A common epigenetic clock from childhood to old age.

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability..

Evaluation of a custom QIAseq targeted DNA panel with 164 ancestry informative markers sequenced with the Illumina MiSeq.

Introduction of new methods requires meticulous evaluation before they can be applied to forensic genetic case work. Here, a custom QIAseq Targeted DNA panel with 164 ancestry informative markers was assessed using the MiSeq sequencing platform. Concordance, sensitivity, and the capability for analysis of mixtures were tested. The assay gave reproducible and nearly concordant results with an input of 10 and 2 ng DNA. Lower DNA input led to an increase in both locus and allele drop-outs, and a higher variation in heterozygote balance. Locus or allele drop-outs in the samples with less than 2 ng DNA input were not necessarily associated with the overall performance of a locus. Thus, the QIAseq assay will be difficult to implement in a forensic genetic setting where the sample material is often scarce and of poor quality. With equal or near equal mixture ratios, the mixture DNA profiles were easily identified by an increased number of imbalanced heterozygotes. For more skewed mixture ratios, the mixture DNA profiles were identified by an increased noise level. Lastly, individuals from Great Britain and the Middle East were investigated. The Middle Eastern individuals showed a greater affinity with South European populations compared to North European populations.

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 SNaPshotTM. 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, SNaPshotTM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

Development and validation of the EUROFORGEN NAME (North African and Middle Eastern) ancestry panel.

Inference of biogeographic origin is an important factor in clinical, population and forensic genetics. The information provided by AIMs (Ancestry Informative Markers) can allow the differentiation of major continental population groups, and several AIM panels have been developed for this purpose. However, from these major population groups, Eurasia covers a wide area between two continents that is difficult to differentiate genetically. These populations display a gradual genetic cline from West Europe to South Asia in terms of allele frequency distribution. Although differences have been reported between Europe and South Asia, Middle East populations continue to be a target of further investigations due to the lack of genetic variability, therefore hampering their genetic differentiation from neighboring populations. In the present study, a custom-built ancestry panel was developed to analyze North African and Middle Eastern populations, designated the 'NAME' panel. The NAME panel contains 111 SNPs that have patterns of allele frequency differentiation that can distinguish individuals originating in North Africa and the Middle East when combined with a previous set of 126 Global AIM-SNPs.