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

New aspects of dental implants and DNA technology in human identification

Missing, ineligible or delayed reference data to establish conventional dental or DNA identification are common scenarios in forensic practice. Therefore, it is worthwhile to explore new avenues that facilitate human identification. Due to the recent remarkable evolution in the prosthetic dental restorations based on dental implants and the emergence of novel DNA technologies utilized to infer the biological profile, the identification process has become easier than ever before. We report on a characteristic case, which highlights the particular importance of dental implants and DNA approaches in the prospective investigations for human identification. The aim of this publication is to focus on the possibility of identifying the batch numbers, even if they were not engraved in dental implants, making antemortem dental records of dental implants more easily accessible to establish a comparative dental identification. In addition, the reported case presents the supplementary data yielded through estimating the epigenetic age using DNA methylation as well as the biogeographical origin using Y-Haplotype and mitochondrial DNA analyses. Our results demonstrate that expanded oral implant investigations that also include implants extraction and comprehensive microscopic measurements can lead to identifying their batch numbers despite the numerous number of implants systems manufactured and distributed worldwide. Data saved by dental implant manufacturers can be very supportive and represent additional reference data for dental identification, when antemortem dental records are still missing. Furthermore, DNA methylation and mitochondrial DNA analyses can support the progress of investigation.

DNA methylation-based biomarkers and the epigenetic clock theory of ageing

Identifying and validating molecular targets of interventions that extend the human health span and lifespan has been difficult, as most clinical biomarkers are not sufficiently representative of the fundamental mechanisms of ageing to serve as their indicators. In a recent breakthrough, biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course. These 'epigenetic clocks' link developmental and maintenance processes to biological ageing, giving rise to a unified theory of life course. Epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age?

Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing

DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and have rarely, if ever, been evaluated and intercompared in an independent validation study. Here, for the first time, we evaluate and compare six blood-based age prediction models (Bekaert¹, Park², Thong³, Weidner⁴, and the Zbiec-Piekarska 1⁵ and Zbiec-Piekarska 2⁶), using DNA methylation analysis by pyrosequencing on 100 blood samples from French individuals aged between 19–65 years. For each model, we perform correlation analysis and evaluate age-prediction performance (mean absolute deviation (MAD) and standard error of the estimate (SEE)). The best age-prediction performances were found with the Bekaert and Thong models (MAD of 4.5–5.2, SEE of 6.8–7.2), followed by the Zbiec-Piekarska 1 model (MAD of 6.8 and SEE of 9.2), while the Park, Weidner and Zbiec-Piekarska 2 models presented lower performances (MAD of 7.2–8.7 and SEE of 9.2–10.3). Given these results, we recommend performing systematic, independent evaluation of all age prediction models on a same cohort to validate the different models and compare their performance.

Studies of hTERT DNA methylation assays on the human age prediction

As an important aspect of epigenetics, DNA methylation has been proven to be suitable for forensic DNA analysis. By detecting changes in DNA methylation, it is desirable to construct a model of age patterns associated with it to infer the age of the individual. The hTERT gene methylation is closely related to tumors, but there are few reports on the relationship between hTERT gene promoter methylation and age. In this study, we utilized the methylation-specific polymerase chain reaction and real-time PCR (relative quantification and absolute quantification) approach to explore the connection between hTERT DNA methylation and age prediction. We fit three models for age prediction based on methylation assay for 90 blood samples from donors aged 1-79 years old. Among them, the model of absolute quantification of real-time enabled the age prediction with R² = 0.9634. We verified the linear regression model with a validation set of 30 blood samples where prediction average error was 4.29 years. Generally, this reliable method improves the DNA methylation analysis of forensic samples.

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.

An integrative module analysis of DNA methylation landscape in aging

To investigate the molecular mechanism of aging, the combination of module analysis and DNA methylation data was used to detect dynamically controlled modules for aging. Multiple differential expression networks (DENs) were constructed based on the microarray profiles across different aging groups (<70 years, 70–80 years, and >80 years). Next, a module-based approach was utilized to extract the common candidate modules across all age groups. We used Module Connectivity Dynamic Score (MCDS) to quantify the connectivity change of the common modules among the different age groups. Functional analyses were implemented for the genes in the common modules to further identify the significant biological processes. A total of two DENs were constructed. Overall 657 informative genes were screened out. When false discovery rate (FDR) was set as 0.05, we found that 148 modules were significant. Only 1 significant 2-differential modules (DMs) (module 493) with dynamic changes was discovered. Significantly, the genes in the module 493 participated in 7 significant pathways, including pentose phosphate pathway, carbon metabolism, and citrate cycle (TCA cycle). In conclusion, pathway functions [pentose phosphate pathway, carbon metabolism, citrate cycle (TCA cycle), chromosomal instability, ateroid biosynthesis, PPAR signaling pathway, and immune response] may serve as potential therapeutic targets in aging.

