use of a prediction interval with an error dispersion and correct prediction rate at ±5 years and ±10 years, respectively.

Forensic Age Estimation through a DNA Methylation-Based Age Prediction Model in the Italian Population: A Pilot Study

DNA methylation is one of the epigenetic marks which has been studied intensively in recent years for age predicting purposes in the forensic area. In order to integrate age prediction into routine forensic workflow, the purpose of this study was to standardize and optimize a DNA methylation-based protocol tailored to the Italian context. A previously published protocol and age-predictive method was implemented for the analysis of 84 blood samples originating from Central Italy. The study here presented is based on the Single Base Extension method, considering five genes: ELOVL2, FHL2, KLF14, C1orf132, now identified as MIR29B2C, and TRIM59. The precise and specific steps consist of DNA extraction and quantification, bisulfite conversion, amplification of converted DNA, first purification, single base extension, second purification, capillary electrophoresis, and analysis of the results to train and test the tool. The prediction error obtained, expressed as mean absolute deviation, showed a value of 3.12 years in the training set and 3.01 years in the test set. Given that population-based differences in DNA methylation patterns have been previously reported in the literature, it would be useful to further improve the study implementing additional samples representative of the entire Italian population.

DNA methylation-based age prediction with bloodstains using pyrosequencing and random forest regression.

The use of DNA methylation to predict chronological age has shown promising potential for obtaining additional information in forensic investigations. To date, several studies have reported age prediction models based on DNA methylation in body fluids with high DNA content. However, it is often difficult to apply these existing methods in practice due to the low amount of DNA present in stains of body fluids that are part of a trace material. In this study, we present a sensitive and rapid test for age prediction with bloodstains based on pyrosequencing and random forest regression. This assay requires only 0.1ng of genomic DNA and the entire procedure can be completed within 10h, making it practical for forensic investigations that require a short turnaround time. We examined the methylation levels of 46 CpG sites from six genes using bloodstain samples from 128 males and 113 females aged 10-79 years. A random forest regression model was then used to construct an age prediction model for males and females separately. The final age prediction models were developed with seven CpG sites (three for males and four for females) based on the performance of the random forest regression. The mean absolute deviation was less than 3 years for each model. Our results demonstrate that DNA methylation-based age prediction using pyrosequencing and random forest regression has potential applications in forensics to accurately predict the biological age of a bloodstain donor.

Differences in DNA Methylation-Based Age Prediction Within Twin Pairs Discordant for Cancer.

DNA methylation-based age acceleration (DNAmAA) is associated with cancer, with both cancer tissue and blood showing increased DNAmAA. We aimed to investigate whether DNAmAA is associated with cancer risk within twin pairs discordant for cancer, and whether DNAmAA has the potential to serve as a biomarker for such. The study included 47 monozygotic and 48 same-sex-dizygotic cancer-discordant twin pairs from the Finnish Twin Cohort study with blood samples available between 17 and 31 years after the cancer diagnosis. We studied all cancers (95 pairs), then separately breast cancer (24 pairs) and all sites other than breast cancer (71 pairs). DNAmAA was calculated for seven models: Horvath, Horvath intrinsic epigenetic age acceleration, Hannum, Hannum intrinsic epigenetic age acceleration, Hannum extrinsic epigenetic age acceleration, PhenoAge and GrimAge. Within-pair differences in DNAmAA were analyzed by paired t tests and linear regression. Twin pairs sampled before cancer diagnosis did not differ significantly in DNAmAA. However, the within-pair differences in DNAmAA before cancer diagnosis increased significantly the closer the cancer diagnosis was, and this acceleration extended for years after the diagnosis. Pairs sampled after the diagnosis differed for DNAmAA with the Horvath models capturing cancer diagnosis-associated DNAmAA across all three cancer groupings. The results suggest that DNAmAA in blood is associated with cancer diagnosis. This may be due to epigenetic alterations in relation to cancer, its treatment or associated lifestyle changes. Based on the current study, the biomarker potential of DNAmAA in blood appears to be limited.

A high-throughput real-time PCR tissue-of-origin test to distinguish blood from lymphoblastoid cell line DNA for (epi)genomic studies

Lymphoblastoid cell lines (LCLs) derive from blood infected in vitro by Epstein–Barr virus and were used in several genetic, transcriptomic and epigenomic studies. Although few changes were shown between LCL and blood genotypes (SNPs) validating their use in genetics, more were highlighted for other genomic features and/or in their transcriptome and epigenome. This could render them less appropriate for these studies, notably when blood DNA could still be available. Here we developed a simple, high-throughput and cost-effective real-time PCR approach allowing to distinguish blood from LCL DNA samples based on the presence of EBV relative load and rearranged T-cell receptors γ and β. Our approach was able to achieve 98.5% sensitivity and 100% specificity on DNA of known origin (458 blood and 316 LCL DNA). It was further applied to 1957 DNA samples from the CEPH Aging cohort comprising DNA of uncertain origin, identifying 784 blood and 1016 LCL DNA. A subset of these DNA was further analyzed with an epigenetic clock indicating that DNA extracted from blood should be preferred to LCL for DNA methylation-based age prediction analysis. Our approach could thereby be a powerful tool to ascertain the origin of DNA in old collections prior to (epi)genomic studies.

