Improved age determination of blood and teeth samples using a selected set of DNA methylation markers

Host genetic diversity and genetic variations of SARS-CoV-2 in COVID-19 pathogenesis and the effectiveness of vaccination

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the outbreak of coronavirus disease 2019 (COVID-19), has shown a vast range of clinical manifestations from asymptomatic to life-threatening symptoms. To figure out the cause of this heterogeneity, studies demonstrated the trace of genetic diversities whether in the hosts or the virus itself. With this regard, this review provides a comprehensive overview of how host genetic such as those related to the entry of the virus, the immune-related genes, gender-related genes, disease-related genes, and also host epigenetic could influence the severity of COVID-19. Besides, the mutations in the genome of SARS-CoV-2 __leading to emerging of new variants__ per se affect the affinity of the virus to the host cells and enhance the immune escape capacity. The current review discusses these variants and also the latest data about vaccination effectiveness facing the most important variants.

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.

A cautionary note on altered pace of aging in the COVID-19 era

Coronavirus disease 2019 (COVID-19) is highly age-dependent due to hi-jacking the molecular control of the immune cells by the severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2) leading to aberrant DNA methylation (DNAm) pattern of blood in comparison to normal individuals. These epigenetic modifications have been linked to perturbations to the epigenetic clock, development of long COVID-19 syndrome, and all-cause mortality risk. I reviewed the effects of COVID-19 on different molecular age markers such as the DNAm, telomere length (TL), and signal joint T-cell receptor excision circle (sjTREC). Integrating the accumulated clinical research data, COVID-19 and novel medical management may alter the pace of aging in adult individuals (<60 years). As such, COVID-19 might be a confounder in epigenetic age estimation similar to life style diversities, pathogens and pathologies which may influence the interpretation of DNAm data. Similarly, the SARS-CoV-2 affects T-lymphocyte function with possible influence on sjTREC levels. In contrast, TL measurements performed years before the SARS-CoV-2 pandemic proved that short TL predisposes to severe COVID- 19 independently from chronological age. However, the persistence of COVID-19 epigenetic scars and the durability of the immune response after vaccination and their effect on the ongoing pace of aging are still unknown. In the light of these data, the heterogeneous nature of the samples in these studies mandates a systematic evaluation of the currrent methods. SARS-CoV-2 may modify the reliability of the age estimation models in real casework because blood is the most common biological sample encountered in forensic contexts.

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman's rank correlation. Differences in DNAm between the groups "healthy children" and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

Application of Aspartic Acid Racemization for Age Estimation in a Spanish Sample

Simple Summary

For the correct identification of human skeletal remains, age is one of the key parameters. However, in adult individuals, this estimation is more difficult as it is not based on growing markers but on degeneration of the skeleton and the teeth. Thus, it can be very variable and less precise than age estimation in children and adolescents. The application of biochemical techniques, with their roots in aging research, could help to improve this estimation. This article presents the application of one of these approaches, aspartic acid racemization, to test its accuracy in a Spanish sample. This is based on the conversion of L-aspartic acid, the regular form of the amino acid in our proteins, into D-aspartic acid, its mirror image. The proportions of D-aspartic acid/L-aspartic acid increase with aging, enabling the determination of age in a more precise way than by applying forensic anthropology methodologies. This paper demonstrates that it was possible to apply this technique in a Spanish sample, obtaining accuracies of ±5 years of actual age. Additional studies should be developed to improve these estimates and to combine this technique with forensic anthropology methods.

Abstract

Correct age-at-death estimation in adult individuals is one of the challenges of forensic investigation. Forensic anthropology macroscopic techniques are non-invasive methods for this purpose. However, several methods need to be applied to accurately estimate age, and the difference between chronological and predictive age may still be around ±10 years. New research trends are focused on the inherent process of aging, which produces changes in tissues and organs at different biochemical levels. One of the oldest and most studied approaches in this field is aspartic acid racemization. The accuracy of this technique in age estimation has been widely demonstrated. However, only a few studies have assessed its accuracy in different populations. The aim of this research was to assess the accuracy of aspartic acid racemization in a Spanish sample and its applicability to forensic cases. Dentin from fifteen third molars from two Spanish populations (ages 19–70 years old) was isolated and D and L forms of aspartic acid were detected through GC/MS, according to a previous published protocol. D/L ratios were calculated and after the application of a regression analysis, a formula for age estimation was developed. The results were similar to previous studies, obtaining an R = 0.91 between racemization ratios and age and a mean absolute error (MAE) between chronological and predictive age of 5 years. These results were ratified by leave-one-out cross-validation, as well as the application of the formula to five teeth of a known age. Despite these promising results, this technique is not exempt from drawbacks; thus, further studies are required to apply this methodology to forensic cases and to combine it with forensic anthropology findings.

Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads

Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.

Age Prediction by DNA Methylation in Neural Networks

Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e., Horvath's and Hannum's formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome ( p=0.024 and , respectively). We discover that for a large number of candidate features, such as genome-wide DNA methylation data, a key factor in improving prediction accuracy is to appropriately weight features that are highly correlated with the outcome of interest.

Evidence for differences in DNA methylation between Germans and Japanese

As a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included "robust CpGs" prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for "robust" CpG markers-which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

Evaluation of Epigenetic Age Based on DNA Methylation Analysis of Several CpG Sites in Ukrainian Population

Epigenetic clocks are the models, which use CpG methylation levels for the age prediction of an organism. Although there were several epigenetic clocks developed there is a demand for development and evaluation of the relatively accurate and sensitive epigenetic clocks that can be used for routine research purposes. In this study, we evaluated two epigenetic clock models based on the 4 CpG sites and 2 CpG sites in the human genome using the pyrosequencing method for their methylation level estimation. The study sample included 153 people from the Ukrainian population with the age from 0 to 101. Both models showed a high correlation with the chronological age in our study sample (R² = 0.85 for the 2 CpG model and R² = 0.92 for the 4 CpG model). We also estimated the accuracy metrics of the age prediction in our study sample. For the age group from 18 to 80 MAD was 5.1 years for the 2 CpG model and 4.1 years for the 4 CpG model. In this regard, we can conclude, that the models evaluated in the study have good age predictive accuracy, and can be used for the epigenetic age evaluation due to the relative simplicity and time-effectiveness.

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).

A Blood-Bone-Tooth Model for Age Prediction in Forensic Contexts

Simple Summary

DNA methylation age estimation is one of the hottest topics in forensic field nowadays. Age estimation can be improved under a multidisciplinary approach, the role of a forensic anthropologist and forensic epigeneticist being crucial in the establishment of new basis for age estimation. The development of epigenetic models for bones and tooth samples is crucial in this way. Moreover, developing models for age estimation using several samples can be a useful tool in forensics. In this study, we built two multi-tissue models for age estimation, combining blood, bones and tooth samples and using two different methodologies. Through the Sanger sequencing methodology, we built a model with seven age-correlated markers and a mean absolute deviation between predicted and chronological ages of 6.06 years. Using the SNaPshot assay, a model with three markers has been developed revealing a mean absolute deviation between predicted and chronological ages of 6.49 years. Our results showed the usefulness of DNA methylation age estimation in forensic contexts and brought new insights into the development of multi-tissue models applied to blood, bones and teeth. In the future, we expected that these procedures can be applied to the Medico-Legal facilities to use DNA methylation in routine practice for age estimation.

Abstract

The development of age prediction models (APMs) focusing on DNA methylation (DNAm) levels has revolutionized the forensic age estimation field. Meanwhile, the predictive ability of multi-tissue models with similar high accuracy needs to be explored. This study aimed to build multi-tissue APMs combining blood, bones and tooth samples, herein named blood–bone–tooth-APM (BBT-APM), using two different methodologies. A total of 185 and 168 bisulfite-converted DNA samples previously addressed by Sanger sequencing and SNaPshot methodologies, respectively, were considered for this study. The relationship between DNAm and age was assessed using simple and multiple linear regression models. Through the Sanger sequencing methodology, we built a BBT-APM with seven CpGs in genes ELOVL2, EDARADD, PDE4C, FHL2 and C1orf132, allowing us to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 6.06 years, explaining 87.8% of the variation in age. Using the SNaPshot assay, we developed a BBT-APM with three CpGs at ELOVL2, KLF14 and C1orf132 genes with a MAD of 6.49 years, explaining 84.7% of the variation in age. Our results showed the usefulness of DNAm age in forensic contexts and brought new insights into the development of multi-tissue APMs applied to blood, bone and teeth.

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.

Evolution of dental age estimation methods in adults over the years from occlusal wear to more sophisticated recent techniques

Background

Age estimation has been an integral part of forensic science, and age estimation by dental means is by far the most commonly employed method. Dental age estimation in children is more accurate and straightforward as most methods use the chronological stages of odontogenesis that are highly systematic, reducing the chances of dispersed results. In contrast, estimation of age in adults becomes tricky and less accurate with varied approaches since tooth formation is already complete.

