DNA methylation-based age prediction from saliva: High age predictability by combination of 7 CpG markers

DNA methylation is currently one of the most promising age-predictive biomarkers. Many studies have reported DNA methylation-based age predictive models, but most of these are based on DNA methylation patterns from blood. Only a few studies have examined age-predictive DNA patterns in saliva, which is one of the most frequently-encountered body fluids at crime scenes. In this study, we generated genome-wide DNA methylation profiles of saliva from 54 individuals and identified CpG markers that showed a high correlation between methylation and age. Because the age-associated marker candidates from saliva differed from those of blood, we investigated DNA methylation patterns of 6 age-associated CpG marker candidates (cg00481951, cg19671120, cg14361627, cg08928145, cg12757011, and cg07547549 of the SST, CNGA3, KLF14, TSSK6, TBR1, and SLC12A5 genes, respectively) in addition to a cell type-specific CpG marker (cg18384097 of the PTPN7 gene) in an independent set of saliva samples obtained from 226 individuals aged 18 to 65 years. Multiplex methylation SNaPshot reactions were used to generate the data. We then generated a linear regression model with age information and the methylation profile from the 113 training samples. The model exhibited a 94.5% correlation between predicted and chronological age with a mean absolute deviation (MAD) from chronological age of 3.13 years. In subsequent validation using 113 test samples, we also observed a high correlation between predicted and chronological age (Spearman’s rho = 0.952, MAD from chronological age = 3.15 years). The model composed of 7 selected CpG sites enabled age prediction in saliva with high accuracy, which will be useful in saliva analysis for investigative leads.     

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.     

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

Many studies have reported age-associated DNA methylation changes and age-predictive models in various tissues and body fluids. Although age-associated DNA methylation changes can be tissue-specific, a multi-tissue age predictor that is applicable to various tissues and body fluids with considerable prediction accuracy might be valuable. In this study, DNA methylation at 5 CpG sites from the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes were investigated in 448 samples from blood, saliva, and buccal swabs. A multiplex methylation SNaPshot assay was developed to measure DNA methylation simultaneously at the 5 CpG sites. Among the 5 CpG sites, 3 CpG sites in the ELOVL2, KLF14 and TRIM59 genes demonstrated strong correlation between DNA methylation and age in all 3 sample types. Age prediction models built separately for each sample type using the DNA methylation values at the 5 CpG sites showed high prediction accuracy with a Mean Absolute Deviation from the chronological age (MAD) of 3.478 years in blood, 3.552 years in saliva and 4.293 years in buccal swab samples. A tissue-combined model constructed with 300 training samples including 100 samples from each blood, saliva and buccal swab samples demonstrated a very strong correlation between predicted and chronological ages (r = 0.937) and a high prediction accuracy with a MAD of 3.844 years in the 148 independent test set samples of 50 blood, 50 saliva and 48 buccal swab samples. Although more validation might be needed, the tissue-combined model’s prediction accuracies in each sample type were very much similar to those obtained from each tissue-specific model. The multiplex methylation SNaPshot assay and the age prediction models in our study would be useful in forensic analysis, which frequently involves DNA from blood, saliva, and buccal swab samples.     

Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2–82 years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors’ blood samples. The MLR model enabled age prediction with R² = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R² = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models’ performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5 years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43 days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.

     

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.     

Age prediction in living: Forensic epigenetic age estimation based on blood samples

DNA methylation analysis in a variety of genes has brought promising results in age estimation. The main aim of this study was to evaluate DNA methylation levels from four age-correlated genes, ELOVL2, FHL2, EDARADD and PDE4C, in blood samples of healthy Portuguese individuals. Fifty-three samples were analyzed through the bisulfite polymerase chain reaction (PCR) sequencing method for CpG dinucleotide methylation status. Linear regression models were used to analyze relationships between methylation levels and chronological age. The highest age-associated CpG in each locus was chosen to build a multi-locus age prediction model (APM), allowing to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 5.35 years, explaining 94.1% of age variation. Validation approaches demonstrated the accuracy and reproducibility of the proposed multi-locus APM. Testing the APM in 51 blood samples from deceased individuals a MAD of 9.72 years was obtained. Potential differences in methylation status between samples from living and deceased individuals could exist since the highest age-correlated CpGs were different in some genes between both groups. In conclusion, our study using the bisulfite PCR sequencing method is in accordance with the high age prediction accuracy of DNA methylation levels in four previously reported age-associated genes. DNA methylation pattern differences between blood samples from living and deceased individuals should be taken into account in forensic contexts.     

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.     

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.     