Adaptive feature selection framework for DNA methylation-based age prediction

Adaptive feature selection framework for DNA methylation-based age prediction

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 3.3years. Collectively, the result in this study showed the potential applicability of ddPCR to predict age from saliva.

A collaborative exercise on DNA methylation-based age prediction and body fluid typing

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant’s data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.

DNA Methylation-Based Age Prediction and Telomere Length Reveal an Accelerated Aging in Induced Sputum Cells Compared to Blood Leukocytes: A Pilot Study in COPD Patients.

Aging is the predominant risk factor for most degenerative diseases, including chronic obstructive pulmonary disease (COPD). This process is however very heterogeneous. Defining the biological aging of individual tissues may contribute to better assess this risky process. In this study, we examined the biological age of induced sputum (IS) cells, and peripheral blood leukocytes in the same subject, and compared these to assess whether biological aging of blood leukocytes mirrors that of IS cells. Biological aging was assessed in 18 COPD patients (72.4 ± 7.7 years; 50% males). We explored mitotic and non-mitotic aging pathways, using telomere length (TL) and DNA methylation-based age prediction (DNAmAge) and age acceleration (AgeAcc) (i.e., difference between DNAmAge and chronological age). Data on demographics, life style and occupational exposure, lung function, and clinical and blood parameters were collected. DNAmAge (67.4 ± 5.80 vs. 61.6 ± 5.40 years; p = 0.0003), AgeAcc (−4.5 ± 5.02 vs. −10.8 ± 3.50 years; p = 0.0003), and TL attrition (1.05 ± 0.35 vs. 1.48 ± 0.21 T/S; p = 0.0341) are higher in IS cells than in blood leukocytes in the same patients. Blood leukocytes DNAmAge (r = 0.927245; p = 0.0026) and AgeAcc (r = 0.916445; p = 0.0037), but not TL, highly correlate with that of IS cells. Multiple regression analysis shows that both blood leukocytes DNAmAge and AgeAcc decrease (i.e., younger) in patients with FEV1% enhancement (p = 0.0254 and p = 0.0296) and combined inhaled corticosteroid (ICS) therapy (p = 0.0494 and p = 0.0553). In conclusion, new findings from our work reveal a differential aging in the context of COPD, by a direct quantitative comparison of cell aging in the airway with that in the more accessible peripheral blood leukocytes, providing additional knowledge which could offer a potential translation into the disease management.

Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

S14. ANALYSIS OF METHYLATION AGE AND BLOOD CELL COMPOSITION IN SUBJECTS WITH CURRENT SUICIDE IDEATION

BackgroundSuicidal Ideation (SI) remain an important and common risk factor affecting people with SCZ, who eventually attempt or complete suicide. Then the question is, what if factors (such as stressful life events and related molecular biomarkers) known to be involved in the aetiology of SCZ could help in predicting SI in this population? The accelerated aging hypothesis of SCZ posits that physiological changes associated with normal aging occur at an earlier age in individuals with SCZ than in the general population. Importantly, epigenetic changes may constitute an important component of aging process. Based on this, the chronological age can be predicted by the epigenetic clock in a highly consistent manner. The aims of this research were to determine the effect chronological and biological age on current SI and secondly, to determine the effect of the variation of cellular blood cell composition on current SI.MethodsA total of 103 participants with a DSM-IV diagnosis of schizophrenia spectrum and other psychotic disorders were recruited from the Center of Addiction and Mental Health. The SI was assessed by the Columbia-Suicide Severity Rating Scale. Genome-wide DNA methylation analysis was generated from whole blood cells. The DNA methylation was assessed using the Illumina Infinium HumanMethylation450 Bead Chip while the DNA methylation-based age prediction and white blood cell composition were performed using the statistical pipeline developed by Horvath.ResultsOut of 103 participants, 18 had current SI (17%) while 85 had NSI. The DNAm age correlated with chronological age in the overall sample (r=0.814, p<0.0001), NSI (r=0.823, p<0.0001) and SI subjects (r=0.734, p=0.001). The strong linear relationship between DNAm age and chronological age showed a high accuracy of the epigenetic clock. However, DNAm age acceleration residuals did not differ between NSI and SI groups (t=1.532, p= 0.129). Comparison of the cellular cell blood composition between the NSI and SI groups indicated no significant differences between the NSI and SI groups (lymphocytes (t= -0.338, p=0.736), monocytes (t=-1.405, p=0.163) and granulocytes (t=0.924, p=0.358)). Furthermore, there were no significant differences between the SI and NSI groups in the analysis of the plasmablast (t=0.138, p=0.890), CD4 naïve (t=0.010, p=0.992) and CD8 naïve (t=0.681, p=0.497)DiscussionStressful life events may change DNA methylation, which in turn can affect suicide ideation and suicidal behavior. Although SCZ is associated with age-related physiological factors, we were unable to find accelerated aging in our study. Nevertheless, we cannot rule out the possibility of other aging mechanism independent of epigenetic aging in SCZ patients.Conclusion: Further studies aimed at investigating the accelerated aging hypothesis in peripheral tissue are warranted to identify individuals with SCZ at risk for suicide. This will permit a tailored treatment and will prevent suicide in SCZ individuals.