Main body

The methods of adult dental age estimation have come a long way from a calculated guess based on crude visual observation of teeth to radiological methods and to more recent sophisticated methods. Technological advances have opened up molecular and genetic methods by utilizing DNA methylation and telomere length to improve the accuracy of age estimation by reducing error chances.

Conclusions

Although dental age estimation methods in children and adolescents have been extensively reviewed, various adult age estimation methods are not reviewed as a whole. The aim of this review is to appraise the evolution of dental age estimation methods in adults over the years from mere visualization of dental attrition to employing more sophisticated means such as radioactive carbon dating and genetics. This comprehensive review also attempts to add an account of the accuracy and suitability of various adult dental age estimation methods.

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.

Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease

Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines-most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique-as an alternative to current age-estimation methods-for two feline species that share a relatively long genetic distance with each other: domestic cat (Felis catus; 79 blood samples) and an endangered Panthera, the snow leopard (Panthera uncia; 11 blood samples). We measured the methylation rates of two gene regions using methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.

Developments in forensic DNA analysis

The analysis of DNA from biological evidence recovered in the course of criminal investigations can provide very powerful evidence when a recovered profile matches one found on a DNA database or generated from a suspect. However, when no profile match is found, when the amount of DNA in a sample is too low, or the DNA too degraded to be analysed, traditional STR profiling may be of limited value. The rapidly expanding field of forensic genetics has introduced various novel methodologies that enable the analysis of challenging forensic samples, and that can generate intelligence about the donor of a biological sample. This article reviews some of the most important recent advances in the field, including the application of massively parallel sequencing to the analysis of STRs and other marker types, advancements in DNA mixture interpretation, particularly the use of probabilistic genotyping methods, the profiling of different RNA types for the identification of body fluids, the interrogation of SNP markers for predicting forensically relevant phenotypes, epigenetics and the analysis of DNA methylation to determine tissue type and estimate age, and the emerging field of forensic genetic genealogy. A key challenge will be for researchers to consider carefully how these innovations can be implemented into forensic practice to ensure their potential benefits are maximised.

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.

DNA methylation of decedent blood samples to estimate the chronological age of human remains

Chronological age estimation may offer valuable investigative leads in human identification cases. Bisulfite pyrosequencing analysis of single CpG sites on five genes (KLF14, ELOVL2, C1orf132, TRIM59, and FHL2) was performed on 264 postmortem blood samples from individuals aged 3 months to 93 years. The goals were to develop age prediction models based on the correlation between the methylation profile and chronological age and to assess the accuracy of the prediction. Linear regression between methylation levels and age at each CpG site revealed that the five markers show a statistically significant correlation with age. The methylation data from a training set of 160 postmortem blood samples were used to develop an age prediction model with a correlation coefficient of 0.65, explaining 73.1% of age variation, with a mean absolute deviation from the chronological age of 7.60 years. The accuracy of the model was evaluated with a test set of 72 samples producing a mean absolute deviation of 7.42 years. The training and test sets were also categorized by specific age groups to assess accuracy and deviation from chronological age. The data for both sets revealed a lower prediction potential as an individual increases in age, particularly for the age categories above 50 years.

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).

Age-related DNA methylation analysis for forensic age estimation using post-mortem blood samples from Japanese individuals

As one of external visible characteristics (EVCs) in forensic phenotyping, age estimation is essential to providing additional information about a sample donor. With the development of epigenetics, age-related DNA methylation may be used as a reliable predictor of age estimation. With the aim of building a feasible age estimation model for Japanese individuals, 53 CpG sites distributed between 11 candidate genes were selected from previous studies. The DNA methylation level of each target CpG site was identified and measured on a massive parallel platform (synthesis by sequencing, Illumina, California, United States) from 60 forensic blood samples during the initial training phase. Multiple linear regression and quantile regression analyses were later performed to build linear and quantile age estimation models, respectively. Four CpG sites on four genes- ASPA, ELOVL2, ITGA2B, and PDE4C -, were found to be highly correlated with chronological age in DNA samples from Japanese individuals (|R| > 0.75). Subsequently, an independent validation dataset (n = 30) was used to verify and evaluate the performance of the two models. Comparison of mean absolute deviation (MAD) with other indicators showed that both models provide accurate age predictions (MAD: linear = 6.493 years; quantile = 6.243 years). The quantile model, however, can provide the changeable prediction intervals that grow wider with increasing age, and this tendency is consistent with the natural aging process in humans. Hence, the quantile model is recommended in this study.

