Human age estimation from blood using mRNA, DNA methylation, DNA rearrangement, and telomere length

A methylation panel of 10 CpGs for accurate age inference via stepwise conditional epigenome-wide association study

Estimating individual age from DNA methylation at age associated CpG sites may provide key information facilitating forensic investigations. Systematic marker screening and feature selection play a critical role in ensuring the performance of the final prediction model. In the discovery stage, we screened for 811876 CpGs from whole blood of 2664 Chinese individuals ranging from 18 to 83 years of age based on a stepwise conditional epigenome-wide association study (SCEWAS). The SCEWAS identified 28 CpGs showing genome-wide significant and independent effects. Further restricting this panel to 10 most informative CpGs showed a tolerable loss of information. A linear model consisting of these 10 CpGs could explain 93% of the age variance (R2 = 0.93) in the training set (n = 2664). In an independent test set of Chinese individuals (n = 648), this model also provided highly accurate predictions (R2 = 0.85, mean absolute deviation, MAD = 3.20 years). The model was additionally validated in a public dataset of multiple ancestral origins (86 Europeans, 14 Asians, and 273 Africans) and the prediction accuracy reduced significantly (R2 = 0.85, MAD = 6.21 years), as might be expected due to different genomic backgrounds, sample sizes, and age ranges. Our 10 CpG model also outperformed the recently proposed 9-CpG model constructed in 390 Chinese males (R2 = 0.79 in test set). We also demonstrated that our SCEWAS approach outperformed the traditional EWAS and the elastic net approach in obtaining a small set of most age informative CpGs. Overall, our systematic genome-wide feature selection identified a small panel of 10 CpGs for accurate age estimation with high potential in forensic applications.

Reproducibility and validation of a targeted and flexible epigenetic clock for forensic applications

DNA methylation variants have been widely used as biomarkers of ageing and several mathematical models have been developed to estimate the biological age. More recently, DNA technology has triggered efforts toward the simplification of the array-based epigenetic clocks and targeted approaches, based on the assessment of a small number of CpG sites have been developed. Among the markers included in these clocks, ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 resulted to be the most strongly validated markers. We tested the reproducibility and validation of a previously developed targeted epigenetic clock purposely optimized for the measurement of chronological age in blood samples. The clock includes DNAm biomarkers strongly correlated with chronological age whose DNA methylation levels were measured by using a multiplex methylation SNaPshot assay. We found that epigenetic age, calculated using the developed clock, was highly correlated with age (r = 0.97) in a total of 201 blood samples covering a full spectrum of human ages. For 74 of these, methylation profiles of the whole genome were obtained through the Infinium Methylation EPIC v2.0 Kit which also allowed to estimate the most frequently used clocks of Horvath. These results show the potential of our efficient and affordable test for simultaneously measuring DNA methylation levels at multiple target CpG sites to assess chronological age. We observed a strong correlation between the prediction models for the analyzed CpG sites measured using the SNaPshot method and those obtained with the Illumina EPIC array, especially with the Horvath2 clock, which was specifically developed for DNA from skin and blood cells.

Forensic Age Estimation From Blood Samples by Combining DNA Methylation and MicroRNA Markers Using Droplet Digital PCR

Age estimation is important in criminal investigations and forensic practice, and extensive studies have focused on age determination based on DNA methylation (DNAm) and miRNA markers. Interestingly, it has been reported that combining different types of molecular omics data helps build more accurate predictive models. However, few studies have compared the application of combined DNAm and miRNA data to predict age in the same cohort. In this study, a novel multiplex droplet digital PCR (ddPCR) system that allows for the simultaneous detection of age-associated DNAm and miRNA markers, including KLF14, miR-106b-5p, and two reference genes (C-LESS-C1 and RNU6B), was developed. Next, we examined and calculated the methylation levels of KLF14 and relative expression levels of miR-106b-5p in 132 blood samples. The collected data were used to establish age prediction models. Finally, the optimal models were evaluated using bloodstain samples. The results revealed that the random forest (RF) model had a minimum mean absolute deviation (MAD) value of 3.51 years and a maximum R2 of 0.84 for the validation sets in the combined age prediction models. However, the MAD was 5.66 years and the absolute error ranged from 3.16 to 10.54 years for bloodstain samples. Larger sample sizes and validation datasets are required to confirm these results in future studies. Overall, a stable method for the detection of KLF14, miR-106b-5p, C-LESS-C1, and RNU6B by 4-plex ddPCR was successfully established, and our study suggests that combining DNAm and miRNA data can improve the accuracy of age prediction, which has potential applications in forensic science.