DNA methylation-based age prediction using cell separation algorithm

The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test the CSA. Secondly, the CSA is examined on three forms of age prediction problems from two body fluids (blood and saliva). The healthy blood samples, diseased blood samples and saliva samples are used to test the method's capability. The results of the CSA are compared not only with other methods proposed in previous studies, but also with the results from stochastic gradient descent (SGD), ADAM, and genetic algorithm (GA). The model results of CSA are extremely better than the four methods proposed in previous works that have not used ANN training process. The CSA also outperformed SGD, ADAM that employ the ANN model without ANN optimization by meta-heuristics. The CSA results are comparable (even superior) to the GA model which takes the advantages of both ANN and meta-heuristics.

Sperm Ribosomal DNA Promoter Methylation Levels Are Correlated With Paternal Aging and May Relate With in vitro Fertilization Outcomes.

The impact of aging on reproductive outcomes has received considerable critical attention; however, there is much less information available on the effects of paternal age compared to the effects of maternal age. In this study, methylation levels of sperm rDNA promoter regions and Long Interspersed Nucleotide Element 1 (LINE-1) were measured using pyrosequencing and fertilization, day 3 good-quality embryo, pregnancies, and implantation results were assessed. We observed significantly increasing levels of DNA methylation in the sperm rDNA promoter regions with age based on stratifying the samples by age alone (P = 0.0001) and performing linear regression analysis (P < 0.0001). Meanwhile, no statistically significant correlations were observed between global LINE-1 methylation with age. No statistically significant correlations were observed between sperm rDNA promoter methylation levels and either the day 3 good-quality embryo rate or clinical pregnancy rate. In contrast, the correlation between sperm rDNA promoter methylation levels and fertilization (2 pronuclei) rate was nearly significant (P = 0.0707), especially the methylation levels of some individual CpG units (CpG_10, P = 0.0176; CpG_11, P = 0.0438; CpG_14, P = 0.0232) and rDNA promoter methylation levels measured using primerS2 (P = 0.0513). No significant correlation was found between sperm rDNA promoter methylation levels and fertilization rates (2 pronuclei, 1 pronuclei, and 1 polypronuclei). Our results demonstrate that sperm are susceptible to age-associated alterations in methylation levels of rDNA promoter regions, suggesting that sperm rDNA promoter methylation levels can be applied to DNA methylation-based age prediction, and that the aberrant methylation of rDNA promoters may be partially responsible for enhanced disease susceptibility of offspring sired by older fathers. Methylation levels of sperm rDNA promoter regions may correlate with polypronuclei rates of IVF programs.

Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing

DNA methylation-based age estimation is a promising new tool for forensic molecular biology. There is growing understanding of the best predictive CpG loci and their performance in various sample types. Since forensic samples usually provide only small amounts of DNA, the sensitivity of the method is crucial. Pyrosequencing is one of the most sensitive methods but only capable to analyze different target regions separately. Thus, multiple input DNA samples are required for investigations of different target regions, which is required for all current age estimation models. To overcome this limitation, we developed a novel multiplex strategy for Pyrosequencing, which allows the investigation of different target regions from a single small amount of input DNA. A pre-amplification step was introduced to increase the amount of target-specific template for the subsequent sequencing PCR step. We tested this multiplex strategy for eight target regions including 15 age CpGs associated with the genes of ELOVL2, FHL2, CCDC102B, C1orf132, KLF14, EDARADD, PDE4C and SST. Except for FHL2, all target regions were successfully sequenced with the multiplex strategy and the precision in terms of reproducibility of the measurements was equal to the singleplex strategy. The measured methylation values at the age CpGs displayed borderline significant differences between both analytical strategies for six out of 14 CpG sites whereas both strategies delivered equal methylation values for the remaining eight age CpGs. In total, our results indicate that the multiplex strategy can act as a promising alternative for age estimation studies in cases when only limited amounts of DNA samples are available.

