Age-related DNA methylation changes for forensic age-prediction

Collaborative exercise: analysis of age estimation using a QIAGEN protocol and the PyroMark Q48 platform

Human age estimation from trace samples may give important leads early in a police investigation by contributing to the description of the perpetrator. Several molecular biomarkers are available for the estimation of chronological age, and currently, DNA methylation patterns are the most promising. In this study, a QIAGEN age protocol for age estimation was tested by five forensic genetic laboratories. The assay comprised bisulfite treatment of the extracted DNA, amplification of five CpG loci (in the genes of ELOVL2, C1orf132, TRIM59, KLF14, and FHL2), and sequencing of the amplicons using the PyroMark Q48 platform. Blood samples from 49 individuals with ages ranging from 18 to 64 years as well as negative and methylation controls were analyzed. An existing age estimation model was applied to display a mean absolute deviation of 3.62 years within the reference data set.

Key points

Age determination as an intelligence tool during investigations can be a powerful tool in forensic genetics.In this study, five laboratories ran 49 samples and obtained a mean absolute deviation of 3.62 years.Five markers were analyzed on a PyroMark Q48 platform.

Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors

Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100-400 samples) produced through targeted methylation sequencing for a small number of predictors (10-25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).

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.

Age Prediction of Human Based on DNA Methylation by Blood Tissues

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R² value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R² were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

AgeGuess, a Methylomic Prediction Model for Human Ages

Aging was a biological process under regulations from both inherited genetic factors and various molecular modifications within cells during the lifespan. Multiple studies demonstrated that the chronological age may be accurately predicted using the methylomic data. This study proposed a three-step feature selection algorithm AgeGuess for the age regression problem. AgeGuess selected 107 methylomic features as the gender-independent age biomarkers and the Support Vector Regressor (SVR) model using these biomarkers achieved 2.0267 in the mean absolute deviation (MAD) compared with the real chronological ages. Another regression algorithm Ridge achieved a slightly better MAD 1.9859 using the same biomarkers. The gender-independent age prediction models may be further improved by establishing two gender-specific models. And it's interesting to observe that there were only two methylation biomarkers shared by the two gender-specific biomarker sets and these two biomarkers were within the two known age-associated biomarker genes CALB1 and KLF14.

MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R²) of 95.96% between age and DNAm. In the train data, the MAD and R² are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

Human age prediction based on DNA methylation of non-blood tissues

BACKGROUND AND OBJECTIVE

The study of human aging contributes to disease prevention, treatment and life extension. Recently, epigenetics studies have evidenced that there is a close association between DNA methylation and human ages. A quantitatively statistical modeling between DNA methylation and ages could predict the person's age more accurately.

METHODS

We propose a regression model to predict human age based on gradient boosting regressor (GBR). We collect a total of 1280 publicly available non-blood tissues samples with ages ranged from 0 to 90 years old. We calculate the Pearson correlation between CpG's DNA methylation level and age to select age-related CpGs.

RESULTS

Thirteen age-related CpG sites are selected. GBR has the smallest mean absolute deviation to the actual age comparing with other three different models including Bayesian ridge, multiple linear regression, and support vector regression. In the training datasets, the cross-validation results show that the correlation R² between predicted age and DNA methylation is 0.89, and the mean absolute deviation is 4.66 years. In an independent testing set with 262 samples, the GBR achieves the mean absolute deviation of 6.08 years. Meanwhile we also briefly describe the function of the selected thirteen CpG sites.

CONCLUSIONS

We build an age predictor to study the association between ages and the DNA methylation of human non-blood tissues. Our new model provides a more accurate estimation of human ages which will be instrumental for understanding the regulation of DNA methylation on human aging and will accurately monitor the individual aging process.

Validation of methylation-based forensic age estimation in time-series bloodstains on FTA cards and gauze at room temperature conditions

We previously proposed a prediction model consisting of 9 CpG sites for forensic age estimation with high practical potentials in Chinese males. Here, we further evaluated the performance of this prediction model in two independent batches of time-series bloodstain samples naturally exposed to room temperature conditions. The first batch consists of 30 Han Chinese males (18-59 years of age) whose peripheral blood was converted into bloodstains on Flinders Technology Association (FTA) cards and naturally exposed to room temperature conditions for different time points up to 3 months. The second batch consists of 99 Han Chinese males (21-66 years of age) whose peripheral blood was divided into 3 replicates, converted into bloodstains on gauze, and naturally exposed to room temperature conditions for 3 months. For each time point and each replicate, the methylation levels at the 9 CpG sites were detected using the EpiTYPER system. Applying the 9-CpG age prediction model to these bloodstain samples resulted in highly accurate age predictions for all time points and replicates (0.81 <R² < 0.91, 2.94 < MAD < 3.55 years). The updated model combining our previous and current data achieved similarly high prediction results. Therefore, our 9-CpG age prediction model was successfully validated in time-series bloodstain samples converted on both FTA card and gauze under natural room temperature conditions, demonstrating high potentials in future forensic applications to Han Chinese males.