Assessing transcriptomic signatures of aging: Testing an mRNA marker panel for forensic age estimation of blood samples

Estimating the age of an unknown perpetrator can be a valuable tool in narrowing down a group of suspects. Research efforts to estimate the age of a stain donor have mainly focused on epigenetic modifications, but there is evidence that RNA expression patterns, i.e. the composition of the transcriptome, change with increasing age, which could be a promising molecular alternative for age prediction. In a previous study, we identified a total of 508 mRNA markers with age related expression from two blood whole transcriptome sequencing data sets, using differential expression analysis with DESeq2 and marker selection with lasso regression. For this study, the selected markers from both approaches were combined into an RNA-specific targeted MPS assay for the Ion Torrent platform and evaluated with 100 EDTA blood samples from healthy donors (aged between 23 and 73 years). We compared three different normalization methods for the obtained sequencing data and investigated the performance of various regression techniques for age prediction. The model based on elastic net regression and dSVA-normalized data exhibited the most robust performance, achieving an MAE of 9.29 years and a correlation of 0.57 between the chronological and predicted age. Although the use of a targeted approach instead of RNA-Seq offers several advantages in a forensic setting, we observed a considerable amount of unwanted variation in the targeted sequencing data. We conclude that it is challenging to detect distinct signals associated with chronological age.

Advancing Forensic Human Chronological Age Estimation: Biochemical, Genetic, and Epigenetic Approaches from the Last 15 Years: A Systematic Review

Forensic age estimation is crucial for identifying unknown individuals and narrowing suspect pools in criminal investigations. Over the past 15 years, significant progress has been made in using biochemical, genetic, and epigenetic markers to estimate chronological age.

METHODS

From research on PubMed a total of 155 studies, related to advancements in age prediction techniques, were selected following PRISMA guidelines. Studies considered eligible dealt with radiocarbon dating, aspartic acid racemization, mitochondrial DNA analysis, signal joint T-cell receptor excision circles, RNA analysis, telomeres, and DNA methylation in the last 15 years and were summarized in a table.

RESULTS

Despite these advancements, challenges persist, including variability in prediction accuracy, sample degradation, and the lack of standardization and reproducibility. DNA methylation emerged as the most promising approach capable of high accuracy across diverse populations and age ranges. Multimodal methods integrating several biomarkers show promise in improving reliability and addressing these limitations.

CONCLUSION

While significant progress has been made, further standardization, validation, and technological integration are needed to enhance forensic age estimation. These efforts are essential for meeting the growing demands of forensic science while addressing ethical and legal considerations.

Methylation-based forensic age estimation in blood, buccal cells, saliva and semen: A comparison of two technologies

Forensic age estimation of stain donors through DNA methylation has been intensively studied in recent years. To date, there are many published age estimation tools which are based on technologies including pyrosequencing, minisequencing, or MPS. With the implementation of such tools into routine forensic casework in many laboratories worldwide, there is a need for thorough evaluation and performance comparison. In this study, we tested published age estimation tools that are based on either minisequencing or MPS on four body fluids (blood, saliva, buccal cells and semen). All samples were analyzed with both technologies and the age estimates were compared. Biological replicates were taken from ten (blood, saliva, buccal cells) or 12 individuals (semen) to assess the reproducibility of each tool. Our study demonstrates high accuracy in estimating chronological age for various body fluids using both technologies, except for semen. The mean absolute errors (MAEs) ranged from three to five years for blood, saliva and buccal cells, while semen exhibited a higher MAE of seven to eight years. Despite the overall good performance for blood, saliva, and buccal cells, significant discrepancies were observed for some individuals both between the two technologies or when compared to their chronological age. Conclusively, we demonstrated that forensic age estimation tools based on two different technologies are similarly accurate for blood, saliva and buccal cells, while the semen tools need some adjustments before implementation into forensic casework. Our results could be helpful in the decision-making process for laboratories seeking to newly establish an age estimation workflow.

Epigenetic Clock: DNA Methylation as a Marker of Biological Age and Age-Associated Diseases

Age is one of the key criteria of human health used in practical medicine to predict the risk of common chronic diseases. However, biological age, which reflects the state of an individual organism, functional capabilities, social well-being, and risk of premature death from various causes, often does not coincide with chronological age. To determine biological age of a particular individuals and the rate of their aging, specific panels of DNA methylation markers called "epigenetic clock" (EC) were proposed. This review summarizes the data about the main types of ECs developed to date and their key characteristics. We described the results of works studying individual aging rates in common age-associated diseases and outlined main directions, development of which could expand application of ECs in fundamental and practical medicine. There is no doubt that revealing complex mechanisms underlying interaction between the rate of epigenetic aging and the risk of age-associated diseases could play a key role for prediction and early diagnosis, as well as for the development of preventive measures that could delay onset of the disease.

Investigation into temporal changes in the human bloodstain lipidome

Bloodstains are crucial pieces of physical evidences found at violent crime scenes, providing valuable information for reconstructing forensic cases. However, there is limited data on how bloodstain lipidomes change over time after deposition. Hence, we deployed a high-throughput high-performance liquid chromatography-mass spectrometry (HPLC-MS) approach to construct lipidomic atlases of bloodstains, whole blood, plasma, and blood cells from 15 healthy adults. A time-course analysis was also performed on bloodstains deposited for up to 6 months at room temperature (~ 25°C). The molecular levels of 60 out of 400 detected lipid species differed dramatically between bloodstain and whole blood samples, with major disturbances observed in membrane glycerophospholipids. More than half of these lipids were prevalent in the cellular and plasmic fractions; approximately 27% and 10% of the identified lipids were uniquely derived from blood cells and plasma, respectively. Furthermore, a subset of 65 temporally dynamic lipid species arose across the 6-month room-temperature deposition period, with decreased triacylglycerols (TAGs) and increased lysophosphatidylcholines (LPCs) as representatives, accounting for approximately 8% of the total investigated lipids. The instability of lipids increased linearly with time, with the most variability observed in the first 10 days. This study sheds light on the impact of air-drying bloodstains on blood components at room temperature and provides a list of potential bloodstain lipid markers for determining the age of bloodstains.

