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

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

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.