Analysis of age-dependent DNA methylation changes in plucked hair samples using massive parallel sequencing

The analysis of age-dependent DNA methylation changes is a valuable tool in epigenetic research and forensic genetics. With some exceptions, most studies in the past concentrated on the analysis of blood, buccal, and saliva samples. Another important sample type in forensic investigations is hair, where age-dependent DNA methylation has not been investigated so far. In this pilot study a deeper look was taken at the possibilities and challenges of DNA methylation analysis in hair. The DNA methylation of selected age-dependent 5’-C-phosphate-G‑3’ (CpG) sites were characterized for their potential use as a biomarker for age prediction using plucked hair samples and massive parallel sequencing. Plucked hair roots of 49 individuals were included in the study. The DNA methylation of 31 hairs was successfully analyzed. The DNA methylation pattern of 10 loci, including ELOVL2, F5, KLF14, and TRIM59, was determined by amplicon-based massive parallel sequencing. Age-dependent changes were found for several markers. The results demonstrate the possible use of already established age-dependent markers but at the same time they have tissue/cell type-specific characteristics. Special challenges such as low amounts of DNA and degraded DNA as well as the possible heterogeneous cellular composition of plucked hair samples, have to be considered.

Forensische DNA-Methylierungsanalyse

Die quantitative Analyse der relativen DNA-Methylierung gilt als eine der vielversprechendsten Methoden der molekularen Altersschätzung. Viele Studien der letzten Jahre identifizierten geeignete Positionen im Genom, deren DNA-Methylierung sich altersabhängig verändert. Für den Einsatz dieser Methode in der Routine- bzw. Fallarbeit ist es von großer Bedeutung, angewandte Analysetechniken zu validieren. Als ein Teilaspekt dieser Validierung sollte die Vergleichbarkeit der Analyseergebnisse zur DNA-Methylierung mithilfe der Mini- und Pyrosequenzierung zwischen verschiedenen Laboren evaluiert werden. Die Arbeitsgruppe „Molekulare Altersschätzung“ der Deutschen Gesellschaft für Rechtsmedizin (DGRM) führte hierzu den ersten, technischen Ringversuch durch, der 4 Positionen in den Genen PDE4C, EDARADD, SST und KLF14 umfasste. Diese Marker waren in vorangegangenen Studien als altersabhängige Biomarker charakterisiert worden. Am Ringversuch nahmen 12 Labore teil, wobei jedes die Wahl zwischen der Minisequenzierung und/oder der Pyrosequenzierung für die quantitative Methylierungsanalyse hatte. Jedem teilnehmenden Labor wurden Blut- und Speichelproben von 3 Personen unterschiedlichen Alters übersandt. Die Wahl der Reagenzien für die Probenbearbeitung wurde den Teilnehmern freigestellt.

Die Ergebnisse der Minisequenzierung zeigten systematische Abweichungen zwischen den Laboren, die am ehesten auf die Verwendung unterschiedlicher Reagenzien und Analyseplattformen zurückzuführen sein können. Die Resultate der Pyrosequenzierung hingegen wiesen nicht auf systematische Abweichungen zwischen den Laboren hin, hier zeigte sich jedoch die Tendenz einer markerabhängigen Abweichung. Darüber hinaus konnten Unterschiede hinsichtlich technischer Probleme zwischen Laboren mit mehr Erfahrung in der jeweiligen Sequenzierungsmethode und Laboren mit weniger Erfahrung festgestellt werden. Sowohl die Beobachtung von systematischen als auch die von markerabhängigen Abweichungen lässt den Schluss zu, dass eine Übertragung von Analysemethoden zwischen Laboren grundsätzlich möglich ist, eine Anpassung des jeweiligen Modells zur Altersschätzung jedoch notwendig sein kann.

Forensische DNA-Methylierungsanalyse

Mit der Entdeckung altersabhängiger epigenetischer Veränderungen, der DNA-Methylierung (DNAm), hat sich eine neue Möglichkeit aufgezeigt, das Alter eines Individuums zu schätzen. Die Methode wurde intensiv erforscht und ihre Anwendung in der forensischen Fallarbeit durch die Aktualisierung des § 81e der Strafprozessordnung (StPO) in Deutschland reguliert. Zur Untersuchung des DNAm-Grades müssen neue Techniken etabliert und validiert werden. Dies macht die Prüfung der Vergleichbarkeit von Messergebnissen aus verschiedenen forensischen Laboren erforderlich.