Epigenetic Age Estimation by Detecting DNA Methylation Status in Buccal Swabs

The analysis of DNA methylation (DNAm) levels at specific CpG sites represents one of the most promising molecular techniques for estimating an individual's age. To date, a considerable number of studies have reported the development of age prediction models on the basis of DNAm in body fluids, with only a few utilizing buccal swabs. The objective of this study was to identify age-dependent methylation CpG sites in three different genes (HOXC4, TRIM59, and ELOVL2) in buccal swab samples from the Chinese Han population. A total of 461 buccal swabs, with an age range of 0.4-80.8 years, were divided into a training set (n = 325) and a validation set (n = 136). Samples were analyzed by pyrosequencing in order to identify age-related genes with correlation coefficient. A random forest regression model was ultimately proposed, including eight CpGs in three genes, with a mean absolute error (MAE) of 2.119 years. The model performs independent validation set with an MAE of 4.391 years. Our findings illustrate that buccal swabs present a suitable alternative to biological traces for age prediction based on DNAm pattern using pyrosequencing and random forest regression, offering the additional advantage of being collected noninvasively.

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual's age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci's biological significance.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

MC1R and age heteroclassification of face phenotypes in the Rio Grande do Sul population

BACKGROUND

Forensic DNA phenotyping (FDP) consists of the use of methodologies for predicting externally visible characteristics (EVCs) from the genetic material of biological samples found in crime scenes and has proven to be a promising tool in aiding human identification in police activities. Currently, methods based on multiplex assays and statistical models of prediction of EVCs related to hair, skin, and iris pigmentation using panels of SNP and INDEL biomarkers have already been developed and validated by the forensic scientific community. As well as traces of pigmentation, an individual's perceived age (PA) can also be considered an EVC and its estimation in unknown individuals can be useful for the progress of investigations. Liu and colleagues (2016) were pioneers in evidencing that, in addition to lifestyle and environmental factors, the presence of SNP and INDEL variants in the MC1R gene - which encodes a transmembrane receptor responsible for regulating melanin production - seems to contribute to an individual's PA. The group highlighted the association between these MC1R gene polymorphisms and the PA in the European population, where carriers of risk haplotypes appeared to be up to 2 years older in comparison to their chronological age (CA).

PURPOSE

Understanding that genotype-phenotype relationships cannot be extrapolated between different population groups, this study aimed to test this hypothesis and verify the applicability of this variant panel in the Rio Grande do Sul admixed population.

METHODS

Based on genomic data from a sample of 261 volunteers representative of gaucho population and using a multiple linear regression (MLR) model, our group was able to verify a significant association among nine intronic variants in loci adjacent to MC1R (e.g., AFG3L1P, TUBB3, FANCA) and facial age appearance, whose PA was defined after age heteroclassification of standard frontal face images through 11 assessors.

RESULTS

Different from that observed in European populations, our results show that the presence of effect alleles (R) of the selected variants in our sample influenced both younger and older face phenotypes. The influence of each variant on PA is expressed as β values.

CONCLUSIONS

There are important molecular mechanisms behind the effects of MC1R locus on PA, and the genomic background of each population seems to be crucial to determine this influence.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

Spitting in the wind?-The challenges of RNA sequencing for biomarker discovery from saliva

Forensic trace contextualization, i.e., assessing information beyond who deposited a biological stain, has become an issue of great and steadily growing importance in forensic genetic casework and research. The human transcriptome encodes a wide variety of information and thus has received increasing interest for the identification of biomarkers for different aspects of forensic trace contextualization over the past years. Massively parallel sequencing of reverse-transcribed RNA ("RNA sequencing") has emerged as the gold standard technology to characterize the transcriptome in its entirety and identify RNA markers showing significant expression differences not only between different forensically relevant body fluids but also within a single body fluid between forensically relevant conditions of interest. Here, we analyze the quality and composition of four RNA sequencing datasets (whole transcriptome as well as miRNA sequencing) from two different research projects (the RNAgE project and the TrACES project), aiming at identifying contextualizing forensic biomarker from the forensically relevant body fluid saliva. We describe and characterize challenges of RNA sequencing of saliva samples arising from the presence of oral bacteria, the heterogeneity of sample composition, and the confounding factor of degradation. Based on these observations, we formulate recommendations that might help to improve RNA biomarker discovery from the challenging but forensically relevant body fluid saliva.

Selecting mRNA markers in blood for age estimation of the donor of a biological stain

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.