Epigenetic Clock: Just a Convenient Marker or an Active Driver of Aging?

A global DNA hypomethylation and local changes in the methylation levels of specific DNA loci occur during aging in mammals. Global hypomethylation mainly affects highly methylated repeat sequences, such as transposable elements; it is an essentially stochastic process usually referred to as "epigenetic drift." Specific changes in DNA methylation affect various genome sequences and could be either hypomethylation or hypermethylation, but the prevailing tendencies are hypermethylation of promoter sequences associated with CpG islands and hypomethylation of CpG poor genes. Methylation levels of multiple CpG sites display a strong correlation to age common between individuals of the same species. Collectively, methylation of such CpG sites could be used as "epigenetic clocks" to predict biological age. Furthermore, the discrepancy between epigenetic and chronological ages could be predictive of all-cause mortality and multiple age-associated diseases. Random changes in DNA methylation (epigenetic drift) could also affect the aging phenotype, causing accidental changes in gene expression and increasing the transcriptional noise between cells of the same tissue. Both effects could become detrimental to tissue functioning and cause a gradual decline in organ function during aging. Strong evidence shows that epigenetic systems contribute to lifespan control in various organisms. Similar to other cell systems, the epigenome is prone to gradual degradation due to the genome damage, stressful agents and other aging factors. However, unlike mutations and many other hallmarks of aging, age-related epigenetic changes could be fully or partially reversed to a "young" state.

Molecular markers used in forensic genetics

Forensic genetics is a field that has become subject to increasing interest in recent years. Both the technology and the markers used for forensic purposes have changed since the 1980s. The minisatellite sequences used in the famous Pitchfork case introduced genetics to the forensic sciences. Minisatellite sequences have now been replaced by more sensitive microsatellite markers, which have become the basis for the creation of genetic profile databases. Modern molecular methods also exploit single nucleotide polymorphisms, which are often the only way to identify degraded DNA samples. The same type of variation is taken into consideration in attempting to establish the ethnicity of a perpetrator and to determine phenotypic traits such as the eye or hair colour of the individual who is the source of the genetic material. This paper contains a review of the techniques and molecular markers used in human and animal forensic genetics, and also presents the potential trends in forensic genetics such as phenotyping.

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.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.

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.

Multi-tissue DNA methylation age: Molecular relationships and perspectives for advancing biomarker utility

The multi-tissue DNA methylation estimator of chronological age (DNAm-age) has been associated with a wide range of exposures and health outcomes. Still, it is unclear how DNAm-age can have such broad relationships and how it can be best utilized as a biomarker. Understanding DNAm-age's molecular relationships is a promising approach to address this critical knowledge gap. In this review, we discuss the existing literature regarding DNAm-age's molecular relationships in six major categories: animal model systems, cancer processes, cellular aging processes, immune system processes, metabolic processes, and nucleic acid processes. We also present perspectives regarding the future of DNAm-age research, including the need to translate a greater number of ongoing research efforts to experimental and animal model systems.

Proof of concept study of age-dependent DNA methylation markers across different tissues by massive parallel sequencing

The use of DNA methylation (DNAm) for chronological age determination has been widely investigated within the last few years for its application within the field of forensic genetics. The majority of forensic studies are based on blood, saliva, and buccal cell samples, respectively. Although these types of samples represent an extensive amount of traces found at a crime scene or are readily available from individuals, samples from other tissues can be relevant for forensic investigations. Age determination could be important for cases involving unidentifiable bodies and based on remaining soft tissue e.g. brain and muscle, or completely depend on hard tissue such as bone. However, due to the cell type specificity of DNAm, it is not evident whether cell type specific age-dependent CpG positions are also applicable for age determination in other cell types. Within this pilot study, we investigated whether 13 previously selected age-dependent loci based on whole blood analysis including amongst others ELOVL2, TRIM59, F5, and KLF14 also have predictive value in other forensically relevant tissues. Samples of brain, bone, muscle, buccal swabs, and whole blood of 29 deceased individuals (age range 0-87 years) were analyzed for these 13 age-dependent markers using massive parallel sequencing. Seven of these loci did show age-dependency in all five tissues. The change of DNAm during lifetime was different in the set of tissues analyzed, and sometimes other CpG sites within the loci showed a higher age-dependency. This pilot study shows the potential of existing blood DNAm markers for age-determination to analyze other tissues than blood. We identified seven known blood-based DNAm markers for use in muscle, brain, bone, buccal swabs, and blood. Nevertheless, a different reference set for each tissue is needed to adapt for tissue-specific changes of the DNAm over time.

