Helena Correia Dias

Age estimation is a paramount issue in criminal, anthropological, and forensic research. Because of this, several areas of research have focused on the establishment of new approaches for age prediction, including bimolecular and anthropological methods. In recent years, DNA methylation (DNAm) has arisen as one of the hottest topics in the field. Many studies have developed age-prediction models (APMs) based on evaluation of DNAm levels of many genes in different tissue types and using different methodological approaches. However, several challenges and confounder factors should be considered before using methylation levels for age estimation in forensic contexts. To provide in-depth knowledge about DNAm age estimation (DNAm age) and to understand why it is not yet a current tool in forensic laboratories, this review encompasses the literature for the most relevant scientific works published from 2015 to 2021 to address the challenges and future directions in the field. More than 60 papers were considered focusing essentially on studies that developed models for age prediction in several sample types.

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.