Longitudinal changes and variation in human DNA methylation analysed with the Illumina MethylationEPIC BeadChip assay and their implications on forensic age prediction

DNA methylation, a pivotal epigenetic modification, plays a crucial role in regulating gene expression and is known to undergo dynamic changes with age. The present study investigated epigenome-wide methylation profiles in 64 individuals over two time points, 15 years apart, using the Illumina EPIC850k arrays. A mixed-effects model identified 2821 age-associated differentially methylated CpG positions (aDMPs) with a median rate of change of 0.18% per year, consistent with a 10-15% change during a human lifespan. Significant variation in the baseline DNA methylation levels between individuals of similar ages as well as inconsistent direction of change with time across individuals were observed for all the aDMPs. Twenty-three of the 2821 aDMPs were previously incorporated into forensic age prediction models. These markers displayed larger changes in DNA methylation with age compared to all the aDMPs and less variation among individuals. Nevertheless, the forensic aDMPs also showed inter-individual variations in the direction of DNA methylation changes. Only cg16867657 in ELOVL2 exhibited a uniform direction of the age-related change among the investigated individuals, which supports the current knowledge that CpG sites in ELOVL2 are the best markers for age prediction.

Improved age estimation from semen using sperm-specific age-related CpG markers

Accurate age estimation from semen has the potential to greatly narrow the pool of unidentified suspects in sexual assault investigations. However, previous efforts utilizing semen age-related CpG (AR-CpG) markers have shown lower accuracy compared to blood AR-CpG-based methods. This discrepancy may be attributed to DNA methylation (DNAm) interferences from "round cells" such as leukocytes and immature sperm cells in semen. This study aimed to develop age calculators based on sperm-specific AR-CpG markers and to achieve performance-improved age estimates from sperm DNA. Through an analysis of publicly available MethylationEPIC microarray data from 90 sperm samples of healthy males aged 22-51 years, we identified 31 sperm-specific AR-CpG markers with absolute Pearson's R values > 0.5 and Benjamini-Hochberg adjusted p values < 0.013. The top 19 AR-CpG markers with the largest absolute R values and beta ranges > 0.10, along with 3 reported semen AR-CpG markers (cg06304190, cg06979108, and cg12837463), were integrated into two methylation SNaPshot panels (Ⅰ and Ⅱ), each containing 11 markers. The 21 qualified AR-CpG markers showed absolute R values ≥ 0.427 in an independent validation cohort of 253 sperm DNA samples (22-67 years), with cg21843517 exhibiting the strongest age correlation (R = 0.853). The optimal models, constructed using sperm DNAm data of the training set (n = 214, 22-67 years) and markers from panel Ⅰ (n = 11), panel Ⅱ (n = 10), or both panels, achieved mean absolute errors (MAEs) of 2.526-4.746, 3.890-5.715, and > 9.800 years on the test sets of sperm (n = 39, 23-64 years), semen (same donors as the sperm test set), and whole blood (n = 40, 22-65 years), respectively. The simplified models incorporating 3, 5, 9, or 14 AR-CpG markers (MAE = 2.918-4.139 years for sperm) still outperformed the Lee et al. original model (MAE = 6.444 years for semen) and the reconstructed panel Lee model (MAE = 6.011 years for sperm). The final models, utilizing all sperm DNAm data (n = 253) and markers from panel Ⅰ, panel Ⅱ, or both panels, yielded mean MAEs of 2.587, 2.766, and 2.200 years, respectively, on the 50 test sets generated by 5 repeats of 10-fold cross-validations. Additionally, multiple markers in both panels demonstrated the ability to discern sperm or semen from blood with 100% accuracy. In summary, our study substantiates the potential of sperm-specific AR-CpG markers for precise age estimation from sperm DNA, providing an improved toolset for forensic investigations.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives

Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.

Age determination through DNA methylation patterns in fingernails and toenails

Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0-96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.

Forensic age estimation from human blood using age-related microRNAs and circular RNAs markers

Aging is a complicated process characterized by progressive and extensive changes in physiological homeostasis at the organismal, tissue, and cellular levels. In modern society, age estimation is essential in a large variety of legal rights and duties. Accumulating evidence suggests roles for microRNAs (miRNAs) and circular RNAs (circRNAs) in regulating numerous processes during aging. Here, we performed circRNA sequencing in two age groups and analyzed microarray data of 171 healthy subjects (17-104 years old) downloaded from Gene Expression Omnibus (GEO) and ArrayExpress databases with integrated bioinformatics methods. A total of 1,403 circular RNAs were differentially expressed between young and old groups, and 141 circular RNAs were expressed exclusively in elderly samples while 10 circular RNAs were expressed only in young subjects. Based on their expression pattern in these two groups, the circular RNAs were categorized into three classes: age-related expression between young and old, age-limited expression-young only, and age-limited expression-old only. Top five expressed circular RNAs among three classes and a total of 18 differentially expressed microRNAs screened from online databases were selected to validate using RT-qPCR tests. An independent set of 200 blood samples (20-80 years old) was used to develop age prediction models based on 15 age-related noncoding RNAs (11 microRNAs and 4 circular RNAs). Different machine learning algorithms for age prediction were applied, including regression tree, bagging, support vector regression (SVR), random forest regression (RFR), and XGBoost. Among them, random forest regression model performed best in both training set (mean absolute error = 3.68 years, r = 0.96) and testing set (MAE = 6.840 years, r = 0.77). Models using one single type of predictors, circular RNAs-only or microRNAs-only, result in bigger errors. Smaller prediction errors were shown in males than females when constructing models according to different-sex separately. Putative microRNA targets (430 genes) were enriched in the cellular senescence pathway and cell homeostasis and cell differentiation regulation, indirectly indicating that the microRNAs screened in our study were correlated with development and aging. This study demonstrates that the noncoding RNA aging clock has potential in predicting chronological age and will be an available biological marker in routine forensic investigation to predict the age of biological 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.