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.

Tracking age-correlated DNA methylation markers in the young

DNA methylation is the most extensively studied epigenetic signature, with a large number of studies reporting age-correlated CpG sites in overlapping genes. However, most of these studies lack sample coverage of individuals under 18 years old and therefore little is known about the progression of DNA methylation patterns in children and adolescents. In the present study we aimed to select candidate age-correlated DNA methylation markers based on public datasets from Illumina BeadChip arrays and previous publications, then to explore the resulting markers in 209 blood samples from donors aged between 2 to 18 years old using the EpiTYPER® DNA methylation analysis system. Results from our analyses identified six genes highly correlated with age in the young, in particular the gene KCNAB3, which indicates its potential as a highly informative and specific age biomarker for childhood and adolescence. We outline a preliminary age prediction model based on quantile regression that uses data from the six CpG sites most strongly correlated with age ranges extended to include children and adolescents.

Hypermethylation of TRIM59 and KLF14 Influences Cell Death Signaling in Familial Alzheimer's Disease

Epigenetic mechanisms play an important role in the development and progression of various neurodegenerative diseases. Abnormal methylation of numerous genes responsible for regulation of transcription, DNA replication, and apoptosis has been linked to Alzheimer's disease (AD) pathology. We have recently performed whole transcriptome profiling of familial early-onset Alzheimer's disease (fEOAD) patient-derived fibroblasts. On this basis, we demonstrated a strong dysregulation of cell cycle checkpoints and DNA damage response (DDR) in both fibroblasts and reprogrammed neurons. Here, we show that the aging-correlated hypermethylation of KLF14 and TRIM59 genes associates with abnormalities in DNA repair and cell cycle control in fEOAD. Based on the resulting transcriptome networks, we found that the hypermethylation of KLF14 might be associated with epigenetic regulation of the chromatin organization and mRNA processing followed by hypermethylation of TRIM59 likely associated with the G2/M cell cycle phase and p53 role in DNA repair with BRCA1 protein as the key player. We propose that the hypermethylation of KLF14 could constitute a superior epigenetic mechanism for TRIM59 hypermethylation. The methylation status of both genes affects genome stability and might contribute to proapoptotic signaling in AD. Since this study combines data obtained from various tissues from AD patients, it reinforces the view that the genetic methylation status in the blood may be a valuable predictor of molecular processes occurring in affected tissues. Further research is necessary to define a detailed role of TRIM59 and KLF4 in neurodegeneration of neurons.

Enhanced sensitivity of CpG island search and primer design based on predicted CpG island position

DNA methylation has important biological roles, such as gene expression regulation, as well as practical applications in forensics, such as in body fluid identification and age estimation. DNA methylation often occurs in the CpG site, and methylation within the CpG islands affects various cellular functions and is related to tissue-specific identification. Several programs have been developed to identify CpG islands; however, the size, location, and number of predicted CpG islands are not identical due to different search algorithms. In addition, they only provide structural information for predicted CpG islands without experimental information, such as primer design. We developed an analysis pipeline package, CpGPNP, to integrate CpG island prediction and primer design. CpGPNP predicts CpG islands more accurately and sensitively than other programs, and designs primers easily based on the predicted CpG island locations. The primer design function included standard, bisulfite, and methylation-specific PCR to identify the methylation of particular CpG sites. In this study, we performed CpG island prediction on all chromosomes and compared CpG island search performance of CpGPNP with other CpG island prediction programs. In addition, we compared the position of primers designed for a specific region within the predicted CpG island using other bisulfite PCR primer programs. The primers designed by CpGPNP were used to experimentally verify the amplification of the target region of markers for body fluid identification and age estimation. CpGPNP is freely available at http://forensicdna.kr/cpgpnp/.

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.

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.

From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence

Human genetic variation is a major resource in forensics, but does not allow all forensically relevant questions to be answered. Some questions may instead be addressable via epigenomics, as the epigenome acts as an interphase between the fixed genome and the dynamic environment. We envision future forensic applications of DNA methylation analysis that will broaden DNA-based forensic intelligence. Together with genetic prediction of appearance and biogeographic ancestry, epigenomic lifestyle prediction is expected to increase the ability of police to find unknown perpetrators of crime who are not identifiable using current forensic DNA profiling.