Hierzu führte die Arbeitsgruppe „Molekulare Altersschätzung“ der Deutschen Gesellschaft für Rechtsmedizin (DGRM) im Winter 2019/2020 den 2. Ringversuch (RV) zur quantitativen DNAm-Analyse mithilfe der Mini- und der Pyrosequenzierung durch. Dieser basierte auf den Erfahrungen des 1. RV 2018/2019, dessen Ergebnisse in dieser Ausgabe ebenfalls vorgestellt werden. Die aktuelle Studie umfasst Analyseergebnisse aus 12 Laboren (ingesamt 14 teilnehmende Labore), von denen einige beide Methoden angewandt haben. Zusätzlich führten 4 Labore eine Altersschätzung an den RV-Proben mit eigenen Markerkombinationen und Modellen durch. Da diese auf unterschiedlichen Referenzdaten und Markerkombinationen beruhen, erfolgte kein qualitativer Vergleich der Modelle, sondern das grundsätzliche Potenzial der Methodik wurde verdeutlicht. Ziele des RV waren die Evaluierung der Vergleichbarkeit der DNAm-Messungen und die Bewertung möglicher Einflussfaktoren, wie Extraktionsmethode und verwendetes Gerät.

Die Ergebnisse zeigen, dass sich die gemessenen DNAm-Werte der untersuchten Marker sowohl zwischen Mini- und Pyrosequenzierung als auch innerhalb der jeweiligen Methode zwischen den Laboren unterscheiden können, sodass mit Schwankungen gerechnet werden muss.

Molecular and morphological findings in a sample of oral surgery patients: What can we learn for multivariate concepts for age estimation?

It has already been proposed that a combined use of different molecular and morphological markers of aging in multivariate models may result in a greater accuracy of age estimation. However, such an approach can be complex and expensive, and not every combination may be useful. The significance and usefulness of combined analyses of D-aspartic acid in dentine, pentosidine in dentine, DNA methylation in buccal swabs at five genomic regions (PDE4C, RPA2, ELOVL2, DDO, and EDARADD), and third molar mineralization were tested by investigating a sample of 90 oral surgery patients. Machine learning models for age estimation were trained and evaluated, and the contribution of each parameter to multivariate models was tested by assessment of the predictor importance. For models based on D-aspartic acid, pentosidine, and the combination of both, mean absolute errors (MAEs) of 2.93, 3.41, and 2.68 years were calculated, respectively. The additional inclusion of the five DNAm markers did not improve the results. The sole DNAm-based model revealed a MAE of 4.14 years. In individuals under 28 years of age, the combination of the DNAm markers with the third molar mineralization stages reduced the MAE from 3.85 to 2.81 years. Our findings confirm that the combination of parameters in multivariate models may be very useful for age estimation. However, the inclusion of many parameters does not necessarily lead to better results. It is a task for future research to identify the best selection of parameters for the different requirements in forensic practice.

Development of two age estimation models for buccal swab samples based on 3 CpG sites analyzed with pyrosequencing and minisequencing

The analysis of DNA methylation levels of specific CpG sites is one of the most promising molecular techniques to estimate an individual's age. Numerous studies were published recently presenting age estimation models based on DNA methylation patterns from blood samples, with only a few using saliva or buccal swabs. The aim of this study was to identify age-dependent methylation of 88 CpG sites in eight different marker regions (PDE4C, ELOVL2, ITGA2B, ASPA, EDARADD, SST, KLF14 and SLC12A5) in buccal swab samples. A total of 141 buccal swabs from individuals with age ranging from 21 to 69 years were split into a training set (n = 95) and a validation set (n = 46). Samples of the training set were analyzed by pyrosequencing and markers with best age correlation were identified. Stepwise linear regression analysis was performed resulting in an age estimation model including three of the examined CpG sites and showing a mean absolute deviation of estimated from chronological age of 5.11 years. To allow easy implementation into forensic laboratories without the need for pyrosequencing equipment, a multiplex minisequencing reaction was developed, including the same CpG sites previously identified by pyrosequencing. An adjusted age estimation model was evaluated with a mean absolute deviation of estimated from chronological age of 5.16 years. The independent validation set of 46 buccal swab samples was used to test model performances. Mean absolute deviation of estimated from chronological age was 5.33 years and 6.44 years for the pyrosequencing model and the minisequencing model, respectively. Comparison of the two methods showed a high concordance of results, both, qualitatively and quantitatively. In conclusion, buccal swabs offer a suitable alternative to blood samples for molecular age estimation with the additional advantage of being collected non-invasively. Furthermore we showed that minisequencing offers a cost-effective and easy-to-integrate alternative to pyrosequencing for the analysis of methylation status of individual CpG sites.

Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging

The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4,409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root-mean-square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (p < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males.

Identifying Methylation Patterns in Dental Pulp Aging: Application to Age-at-Death Estimation in Forensic Anthropology

Age-at-death estimation constitutes one of the key parameters for identification of human remains in forensic investigations. However, for applications in forensic anthropology, many current methods are not sufficiently accurate for adult individuals, leading to chronological age estimates erring by ±10 years. Based on recent trends in aging studies, DNA methylation has great potential as a solution to this problem. However, there are only a few studies that have been published utilizing DNA methylation to determine age from human remains. The aim of the present study was to expand the range of this work by analyzing DNA methylation in dental pulp from adult individuals. Healthy erupted third molars were extracted from individuals aged 22–70. DNA from pulp was isolated and bisulfite converted. Pyrosequencing was the chosen technique to assess DNA methylation. As noted in previous studies, we found that ELOVL2 and FHL2 CpGs played a role in age estimation. In addition, three new markers were evaluated—NPTX2, KLF14, and SCGN. A set of CpGs from these five loci was used in four different multivariate regression models, providing a Mean Absolute Error (MAE) between predicted and chronological age of 1.5–2.13 years. The findings from this research can improve age estimation, increasing the accuracy of identification in forensic anthropology.

Development of the VISAGE enhanced tool and statistical models for epigenetic age estimation in blood, buccal cells and bones

DNA methylation is known as a biomarker for age with applications in forensics. Here we describe the VISAGE (VISible Attributes through GEnomics) Consortium's enhanced tool for epigenetic age estimation in somatic tissues. The tool is based on eight DNA methylation markers (44 CpGs), bisulfite multiplex PCR followed by sequencing on the MiSeq FGx platform, and three statistical prediction models for blood, buccal cells and bones. The model for blood is based on six CpGs from ELOVL2, MIR29B2CHG, KLF14, FHL2, TRIM59 and PDE4C, and predicts age with a mean absolute error (MAE) of 3.2 years, while the model for buccal cells includes five CpGs from PDE4C, MIR29B2CHG, ELOVL2, KLF14 and EDARADD and predicts age with MAE of 3.7 years, and the model for bones has six CpGs from ELOVL2, KLF14, PDE4C and ASPA and predicts age with MAE of 3.4 years. The VISAGE enhanced tool for age estimation in somatic tissues enables reliable collection of DNA methylation data from small amounts of DNA using a sensitive multiplex MPS assay that provides accurate estimation of age in blood, buccal swabs, and bones using the statistical model tailored to each tissue.

Heterogeneous Hunter-Gatherer and Steppe-Related Ancestries in Late Neolithic and Bell Beaker Genomes from Present-Day France

The transition from the Late Neolithic to the Bronze Age has witnessed important population and societal changes in western Europe.¹ These include massive genomic contributions of pastoralist herders originating from the Pontic-Caspian steppes^2,3 into local populations, resulting from complex interactions between collapsing hunter-gatherers and expanding farmers of Anatolian ancestry.^4-8 This transition is documented through extensive ancient genomic data from present-day Britain,^9,10 Ireland,^11,12 Iberia,¹³ Mediterranean islands,^14,15 and Germany.⁸ It remains, however, largely overlooked in France, where most focus has been on the Middle Neolithic (n = 63),^8,9,16 with the exception of one Late Neolithic genome sequenced at 0.05× coverage.¹⁶ This leaves the key transitional period covering ∼3,400-2,700 cal. years (calibrated years) BCE genetically unsampled and thus the exact time frame of hunter-gatherer persistence and arrival of steppe migrations unknown. To remediate this, we sequenced 24 ancient human genomes from France spanning ∼3,400-1,600 cal. years BCE. This reveals Late Neolithic populations that are genetically diverse and include individuals with dark skin, hair, and eyes. We detect heterogeneous hunter-gatherer ancestries within Late Neolithic communities, reaching up to ∼63.3% in some individuals, and variable genetic contributions of steppe herders in Bell Beaker populations. We provide an estimate as late as ∼3,800 years BCE for the admixture between Neolithic and Mesolithic populations and as early as ∼2,650 years BCE for the arrival of steppe-related ancestry. The genomic heterogeneity characterized underlines the complex history of human interactions even at the local scale.