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNAs based on the previously reported genome-wide expression data. In addition, predicted models for age estimations were built by three machine learning methods. We identified 283 differentially expressed mRNAs between two groups with different age ranges. Nine mRNAs out of 283 mRNAs showed different expression patterns between smokers and non-smokers and were eliminated from the following analysis. Age-associated mRNAs were further screened from the remaining mRNAs by the cross-validation error analysis of random forest. Finally, 14 mRNAs were chosen to build the model for age predictions. These 14 mRNAs showed relatively high correlations with age. Furthermore, we found that random forest showed the optimal performance for age prediction in comparison to the generalized linear model and support vector machine. To sum up, the 14 age-associated mRNAs identified in this study could be viewed as valuable markers for age estimations and studying the aging process.

Circular RNA as a Potential Biomarker for Forensic Age Prediction

In forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulations on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and C.elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs’ potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20–62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNA candidates including the false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 30 healthy unrelated subjects by RT-qPCR, and finally, 5 age-related circRNAs were chosen for final age prediction models using 100 samples of 19–73 years old. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, regression tree and RFR performed better than the others with MAE values of 8.767 years (S.rho = 0.6983) and 9.126 years (S.rho = 0.660), respectively. Sex effect analysis showed age prediction models significantly yielded smaller prediction MAE values for males than females (MAE = 6.133 years for males, while 10.923 years for females in the regression tree model). In the current study, we first used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.

Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models

Epigenetic clock, a highly accurate age estimator based on DNA methylation (DNAm) level, is the basis for predicting mortality/morbidity and elucidating the molecular mechanism of aging, which is of great significance in forensics, justice, and social life. Herein, we integrated machine learning (ML) algorithms to construct blood epigenetic clock in Southern Han Chinese (CHS) for chronological age prediction. The correlation coefficient (r) meta-analyses of 7,084 individuals were firstly implemented to select five genes (ELOVL2, C1orf132, TRIM59, FHL2, and KLF14) from a candidate set of nine age-associated DNAm biomarkers. The DNAm-based profiles of the CHS cohort (240 blood samples differing in age from 1 to 81 years) were generated by the bisulfite targeted amplicon pyrosequencing (BTA-pseq) from 34 cytosine-phosphate-guanine sites (CpGs) of five selected genes, revealing that the methylation levels at different CpGs exhibit population specificity. Furthermore, we established and evaluated four chronological age prediction models using distinct ML algorithms: stepwise regression (SR), support vector regression (SVR-eps and SVR-nu), and random forest regression (RFR). The median absolute deviation (MAD) values increased with chronological age, especially in the 61–81 age category. No apparent gender effect was found in different ML models of the CHS cohort (all p &gt; 0.05). The MAD values were 2.97, 2.22, 2.19, and 1.29 years for SR, SVR-eps, SVR-nu, and RFR in the CHS cohort, respectively. Eventually, compared to the MAD range of the meta cohort (2.53–5.07 years), a promising RFR model (ntree = 500 and mtry = 8) was optimized with an MAD of 1.15 years in the 1–60 age categories of the CHS cohort, which could be regarded as a robust epigenetic clock in blood for age-related issues.

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.

Assessment of renal glomerulosclerosis and thickness of the carotid intima-media complex as a means of age estimation in Western European bodies

INTRODUCTION

The estimation of age-at-death of unidentified cadavers is a central aspect of the identification process. With increasing age, the incidence of glomerulosclerosis and the thickness of the carotid wall have been observed to also increase. This correlation has been demonstrated in various international histological studies. The aim of our study was to assess whether these correlations also apply to a Western European population.

METHODOLOGY

In this retrospective observational study, kidney and common carotid artery samples from 216 cases autopsied at the Institute of Legal Medicine at the Justus-Liebig University in Giessen, Germany, were examined. Only cases with available tissue samples from both body sides were included. Exclusion criteria were poor sample quality and an age younger than 21 years. After histological processing, the tissue samples were assessed and digitally evaluated. Regression and classification analyses were used to investigate the correlation between age-at-death and intima-media thickness and age-at-death and the incidence of renal glomerular sclerosis.

RESULTS

Of the 216 autopsy cases, 183 were included for evaluation. Analysis of the carotid artery segments showed a strong correlation (Pearson correlation coefficient r = 0.887) between the intima-media-complex thickness and chronological age. Classification of the glomerulosclerotic incidence showed a correlation of 37.7-43.1% with the predicted age group.

DISCUSSION

Both the intima-media thickness and the proportion of sclerotic glomeruli can be used to estimate age in Western European cadavers. On the basis of these results, both methods are suited to supplement other already established methods for age-at-death estimation in the identification of an unknown cadaver.