Dynamic DNA Methylation During Aging: A "Prophet" of Age-Related Outcomes

The biological markers of aging used to predict physical health status in older people are of great interest. Telomere shortening, which occurs during the process of cell replication, was initially considered a promising biomarker for the prediction of age and age-related outcomes (e.g., diseases, longevity). However, the high instability in detection and low correlation with age-related outcomes limit the extension of telomere length to the field of prediction. Currently, a growing number of studies have shown that dynamic DNA methylation throughout human lifetime exhibits strong correlation with age and age-related outcomes. Indeed, many researchers have built age prediction models with high accuracy based on age-dependent methylation changes in certain CpG loci. For now, DNA methylation based on epigenetic clocks, namely epigenetic or DNA methylation age, serves as a new standard to track chronological age and predict biological age. Measures of age acceleration (Δage, DNA methylation age – chronological age) have been developed to assess the health status of a person. In addition, there is evidence that an accelerated epigenetic age exists in patients with certain age-related diseases (e.g., Alzheimer’s disease, cardiovascular disease). In this review, we provide an overview of the dynamic signatures of DNA methylation during aging and emphasize its practical utility in the prediction of various age-related outcomes.

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.

Differentially methylated CpG regions analyzed by PCR-high resolution melting for monozygotic twin pair discrimination

Discrimination between monozygotic (MZ) twins is a forensic limitation when using conventional DNA profiling techniques for human identification. Recent works based on epigenetics seem to open a new way to solve this issue due to methylation status of MZ twins change during their lifetime. Methylation analysis through BeadChip platforms allows the study up to 850 K CpG sites revealing that numerous differential methylation regions exist between MZ twins. However, this methodology is difficult to implement in forensic laboratories. On the contrary, PCR-HRM (High Resolution Melting) technology is one of the easiest methods for analyzing DNA methylation and it has been capable to discriminate between MZ twins. The purpose of this study is to contribute with new differential methylation regions in MZ twins to those that have been previously studied through PCR-HRM. Here, we have selected 6 CpG regions located at the ITGA2B, ASPA, PDE4C, ZIC5, USP11 and NOP14 loci that have shown methylation status variation during lifetime. The study has been carried out from saliva-derived DNA of 18 MZ twin pairs. The most discriminating regions were those located at ITGA2B, ASPA and ZIC5 loci showing significant within-pair differences in 44.4% of the cases. Non evidences of relation between age and significant differences between MZ twins were found, although the 50% of MZ twin pairs were discrimnated in the oldest age range (59-66 years old). These results support the use of these regions to increase the number of epigenetics age-related markers available to discriminate between MZ twins in a pair by PCR-HRM in forensic laboratories.

Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor

All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0–103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.

Recent progress, methods and perspectives in forensic epigenetics

Forensic epigenetics, i.e., investigating epigenetics variation to resolve forensically relevant questions unanswerable with standard forensic DNA profiling has been gaining substantial ground over the last few years. Differential DNA methylation among tissues and individuals has been proposed as useful resource for three forensic applications i) determining the tissue type of a human biological trace, ii) estimating the age of an unknown trace donor, and iii) differentiating between monozygotic twins. Thus far, forensic epigenetic investigations have used a wide range of methods for CpG marker discovery, prediction modelling and targeted DNA methylation analysis, all coming with advantages and disadvantages when it comes to forensic trace analysis. In this review, we summarize the most recent literature on these three main topics of current forensic epigenetic investigations and discuss limitations and practical considerations in experimental design and data interpretation, such as technical and biological biases. Moreover, we provide future perspectives with regard to new research questions, new epigenetic markers and recent technological advances that - as we envision - will move the field towards forensic epigenomics in the near future.

Back to the future: Epigenetic clock plasticity towards healthy aging

Aging is the most important risk factor for major human lifestyle diseases, including cancer, neurological and cardiometabolic disorders. Due to the complex interplay between genetics, lifestyle and environmental factors, some individuals seem to age faster than others, whereas centenarians seem to have a slower aging process. Therefore, a biochemical biomarker reflecting the relative biological age would be helpful to predict an individual's health status and aging disease risk. Although it is already known for years that cumulative epigenetic changes occur upon aging, DNA methylation patterns were only recently used to construct an epigenetic clock predictor for biological age, which is a measure of how well your body functions compared to your chronological age. Moreover, the epigenetic DNA methylation clock signature is increasingly applied as a biomarker to estimate aging disease susceptibility and mortality risk. Finally, the epigenetic clock signature could be used as a lifestyle management tool to monitor healthy aging, to evaluate preventive interventions against chronic aging disorders and to extend healthy lifespan. Dissecting the mechanism of the epigenetic aging clock will yield valuable insights into the aging process and how it can be manipulated to improve health span.