Age related changes of rib cortical bone matrix and the application to forensic age-at-death estimation

Forensic anthropology includes, amongst other applications, the positive identification of unknown human skeletal remains. The first step in this process is an assessment of the biological profile, that is: sex, age, stature and ancestry. In forensic contexts, age estimation is one of the main challenges in the process of identification. Recently established admissibility criteria are driving researchers towards standardisation of methodological procedures. Despite these changes, experience still plays a central role in anthropological examinations. In order to avoid this issue, age estimation procedures (i) must be presented to the scientific community and published in peer reviewed journals, (ii) accurately explained in terms of procedure and (iii) present clear information about the accuracy of the estimation and possible error rates. In order to fulfil all these requirements, a number of methods based on physiological processes which result in biochemical changes in various tissue structures at the molecular level, such as modifications in DNA-methylation and telomere shortening, racemization of proteins and stable isotopes analysis, have been developed. The current work proposes a new systematic approach in age estimation based on tracing physicochemical and mechanical degeneration of the rib cortical bone matrix. This study used autopsy material from 113 rib specimens. A set of 33 parameters were measured by standard bio-mechanical (nanoindentation and microindentation), physical (TGA/DSC, XRD and FTIR) and histomorphometry (porosity-ImageJ) methods. Stepwise regressions were used to create equations that would produce the best 'estimates of age at death' vs real age of the cadavers. Five equations were produced; in the best of cases an equation counting 7 parameters had an R² = 0.863 and mean absolute error of 4.64 years. The present method meets all the admissibility criteria previously described. Furthermore, the method is experience-independent and as such can be performed without previous expert knowledge of forensic anthropology and human anatomy.

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

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

Epigenetic Associations With Estimated Glomerular Filtration Rate Among Men With Human Immunodeficiency Virus Infection

BACKGROUND

People living with human immunodeficiency virus (HIV) infection have higher risk for chronic kidney disease (CKD), defined by a reduced estimated glomerular filtration rate (eGFR). Previous studies have implicated epigenetic changes related to CKD; however, the mechanism of HIV-related CKD has not been thoroughly investigated.

METHODS

We conducted an epigenome-wide association study of eGFR among 567 HIV-positive and 117 HIV-negative male participants in the Veterans Aging Cohort Study to identify epigenetic signatures of kidney function.

RESULTS

By surveying more than 400 000 cytosine guanine dinucleotide (CpG) sites measured from peripheral blood mononuclear cells, we identified 15 sites that were significantly associated with eGFR (false discovery rate Q value < 0.05) among HIV-positive participants. The most significant CpG sites, located at MAD1L1, TSNARE1/BAI1, and LTV1, were all negatively associated with eGFR (cg06329547, P = 5.25 × 10-9; cg23281907, P = 1.37 × 10-8; cg18368637, P = 5.17 × 10-8). We also replicated previously reported eGFR-associated CpG sites including cg17944885 (P = 2.5 × 10-5) located between ZNF788 and ZNF20 on chromosome 19 in the pooled population.

CONCLUSIONS

In this study we uncovered novel epigenetic associations with kidney function among people living with HIV and suggest potential epigenetic mechanisms linked with HIV-related CKD risk.

A newly developed age estimation method based on CpG methylation of teeth-derived DNA using real-time methylation-specific PCR

Age estimation of unidentified bodies is important in forensic medicine and crime scenes. There is accumulating evidence that DNA methylation in the human genome isolated from body fluids changes with age. Most of the data have been obtained by pyrosequencing. In the forensic field, a simple, quick, and economical method is required to evaluate the age of various types of samples. In this study, an age estimation method based on methylation levels of DNA extracted from teeth using real-time methylation-specific PCR (MSP) was developed. The CpG island in the upstream region of ELOVL2, which is known as a validated biomarker in blood samples, was selected as a target site. The CpG methylation levels highly correlated with age (r = 0.843, n = 29). Age-related increase in DNA methylation levels was not affected by sex differences. In addition, the simple regression model based on methylation status of the CpG island exhibited moderate accuracy with a mean absolute deviation between chronological age and predicted age of 8.94 years. The results imply that real-time MSP can be a new tool to perform age prediction of unidentified bodies in forensic scenes.