Evaluating the performance of five up-to-date DNA/RNA co-extraction methods for forensic application

The importance of RNA evidence is growing with new developments in RNA profiling methods and purposes. As forensic samples often can be of small quantity, extraction methods with high yields of both DNA and RNA are desirable. In order to identify the optimal DNA/RNA co-extraction workflow for forensic samples, we evaluated the performance of three frequently-used methods, two new approaches for DNA/RNA co-extraction and a manual phenol/chloroform RNA-only extraction method on blood and saliva samples. Based on a comprehensive analysis of the RNA and DNA quantities, as well as the STR genotyping and mRNA profiling results, we conclude that the two frequently-used co-extraction methods, combining commercially available DNA and RNA extraction kits, achieved the best performance. However, not any combination of commercially available DNA and RNA extraction kits works well and extensive optimization is necessary, as seen in the poor results of the two new approaches.

Cell survival and DNA damage repair are promoted in the human blood thanatotranscriptome shortly after death

RNA analysis of post-mortem tissues, or thanatotranscriptomics, has become a topic of interest in forensic science due to the essential information it can provide in forensic investigations. Several studies have previously investigated the effect of death on gene transcription, but it has never been conducted with samples of the same individual. For the first time, a longitudinal mRNA expression analysis study was performed with post-mortem human blood samples from individuals with a known time of death. The results reveal that, after death, two clearly differentiated groups of up- and down-regulated genes can be detected. Pathway analysis suggests active processes that promote cell survival and DNA damage repair, rather than passive degradation, are the source of early post-mortem changes of gene expression in blood. In addition, a generalized linear model with an elastic net restriction predicted post-mortem interval with a root mean square error of 4.75 h. In conclusion, we demonstrate that post-mortem gene expression data can be used as biomarkers to estimate the post-mortem interval though further validation using independent sample sets is required before use in forensic casework.

Epigenetic age prediction in semen - marker selection and model development

DNA methylation analysis is becoming increasingly useful in biomedical research and forensic practice. The discovery of differentially methylated sites (DMSs) that continuously change over an individual's lifetime has led to breakthroughs in molecular age estimation. Although semen samples are often used in forensic DNA analysis, previous epigenetic age prediction studies mainly focused on somatic cell types. Here, Infinium MethylationEPIC BeadChip arrays were applied to semen-derived DNA samples, which identified numerous novel DMSs moderately correlated with age. Validation of the ten most age-correlated novel DMSs and three previously known sites in an independent set of semen-derived DNA samples using targeted bisulfite massively parallel sequencing, confirmed age-correlation for nine new and three previously known markers. Prediction modelling revealed the best model for semen, based on 6 CpGs from newly identified genes SH2B2, EXOC3, IFITM2, and GALR2 as well as the previously known FOLH1B gene, which predict age with a mean absolute error of 5.1 years in an independent test set. Further increases in the accuracy of age prediction from semen DNA will require technological progress to allow sensitive, simultaneous analysis of a much larger number of age correlated DMSs from the compromised DNA typical of forensic semen stains.

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

Genome-wide identification of age-related CpG sites for age estimation from blood DNA of Han Chinese individuals

Age-related CpG (AR-CpG) sites are currently the most promising molecular markers for forensic age estimation. However, the AR-CpG sites of Han Chinese population remains to be systematically characterized. In this study, we performed genome-wide methylation analyses on 42 whole blood DNA from healthy Han Chinese volunteers (aged from 18 to 62 years) using the Illumina MethylationEPIC BeadChip microarray. As expected, both known and novel AR-CpG sites were identified. Considering the sex difference in aging rate, we then separately selected AR-CpG candidates and built pyrosequencing-based multiple linear regression models for age estimation of males and females. The model constructed from the male sample group (n = 167, aged from 1.50 to 85.71 years) explained 95.22% of variation in age using five AR-CpG sites (chr6:11044864 ELOVL2, chr1:207997068 C1orf132, cg19283806 CCDC102B, cg17740900, and chr10:73740306 CHST3) and yielded a mean absolute error (MAE) of 2.79 years. The model constructed from the female sample group (n = 141, aged from 3.33 to 80.38 years) explained 94.90% of variation in age with six AR-CpG sites (chr6:11044867 ELOVL2, chr1:207997060 C1orf132, chr2:106015757 FHL2, cg26947034, chr16: 67184108 B3GNT9, and chr20:44658203 SLC12A5) and yielded an MAE of 2.53 years. Besides, the estimated age was highly correlated with the actual age (R > 0.97). The robustness of these AR-CpG markers was demonstrated by 10-fold cross-validations. In conclusion, we updated the AR-CpG sites of Han Chinese population and provided two sets of AR-CpG sites for accurate age estimation.