Age Estimation with DNA: From Forensic DNA Fingerprinting to Forensic (Epi)Genomics: A Mini-Review

Forensic genetics developed from protein-based techniques a quarter of a century ago and became famous as “DNA fingerprinting,” this being based on restriction fragment length polymorphisms (RFLPs) of high-molecular-weight DNA. The amplification of much smaller short tandem repeat (STR) sequences using the polymerase chain reaction soon replaced RFLP analysis and advanced to become the gold standard in genetic identification. Meanwhile, STR multiplexes have been developed and made commercially available which simultaneously amplify up to 30 STR loci from as little as 15 cells or fewer. The enormous information content that comes with the large variety of observed STR genotypes allows for genetic individualisation (with the exception of identical twins). Carefully selected core STR loci form the basis of intelligence-led DNA databases that provide investigative leads by linking unsolved crime scenes and criminals through their matched STR profiles. Nevertheless, the success of modern DNA fingerprinting depends on the availability of reference material from suspects. In order to provide new investigative leads in cases where such reference samples are absent, forensic scientists started to explore the prediction of phenotypic traits from the DNA of the evidentiary sample. This paradigm change now uses DNA and epigenetic markers to forecast characteristics that are useful to triage further investigative work. So far, the best investigated externally visible characteristics are eye, hair and skin colour, as well as geographic ancestry and age. Information on the chronological age of a stain donor (or any sample donor) is elemental for forensic investigations in a number of aspects and has, therefore, been explored by researchers in some detail. Among different methodological approaches tested to date, the methylation-sensitive analysis of carefully selected DNA markers (CpG sites) has brought the most promising results by providing prediction accuracies of ±3–4 years, which can be comparable to, or even surpass those from, eyewitness reports. This mini-review puts recent developments in age estimation via (epi)genetic methods in the context of the requirements and goals of forensic genetics and highlights paths to follow in the future of forensic genomics.

Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures

Aging is reflected by highly reproducible DNA methylation (DNAm) changes that open new perspectives for estimation of chronological age in legal medicine. DNA can be harvested non-invasively from cells at the inside of a person's cheek using buccal swabs - but these specimens resemble heterogeneous mixtures of buccal epithelial cells and leukocytes with different epigenetic makeup. In this study, we have trained an age predictor based on three age-associated CpG sites (associated with the genesPDE4C, ASPA, and ITGA2B) for swab samples to reach a mean absolute deviation (MAD) between predicted and chronological age of 4.3 years in a training set and of 7.03 years in a validation set. Subsequently, the composition of buccal epithelial cells versus leukocytes was estimated by two additional CpGs (associated with the genes CD6 and SERPINB5). Results of this "Buccal-Cell-Signature" correlated with cell counts in cytological stains (R2 = 0.94). Combination of cell type-specific and age-associated CpGs into one multivariate model enabled age predictions with MADs of 5.09 years and 5.12 years in two independent validation sets. Our results demonstrate that the cellular composition in buccal swab samples can be determined by DNAm at two cell type-specific CpGs to improve epigenetic age predictions.

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 (27K/450K). 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=7.5years), 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.

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.

Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes

Reliable methodology for predicting the age of mature dogs is currently unavailable. In this study, amplicon sequencing of 50 blood samples obtained from diseased dogs was used to measure methylation in seven DNA regions. Significant correlations between methylation level and age were identified in four of the seven regions. These four regions were then tested in samples from 31 healthy toy poodles, and correlations were detected in two regions. The age of another 11 dogs was predicted using data from the diseased dogs and the healthy poodles. The mean difference between the actual and calculated ages was 34.3 and 23.1 months, respectively. Further research is needed to identify additional sites of age-related methylation and allow accurate age prediction in dogs.

LINE-1 DNA methylation: A potential forensic marker for discriminating monozygotic twins

Discriminating individuals within a pair of monozygotic (MZ) twins using genetic markers remains unresolved. This inability causes problems in criminal or paternity cases involving MZ twins as suspects or alleged fathers. Our previous study showed DNA methylation differences in interspersed repeat sequences such as Alu and LINE-1 within pairs of newborn MZ twins. To further evaluate the possible value of LINE-1 DNA methylation for discriminating MZ twins, this study investigated the LINE-1 DNA methylation of a large number of twins. We collected blood samples and buccal cell samples from 119 pairs of MZ and 57 pairs of dizygotic (DZ) twins. Genomic DNA was extracted and LINE-1 methylation level was detected using bisulfite pyrosequencing. The mean methylation level of the three CpG sites in the blood sample among the 176 unrelated individuals was 76.60% and 70.08% in buccal samples. This difference was significant, indicating the tissue specificity of LINE-1 DNA methylation. Among 119 pairs of MZ twins, 15 pairs could be discriminated according to the difference of CpG methylation level between them, which accounted for 12.61% of total number of MZ pairs. As for DZ twins, 10 pairs had significant differences between two individuals, which accounted for 17.54% of the total 57 DZ pairs. In conclusion, there are global DNA methylation differences within some healthy concordant monozygotic (MZ) twin pairs. LINE-1 DNA methylation might be a potential marker for helping to discriminate individuals within MZ twin pairs, and the tissue specificity must be considered in practice.