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

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.