Evidence for Biological Age Acceleration and Telomere Shortening in COVID-19 Survivors

The SARS-CoV-2 infection determines the COVID-19 syndrome characterized, in the worst cases, by severe respiratory distress, pulmonary and cardiac fibrosis, inflammatory cytokine release, and immunosuppression. This condition has led to the death of about 2.15% of the total infected world population so far. Among survivors, the presence of the so-called persistent post-COVID-19 syndrome (PPCS) is a common finding. In COVID-19 survivors, PPCS presents one or more symptoms: fatigue, dyspnea, memory loss, sleep disorders, and difficulty concentrating. In this study, a cohort of 117 COVID-19 survivors (post-COVID-19) and 144 non-infected volunteers (COVID-19-free) was analyzed using pyrosequencing of defined CpG islands previously identified as suitable for biological age determination. The results show a consistent biological age increase in the post-COVID-19 population, determining a DeltaAge acceleration of 10.45 ± 7.29 years (+5.25 years above the range of normality) compared with 3.68 ± 8.17 years for the COVID-19-free population (p < 0.0001). A significant telomere shortening parallels this finding in the post-COVID-19 cohort compared with COVID-19-free subjects (p < 0.0001). Additionally, ACE2 expression was decreased in post-COVID-19 patients, compared with the COVID-19-free population, while DPP-4 did not change. In light of these observations, we hypothesize that some epigenetic alterations are associated with the post-COVID-19 condition, particularly in younger patients (< 60 years).

Male-specific age estimation based on Y-chromosomal DNA methylation

Although DNA methylation variation of autosomal CpGs provides robust age predictive biomarkers, no male-specific age predictor exists based on Y-CpGs yet. Since sex chromosomes play an important role in aging, a Y-chromosome-based age predictor would allow studying male-specific aging effects and would also be useful in forensics. Here, we used blood-based DNA methylation microarray data of 1,057 males from six cohorts aged 15-87 and identified 75 Y-CpGs with an interquartile range of ≥0.1. Of these, 22 and six were significantly hyper- and hypomethylated with age (p(cor)<0.05, Bonferroni), respectively. Amongst several machine learning algorithms, a model based on support vector machines with radial kernel performed best in male-specific age prediction. We achieved a mean absolute deviation (MAD) between true and predicted age of 7.54 years (cor=0.81, validation) when using all 75 Y-CpGs, and a MAD of 8.46 years (cor=0.73, validation) based on the most predictive 19 Y-CpGs. The accuracies of both age predictors did not worsen with increased age, in contrast to autosomal CpG-based age predictors that are known to predict age with reduced accuracy in the elderly. Overall, we introduce the first-of-its-kind male-specific epigenetic age predictor for future applications in aging research and forensics.

Forensic transcriptome analysis using massively parallel sequencing

The application of transcriptome analyses in forensic genetics has experienced tremendous growth and development in the past decade. The earliest studies and main applications were body fluid and tissue identification, using targeted RNA transcripts and a reverse transcription endpoint PCR method. A number of markers have been identified for the forensically most relevant body fluids and tissues and the method has been successfully used in casework. The introduction of Massively Parallel Sequencing (MPS) opened up new perspectives and opportunities to advance the field. Contrary to genomic DNA where two copies of an autosomal DNA segment are present in a cell, abundant RNA species are expressed in high copy numbers. Even whole transcriptome sequencing (RNA-Seq) of forensically relevant body fluids and of postmortem material was shown to be possible. This review gives an overview on forensic transcriptome analyses and applications. The methods cover whole transcriptome as well as targeted MPS approaches. High resolution forensic transcriptome analyses using MPS are being applied to body fluid/ tissue identification, determination of the age of stains and the age of the donor, the estimation of the post-mortem interval and to post mortem death investigations.

Telomere length and signal joint T-cell receptor rearrangement excision circles as biomarkers for chronological age estimation

BACKGROUND

Chronological age estimation is a challenging marker in the field of forensic medicine. The current study aimed to investigate the accuracy of signal joint T-cell receptor rearrangement excision circles (sjTRECs) quantification and telomere length measurement as methods for estimating chronological age.

METHODS

Telomere length was estimated in the DNA derived from the buccal cells through estimating the telomeric restriction fragment (TRF) length using TeloTTAGGG Telomere Length Assay while the sjTRECs quantification was carried out on DNA isolated from the blood samples using qPCR.

RESULTS

The TRF length was shortened with increased age (r = -0.722, p < 0.001). The sjTRECs were also decreased with increased age (r = -0.831, p < 0.001). Stronger coefficient and lower standard error of the estimate was obtained when multiple regression analysis for age prediction based on the values of both methods was applied (r = -0.876, p < 0.001).

Age estimation using DNA methylation technique in forensics: a systematic review

Background

In addition to the DNA sequence, epigenetic markers have become substantial forensic tools during the last decade. Estimating the age of an individual from human biological remains may provide information for a forensic investigation. Age estimation in molecular strategies can be obtained by telomere length, mRNa mutation, or by sjTRECs but the accuracy is not sufficient in forensic practice because of high margin error.

Main body

One solution to this problem is to use DNA methylation methods. DNA methylation markers for tissue identification at age-associated CpG sites have been suggested as the most informative biomarkers for estimating the age of an unknown donor. This review aims to give an overview of DNA methylation profiling for estimating the age in cases of forensic relevance and the important aspects in determining the mean absolute deviation (MAD) or mean absolute error (MAE) of the estimated age. Online database searching was performed through PubMed, Scopus, and Google Scholar with keywords selected for forensic age estimation. Thirty-two studies were included in the review, with variable DNA samples but blood commonly as a source. Pyrosequencing and EpiTYPER were methods mostly used in DNA analysis. The MAD in the estimates from DNA methylation was about 3 to 5 years, which was better than other methods such as those based on telomere length or signal-joint T-cell receptor excision circles. The ELOVL2 gene was a commonly used DNA methylation marker in age estimation.