Epigenetic age estimation in saliva and in buccal cells

Age estimation based on epigenetic markers is a DNA intelligence tool with the potential to provide relevant information for criminal investigations, as well as to improve the inference of age-dependent physical characteristics such as male pattern baldness or hair color. Age prediction models have been developed based on different tissues, including saliva and buccal cells, which show different methylation patterns as they are composed of different cell populations. On many occasions in a criminal investigation, the origin of a sample or the proportion of tissues is not known with certainty, for example the provenance of cigarette butts, so use of combined models can provide lower prediction errors.In the present study, two tissue-specific and seven age-correlated CpG sites were selected from publicly available data from the Illumina HumanMethylation 450 BeadChip and bibliographic searches, to help build a tissue-dependent, and an age-prediction model, respectively. For the development of both models, a total of 184 samples (N = 91 saliva and N = 93 buccal cells) ranging from 21 to 86 years old were used. Validation of the models was performed using either k-fold cross-validation and an additional set of 184 samples (N = 93 saliva and N = 91 buccal cells, 21–86 years old).The tissue prediction model was developed using two CpG sites (HUNK and RUNX1) based on logistic regression that produced a correct classification rate for saliva and buccal swab samples of 88.59 % for the training set, and 83.69 % for the testing set. Despite these high success rates, a combined age prediction model was developed covering both saliva and buccal cells, using seven CpG sites (cg10501210, LHFPL4, ELOVL2, PDE4C, HOXC4, OTUD7A and EDARADD) based on multivariate quantile regression giving a median absolute error (MAE): ± 3.54 years and a correct classification rate ( %CP±PI) of 76.08 % for the training set, and an MAE of ± 3.66 years and a %CP±PI of 71.19 % for the testing set. The addition of tissue-of origin as a co-variate to the model was assessed, but no improvement was detected in age predictions. Finally, considering the limitations usually faced by forensic DNA analyses, the robustness of the model and the minimum recommended amount of input DNA for bisulfite conversion were evaluated, considering up to 10 ng of genomic DNA for reproducible results. The final multivariate quantile regression age predictor based on the models we developed has been placed in the open-access Snipper forensic classification website.     

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.     

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.     

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.     

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.     

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 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.     

Developmental validation studies of epigenetic DNA methylation markers for the detection of blood,semen and saliva samples

Determining the type and origin of body fluids in a forensic investigation can provide important assistance in reconstructing crime scenes. A set of epigenetic markers, ZC3H12D, BCAS4 and cg06379435, have been developed to produce unique and specific patterns of DNA methylation that can be used to identify semen, saliva, and blood, respectively. To ensure the efficacy of these markers, developmental validation studies were performed to determine the conditions and limitations of this new tool for forensic analysis. DNA was extracted from human samples and bisulfite modified using commercial bisulfite modification kits. Specific primers were used to amplify the region of interest and the methylation profile of the CpG sites were determined by pyrosequencing. The percent methylation values at each CpG site were determined in multiple samples and averaged for each tissue type. The versatility of these new markers is presented by showing the results of validation studies on sensitivity, human specificity, stability and mixture resolution. When testing the markers using different organisms, we did obtain positive results for certain non-human primate samples, however, all other tested species were negative. The lowest concentration consistently detected varied from 0.1 to 10 ng, depending on the locus, indicating the importance of primer design and sequence in the assay. The method also proved to be effective when inhibitors were present in the samples or when samples were degraded by heat. Simulated case- samples were also tested. In the case of mixtures of different cell types, the overall methylation values varied in a consistent and predictable manner when multiple cell types were present in the same sample. Overall, the validation studies demonstrate the robustness and effectiveness of this new tool for body fluid identification.     

Validation of a novel fluorescent probe-based real-time PCR assay to detect saliva-specific unmethylated CpG sites for saliva identification

The identification of saliva from forensic samples is often important to establish what happened at a crime scene, especially in sexual assault cases. Recently, CpG sites that are specifically methylated or unmethylated in saliva have been reported as markers for saliva identification. In this study, we designed a fluorescent probe-based real-time polymerase chain reaction (PCR) assay for analyzing the methylation status of two neighboring CpG sites, which we previously found were saliva-specifically unmethylated. Specificity analysis using various types of body fluid/tissue samples demonstrated a probe detecting the unmethylation of the two CpG sites reacted only to saliva DNA, indicating this probe as an all-or-nothing marker for the presence of saliva DNA. Sensitivity analysis demonstrated that the detection limit was 0.5 ng saliva DNA as input for bisulfite conversion, while we confirmed a negative effect of larger amounts of non-saliva DNA on sensitivity in the analysis of saliva–vaginal DNA mixtures. We finally validated the applicability of this test to swabs from licked skin and bottles after drinking as mock forensic samples in comparison with other saliva-specific markers. We confirmed the potential usefulness of this test for skin samples, from which a saliva-specific mRNA was not detected reliably, while the ingredients in several beverages might affect methylation analysis. Given the simplicity of real-time PCR as well as the high specificity and sensitivity of the test, we believe the developed method is suitable for routine forensic analysis and can play an important role in saliva identification.     

Evaluation of a blood-specific DNA methylated region and trial for allele-specific blood identification from mixed body fluid DNA

The identification of blood samples obtained from crime scenes has been an important step in forensic investigation. Recently, a novel approach using the blood-specific methylated CpG site cg06379435 has been reported. In this study, we developed a real-time polymerase-chain-reaction-based method that can simply and rapidly quantitate the methylation ratio of cg06379435 and its neighboring CpGs and set the threshold ratios for blood identification by analyzing various body fluid samples. Blood identification using the thresholds was successfully performed in the analysis of a small amount (1 ng) of DNA from blood and various aged blood samples, including 29-year-old stains. We also demonstrated a test for allele-specific blood identification from a mixed DNA sample by bisulfite sequencing analysis of these CpG sites and their neighboring single nucleotide polymorphism, rs7359943 (A/G), which is of relevance in cases where mixed samples are obtained from crime scenes. The stability of DNA methylation in aged samples and the usefulness of neighboring genetic information shown in this study suggest that DNA-methylation-based body fluid identification will play a major role in future forensic investigations.     

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.