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.

A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing

Chronological age estimation is an important piece of human identification used in forensic practice. Epigenetic modifications, especially DNA methylation, have been proposed to predict age. The methylation of the ELOVL2 gene is one of the age-related markers that could be tested in fresh or postmortem blood sample. We study the use of DNA methylation markers on the ELOVL2 gene and develop a prediction model to estimate the age from a postmortem blood sample using pyrosequencing. From 100 anonymous blood samples, a correlation study of DNA methylation and age was investigated. The regression analysis revealed 2 CpG sites for model prediction with an adjusted R² value of 0.7 (p < 0.01). The model explained 74% of the variation in postmortem blood samples (n = 36) with a prediction error (RMSE) of 10.2 years and a mean absolute deviation (MAD) of 7.1 years, whereas the model (excluding a younger age group) had improved with a RMSE of 5.6 years and a MAD of 4.2 years. The performance parameters were analyzed in several simulated models and indicated that these markers are advantageous for age estimation in forensic scenarios. Finally, a robustness and reproducibility of the pyrosequencing technique would enable this approach to be the part of an age prediction in forensic investigation.

The use of DNA methylation clock in aging research

One of the key characteristics of aging is a progressive loss of physiological integrity, which weakens bodily functions and increases the risk of death. A robust biomarker is important for the assessment of biological age, the rate of aging, and a person's health status. DNA methylation clocks, novel biomarkers of aging, are composed of a group of cytosine-phosphate-guanine dinucleotides, the DNA methylation status of which can be used to accurately measure subjective age. These clocks are considered accurate biomarkers of chronological age for humans and other vertebrates. Numerous studies have demonstrated these clocks to quantify the rate of biological aging and the effects of longevity and anti-aging interventions. In this review, we describe the purpose and use of DNA methylation clocks in aging research.

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.

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

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

Postmortem age estimation via DNA methylation analysis in buccal swabs from corpses in different stages of decomposition-a "proof of principle" study

Age estimation based on the analysis of DNA methylation patterns has become a focus of forensic research within the past few years. However, there is little data available regarding postmortem DNA methylation analysis yet, and literature mainly encompasses analysis of blood from corpses without any signs of decomposition. It is not entirely clear yet which other types of specimen are suitable for postmortem epigenetic age estimation, and if advanced decomposition may affect methylation patterns of CpG sites. In living persons, buccal swabs are an easily accessible source of DNA for epigenetic age estimation. In this work, the applicability of this approach (buccal swabs as source of DNA) under different postmortem conditions was tested. Methylation levels of PDE4C were investigated in buccal swab samples collected from 73 corpses (0–90 years old; mean: 51.2) in different stages of decomposition. Moreover, buccal swab samples from 142 living individuals (0–89 years old; mean 41.2) were analysed. As expected, methylation levels exhibited a high correlation with age in living individuals (training set: r² = 0.87, validation set: r² = 0.85). This was also the case in postmortem samples (r² = 0.90), independent of the state of decomposition. Only in advanced putrified cases with extremely low DNA amounts, epigenetic age estimation was not possible. In conclusion, buccal swabs are a suitable and easy to collect source for DNA methylation analysis as long as sufficient amounts of DNA are present.

Development and optimization of the VISAGE basic prototype tool for forensic age estimation

The VISAGE (VISible Attributes through GEnomics) consortium aims to develop, optimize and validate prototype tools to broaden the use of DNA intelligence methods in forensic routine laboratories. This includes age estimation based on the quantification of DNA methylation at specific CpG sites. Here, we present the VISAGE basic prototype tool for age estimation targeting 32 CpGs from five genes ELOVL2, MIR29B2CHG (herein, MIR29B2C), FHL2, TRIM59 and KLF14. The assay interrogates these well described age markers by multiplex PCR for bisulfite converted DNA and massively parallel sequencing on a MiSeq FGx instrument. We describe protocol optimizations including tests on five bisulfite conversion kits and an evaluation of the assay's reproducibility and sensitivity with artificially methylated DNA standards. We observed robust quantification of methylation levels with a mean standard deviation of 1.4 % across ratios. Sensitivity tests showed no increase of variability down to 20 ng DNA input into bisulfite conversion with a median difference below 1.6 % between technical replicates.