DNA methylation-based age prediction and telomere length in white blood cells and cumulus cells of infertile women with normal or poor response to ovarian stimulation.

An algorithm assessing the methylation levels of 353 informative CpG sites in the human genome permits accurate prediction of the chronologic age of a subject. Interestingly, when there is discrepancy between the predicted age and chronologic age (age acceleration or “AgeAccel”), patients are at risk for morbidity and mortality. Identification of infertile patients at risk for accelerated reproductive senescence may permit preventative action. This study aimed to assess the accuracy of the “epigenetic clock” concept in reproductive age women undergoing fertility treatment by applying the age prediction algorithm in peripheral (white blood cells [WBCs]) and follicular somatic cells (cumulus cells [CCs]), and to identify whether women with premature reproductive aging (diminished ovarian reserve) were at risk of AgeAccel in their age prediction. Results indicated that the epigenetic algorithm accurately predicts age when applied to WBCs but not to CCs. The age prediction of CCs was substantially younger than chronologic age regardless of the patient’s age or response to stimulation. In addition, telomeres of CCs were significantly longer than that of WBCs. Our findings suggest that CCs do not demonstrate changes in methylome-predicted age or telomere-length in association with increasing female age or ovarian response to stimulation.

DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models

The field of DNA intelligence focuses on retrieving information from DNA evidence that can help narrow down large groups of suspects or define target groups of interest. With recent breakthroughs on the estimation of geographical ancestry and physical appearance, the estimation of chronological age comes to complete this circle of information. Recent studies have identified methylation sites in the human genome that correlate strongly with age and can be used for the development of age-estimation algorithms. In this study, 110 whole blood samples from individuals aged 11–93 years were analysed using a DNA methylation quantification assay based on bisulphite conversion and massively parallel sequencing (Illumina MiSeq) of 12 CpG sites. Using this data, 17 different statistical modelling approaches were compared based on root mean square error (RMSE) and a Support Vector Machine with polynomial function (SVMp) model was selected for further testing. For the selected model (RMSE = 4.9 years) the mean average error (MAE) of the blind test (n = 33) was calculated at 4.1 years, with 52% of the samples predicting with less than 4 years of error and 86% with less than 7 years. Furthermore, the sensitivity of the method was assessed both in terms of methylation quantification accuracy and prediction accuracy in the first validation of this kind. The described method retained its accuracy down to 10 ng of initial DNA input or ∼2 ng bisulphite PCR input. Finally, 34 saliva samples were analysed and following basic normalisation, the chronological age of the donors was predicted with less than 4 years of error for 50% of the samples and with less than 7 years of error for 70%.

A validation study of DNA methylation-based age prediction using semen in forensic casework samples.

Previously, an age-predictive method based on DNA-methylation patterns in semen was developed, using three CpG sites (cg06304190 in the TTC7B gene, cg12837463, and cg06979108 in the NOX4 gene). Before considering the routine use of a new method in forensics, validation studies such as concordance and sensitivity tests are essential for obtaining expanded and more reliable forensic information. Here, we evaluated a previously described age-predictive method for semen for routine forensic use. Concordance testing showed a high correlation between the predicted and chronological age, with a mean absolute deviation from the chronological age of 4.8 years. Sensitivity testing suggested that age prediction with reliable accuracy and consistency was possible with >5 ng of bisulfite-converted DNA. We also confirmed the applicability of the age-predictive method in forensic casework, using forensic samples. Thus, the proposed method could serve as a very valuable forensics tool for accurate age prediction with semen samples.

DNA methylation-based age prediction from various tissues and body fluids.

Aging is a natural and gradual process in human life. It is influenced by heredity, environment, lifestyle, and disease. DNA methylation varies with age, and the ability to predict the age of donor using DNA from evidence materials at a crime scene is of considerable value in forensic investigations. Recently, many studies have reported age prediction models based on DNA methylation from various tissues and body fluids. Those models seem to be very promising because of their high prediction accuracies. In this review, the changes of age-associated DNA methylation and the age prediction models for various tissues and body fluids were examined, and then the applicability of the DNA methylation-based age prediction method to the forensic investigations was discussed. This will improve the understandings about DNA methylation markers and their potential to be used as biomarkers in the forensic field, as well as the clinical field.