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.

The accurate quantification of DNA in forensic samples is of utmost importance. These samples are often present in limited amounts; therefore, it is indicated to use the appropriate analysis route with the optimum DNA amount (when possible). Also, DNA quantification can inform about the degradation stage and therefore support the decision on which downstream genotyping method to use. Consequently, DNA quantification aids in getting the best possible results from a forensic sample, considering both its DNA quantity and quality limitations. Here, we introduce NuMY, a new quantitative real-time PCR (qPCR) method for the parallel quantification of human nuclear (n) and mitochondrial (mt) DNA, assessing the male portion in mixtures of both sexes and testing for possible PCR inhibition. NuMY is based on previous work and follows the MIQE guidelines whenever applicable. Although quantification of nuclear (n)DNA by simultaneously analyzing autosomal and male-specific targets is available in commercial qPCR kits, tools that include the quantification of mtDNA are sparse. The quantification of mtDNA has proven relevant for samples with low nDNA content when conventional DNA fingerprinting techniques cannot be followed. Furthermore, the development and use of new massively parallel sequencing assays that combine multiple marker types, i.e., autosomal, Y-chromosomal, and mtDNA, can be optimized when precisely knowing the amount of each DNA component present in the input sample. For high-quality DNA extracts, NuMY provided nDNA results comparable to those of another quantification technique and has also proven to be a reliable tool for challenging, forensically relevant samples such as mixtures, inhibited, and naturally degraded samples.

In recent years, the estimation of the age of a stain donor through DNA methylation has received much attention in the forensic genetics community. It can provide investigative leads for law enforcement when no DNA profile match could be obtained from a crime scene stain. It has been shown that different human populations can have systematically different methylation profiles. This raises the question whether the age of individuals from different biogeographic backgrounds is estimated with different accuracies using forensic age estimation tools. In our study, we investigated 30 individuals each from Central Europe, East Asia and West Africa. All individuals donated blood, saliva and buccal cell samples and each sample was analyzed with two forensic age estimation tools (VISAGE enhanced tool and Jung et al. SNaPshot tool). Our study showed that there were no statistically significant differences in age estimation accuracy between the three investigated populations regardless of the body fluid or age estimation tool used. The only exception was in the saliva samples, which showed statistically significant differences in age estimation errors with the VISAGE enhanced tool when comparing the East Asian and West African groups. Including knowledge about the biogeographic origin of a stain donor might therefore not be as important as previously assumed.

Methylation-based forensic donor age estimation is an emerging field in forensic genomics. It can be used not only to estimate the age of a living person, but also to determine age-at-death of an unknown corpse or skeleton. Therefore, age estimation tools suitable for bone samples have been developed in recent years. As commonly known, methylation profiles differ considerably between different sample types. However, it is unclear whether different types of bones display different methylation patterns and whether exposure to forensically relevant environmental conditions affects bone methylation. In this study, we used the VISAGE enhanced age estimation tool for bones to estimate the age of freshly collected bone samples from 30 individuals. Five bone types (rib, femur, clavicle, iliac crest and petrous bone) were analysed for each individual. While we found high overall age estimation errors with this tool (MAE = 9.79 years, mean error = -7.25 years), only the petrous bones displayed statistically significantly different age estimation errors compared to at least one of the other four bone types. As is commonly necessary when adapting a methylation-based assay in different laboratories, a linear transformation between predicted and chronological age was applied to calibrate the estimates to our cohort, which led to an improved overall MAE of 7.03 years. In addition, this study mimicked casework samples by exposing the femur samples of the cohort to burying, submersion in water or burning. From the mock casework samples, only 12 produced a result with an MAE of approximately 10-15 years using the transformation model. Finally, two actual casework samples were analysed. Although it was technically possible to generate an age estimate, the results obtained with the transformation model deviated considerably from the chronological ages (estimation error of 10 and 29 years, respectively). We concluded that it is generally possible to estimate age-at-death from bone material through DNA methylation analysis when the bone material is fresh. However, under forensically relevant conditions, it might be difficult to obtain reliable age estimation results.

Forensic DNA methylation-based donor age prediction is currently one of the most researched methods in the field of forensic genetics. Many tools have already been developed for this purpose, focusing on different body fluids, target markers and DNA methylation detection technologies. One such tool created by the VISAGE consortium allows to predict donor age from three different somatic cells, namely blood, buccal cells and bones. This VISAGE tool is based on massively parallel sequencing and requires bioinformatic expertise to analyse the data. Although the VISAGE consortium developed a software for automatic data analysis, this software does not automatically perform quality control steps the user can look at, no visual outputs (such as graphical plots) are generated and it is not conveniently usable if many samples are being analyzed. In this study, we therefore propose an alternative easy-to-use data analysis pipeline consisting of two shiny apps for the VISAGE somatic age prediction tool. The first Python-based shiny app allows for bam file generation and read count extraction from raw data and the second R-based shiny app performs quality controls, visualizations, and allows the user to access all intermediary analysis files. These apps facilitate the analysis of the VISAGE age prediction tool for all end users, requiring only minimal bioinformatics skills and the highest level of transparency how the results were generated.