DNA methylation levels in candidate genes associated with chronological age in mammals are not conserved in a long-lived seabird

Most seabirds do not have any outward identifiers of their chronological age, so estimation of seabird population age structure generally requires expensive, long-term banding studies. We investigated the potential to use a molecular age biomarker to estimate age in short-tailed shearwaters (Ardenna tenuirostris). We quantified DNA methylation in several A. tenuirostris genes that have shown age-related methylation changes in mammals. In birds ranging from chicks to 21 years of age, bisulphite treated blood and feather DNA was sequenced and methylation levels analysed in 67 CpG sites in 13 target gene regions. From blood samples, five of the top relationships with age were identified in KCNC3 loci (CpG66: R² = 0.325, p = 0.019). In feather samples ELOVL2 (CpG42: R² = 0.285, p = 0.00048) and EDARADD (CpG46: R² = 0.168, p = 0.0067) were also weakly correlated with age. However, the majority of markers had no clear association with age (of 131 comparisons only 12 had a p-value < 0.05) and statistical analysis using a penalised lasso approach did not produce an accurate ageing model. Our data indicate that some age-related signatures identified in orthologous mammalian genes are not conserved in the long-lived short tailed shearwater. Alternative molecular approaches will be required to identify a reliable biomarker of chronological age in these seabirds.

Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory application for cigarette butts

There is high demand for forensic age prediction in actual crime investigations. In this study, a novel age prediction model for saliva samples using methylation-sensitive high resolution melting (MS-HRM) was developed. The methylation profiles of ELOVL2 and EDARADD showed high correlations with age and were used to predict age with support vector regression. ELOVL2 was first reported as an age predictive marker for saliva samples. The prediction model showed high accuracy with a mean absolute deviation (MAD) from chronological age of 5.96 years among 197 training samples. The model was further validated with an additional 50 test samples (MAD = 6.25). In addition, the age prediction model was applied to saliva extracted from seven cigarette butts, as in an actual crime scene. The MAD (7.65 years) for these samples was slightly higher than that of intact saliva samples. A smoking habit or the ingredients of cigarettes themselves did not significantly affect the prediction model and could be ignored. MS-HRM provides a quick (2 hours) and cost-effective (95% decreased compared to that of DNA chips) method of analysis. Thus, this study may provide a novel strategy for predicting the age of a person of interest in actual crime scene investigations.

Measuring Animal Age with DNA Methylation: From Humans to Wild Animals

DNA methylation (DNAm) is a key mechanism for regulating gene expression in animals and levels are known to change with age. Recent studies have used DNAm changes as a biomarker to estimate chronological age in humans and these techniques are now also being applied to domestic and wild animals. Animal age is widely used to track ongoing changes in ecosystems, however chronological age information is often unavailable for wild animals. An ability to estimate age would lead to improved monitoring of (i) population trends and status and (ii) demographic properties such as age structure and reproductive performance. Recent studies have revealed new examples of DNAm age association in several new species increasing the potential for developing DNAm age biomarkers for a broad range of wild animals. Emerging technologies for measuring DNAm will also enhance our ability to study age-related DNAm changes and to develop new molecular age biomarkers.

Detection and evaluation of DNA methylation markers found at SCGN and KLF14 loci to estimate human age

Recent developments in the analysis of epigenetic DNA methylation patterns have demonstrated that certain genetic loci show a linear correlation with chronological age. It is the goal of this study to identify a new set of epigenetic methylation markers for the forensic estimation of human age. A total number of 27 CpG sites at three genetic loci, SCGN, DLX5 and KLF14, were examined to evaluate the correlation of their methylation status with age. These sites were evaluated using 72 blood samples and 91 saliva samples collected from volunteers with ages ranging from 5 to 73 years. DNA was bisulfite modified followed by PCR amplification and pyrosequencing to determine the level of DNA methylation at each CpG site. In this study, certain CpG sites in SCGN and KLF14 loci showed methylation levels that were correlated with chronological age, however, the tested CpG sites in DLX5 did not show a correlation with age. Using a 52-saliva sample training set, two age-predictor models were developed by means of a multivariate linear regression analysis for age prediction. The two models performed similarly with a single-locus model explaining 85% of the age variance at a mean absolute deviation of 5.8 years and a dual-locus model explaining 84% of the age variance with a mean absolute deviation of 6.2 years. In the validation set, the mean absolute deviation was measured to be 8.0 years and 7.1 years for the single- and dual-locus model, respectively. Another age predictor model was also developed using a 40-blood sample training set that accounted for 71% of the age variance. This model gave a mean absolute deviation of 6.6 years for the training set and 10.3years for the validation set. The results indicate that specific CpGs in SCGN and KLF14 can be used as potential epigenetic markers to estimate age using saliva and blood specimens. These epigenetic markers could provide important information in cases where the determination of a suspect's age is critical in developing investigative leads.