Conclusion

DNA methylation is a favorable candidate for estimating the age at the time of death in forensic profiling, with an uncertainty mean absolute deviation of about 3 to 5 years in the predicted age. The sample type, platform techniques used, and methods to construct age predictive models were important in determining the accuracy in mean absolute deviation or mean absolute error. The DNA methylation outcome suggests good potential to support conventional STR profiling in forensic cases.

Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter

Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual’s lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3–4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER^®, pyrosequencing, MiSeq, and SNaPshot^TM. The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER^®, pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshot^TM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

Epigenetic clock measuring age acceleration via DNA methylation levels in blood is associated with decreased oocyte yield

PURPOSE

To investigate how biologic age (phenotypic age at which your body functions) greater than chronologic age, (age acceleration (AgeAccel)), correlates with oocyte yield.

METHODS

Thirty-nine women undergoing ovarian stimulation, inclusive of all infertility diagnoses, were included in this pilot study. Methylome analysis of peripheral blood was utilized to determine biologic age. AgeAccel was defined as biologic age > 2 years older than chronologic age. A negative binomial model was used to obtain the crude association of AgeAccel with number of oocytes. A parsimonious adjusted model for the number of oocytes was obtained using backwards selection (p < 0.05).

RESULTS

Measures of age were negatively correlated with number of oocytes (chronological age Pearson ρ = - 0.45, biologic age Pearson ρ = - 0.46) and AMH was positively correlated with number of oocytes (Pearson ρ = 0.91). Patients with AgeAccel were noted to have lower AMH values (1.29 ng/mL vs. 2.29, respectively (p = 0.049)) and lower oocyte yield (5.50 oocytes vs. 14.50 oocytes, respectively (p = 0.0030)). A crude association of a 7-oocyte reduction in the age-accelerated group was found (- 6.9 oocytes (CI - 11.6, - 2.4)). In a model with AMH and antral follicle count, AgeAccel was associated with a statistically significant 3.3 reduction in the number of oocytes (- 3.1; 95% CI - 6.5, - 0.1; p = 0.036).

CONCLUSIONS

In this small pilot study, AgeAccel is associated with a lower AMH and lower oocyte yield providing preliminary evidence that biologic age, specifically AgeAccel, may serve as an epigenetic biomarker to improve the ability of predictive models to assess ovarian reserve.

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.

DNA methylation levels and telomere length in human teeth: usefulness for age estimation

In the last decade, increasing knowledge of epigenetics has led to the development of DNA methylation-based models to predict age, which have shown high predictive accuracy. However, despite the value of teeth as forensic samples, few studies have focused on this source of DNA. This study used bisulfite pyrosequencing to measure the methylation levels of specific CpG sites located in the ELOVL2, ASPA, and PDE4C genes, with the aim of selecting the most age-informative genes and determining their associations with age, in 65 tooth samples from individuals 15 to 85 years old. As a second aim, methylation data and measurements of relative telomere length in the same set of samples were used to develop preliminary age prediction models to evaluate the accuracy of both biomarkers together and separately in estimating age from teeth for forensic purposes. In our sample, several CpG sites from ELOVL2 and PDE4C genes, as well as telomere length, were significantly associated with chronological age. We developed age prediction quantile regression models based on DNA methylation levels, with and without telomere length as an additional variable, and adjusted for type of tooth and sex. Our results suggest that telomere length may have limited usefulness as a supplementary marker for DNA methylation-based age estimation in tooth samples, given that it contributed little improvement in the prediction errors of the models. In addition, even at older ages, DNA methylation appeared to be more informative in predicting age than telomere length when both biomarkers were evaluated separately.

CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer

Many biological indicators related to chronological age have been proposed. Recent studies found that epigenetic clock or DNA methylation age is highly correlated with chronological age. In particular, a significant difference between DNA methylation age (m-age) and chronological age was observed in cancers. However, the prediction and characterization of m-age in pan-cancer remains an explored area. In this study, 1,631 age-related methylation sites in normal tissues were discovered and analyzed. A comprehensive computational model named CancerClock was constructed to predict the m-age for normal samples based on methylation levels of the extracted methylation sites. LASSO linear regression model was used to screen and train the CancerClock model in normal tissues. The accuracy of CancerClock has proved to be 81%, and the correlation value between chronological age and m-age was 0.939 (P < 0.01). Next, CancerClock was used to evaluate the difference between m-age and chronological age for 33 cancer types from TCGA. There were significant differences between predicted m-age and chronological age in large number of cancer samples. These cancer samples were defined as “age-related cancer samples” and they have some differential methylation sites. The differences between predicted m-age and chronological age may contribute to cancer development. Some of these differential methylation sites were associated with cancer survival. CancerClock provided assistance in estimating the m-age in normal and cancer samples. The changes between m-age and chronological age may improve the diagnosis and prognosis of cancers.