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

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

DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups

Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer’s disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for TRIM59 and KLF14 markers in the group of early onset Alzheimer’s disease suggesting accelerated aging of patients. In the Graves’ disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for TRIM59 and FHL2 with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the ELOVL2 and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.

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.

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=3.15years). 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.

Forensic DNA methylation profiling from evidence material for investigative leads

DNA methylation is emerging as an attractive marker providing investigative leads to solve crimes in forensic genetics. The identification of body fluids that utilizes tissue-specific DNA methylation can contribute to solving crimes by predicting activity related to the evidence material. The age estimation based on DNA methylation is expected to reduce the number of potential suspects, when the DNA profile from the evidence does not match with any known person, including those stored in the forensic database. Moreover, the variation in DNA implicates environmental exposure, such as cigarette smoking and alcohol consumption, thereby suggesting the possibility to be used as a marker for predicting the lifestyle of potential suspect. In this review, we describe recent advances in our understanding of DNA methylation variations and the utility of DNA methylation as a forensic marker for advanced investigative leads from evidence materials. [BMB Reports 2016; 49(7): 359-369]

Donor age and C1orf132/MIR29B2C determine age-related methylation signature of blood after allogeneic hematopoietic stem cell transplantation

Background

Our recent study demonstrated that DNA methylation status in a set of CpGs located in ELOVL2, C1orf132, TRIM59, KLF14, and FHL2 can accurately predict calendar age in blood. In the present work, we used these markers to evaluate the effect of allogeneic hematopoietic stem cell transplantation (HSCT) on the age-related methylation signature of human blood.

Methods

DNA methylation in 32 CpGs was investigated in 16 donor-recipient pairs using pyrosequencing. DNA was isolated from the whole blood collected from recipients 27–360 days (mean 126) after HSCT and from the donors shortly before the HSCT.

Results

It was found that in the recipients, the predicted age did not correlate with their calendar age but was correlated with the calendar age (r = 0.94, p = 4 × 10⁻⁸) and predicted age (r = 0.97, p = 5 × 10⁻¹⁰) of a respective donor. Despite this strong correlation, the predicted age of a recipient was consistently lower than the predicted age of a donor by 3.7 years (p = 7.8 × 10⁻⁴). This shift was caused by hypermethylation of the C1orf132 CpGs, for C1orf132 CpG_1. Intriguingly, the recipient-donor methylation difference correlated with calendar age of the donor (r = 0.76, p = 6 × 10⁻⁴). This finding could not trivially be explained by shifts of the major cellular factions of blood.

Conclusions

We confirm the single previous report that after HSCT, the age of the donor is the major determinant of age-specific methylation signature in recipient’s blood. A novel finding is the unique methylation dynamics of C1orf132 which encodes MIR29B2C implicated in the self-renewing of hematopoietic stem cells. This observation suggests that C1orf132 could influence graft function after HSCT.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0257-7) contains supplementary material, which is available to authorized users.

Simplified Assay for Epigenetic Age Estimation in Whole Blood of Adults

Biological age is not always concordant with chronological age and the departures are of interest for understanding how diseases and environmental insults affect tissue function, organismal health, and life expectancy. The best-known biological age biomarker is telomere length, but there are more accurate biomarkers as the recently developed based in epigenetic, transcriptomic, or biochemical changes. The most accurate are the epigenetic biomarkers based on specific changes in DNA methylation referred as DNA methylation age measures (DmAM). Here, we have developed and validated a new DmAM that addresses some limitations of the previously available. The new DmAM includes the study in whole blood (WB) of 8 CpG sites selected as the most informative on a training set of 390 healthy subjects. The 8 CpG DmAM showed better accuracy than other DmAM based in few CpG in an independent validation set of 335 subjects. Results were not significantly influenced by sex, smoking, or variation in blood cell subpopulations. In addition, the new 8 CpG DmAM was amenable to study in a single multiplex reaction done with methylation-sensitive single-nucleotide primer extension (MS-SNuPE), a methodology based on commercially available reagents and run in capillary electrophoresis sequencers. In this way, the high cost of DNA methylation microarrays or of a pyrosequencer, which are needed for alternative DmAM, was avoided. Performance of the DmAM with MS-SNuPE was assessed in a set of 557 donors, showing high call rate (>97%), low CV (<3.3%) and high accuracy (Mean Absolute Deviation = 6.07 years). Therefore, the 8 CpG DmAM is a feasible and accurate tool for assessing the epigenetic component of biological age in blood of adults.