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

SIMPLE SUMMARY: DNA methylation age estimation is one of the hottest topics in forensic field nowadays. Age estimation can be improved under a multidisciplinary approach, the role of a forensic anthropologist and forensic epigeneticist being crucial in the establishment of new basis for age estimation. The development of epigenetic models for bones and tooth samples is crucial in this way. Moreover, developing models for age estimation using several samples can be a useful tool in forensics. In this study, we built two multi-tissue models for age estimation, combining blood, bones and tooth samples and using two different methodologies. Through the Sanger sequencing methodology, we built a model with seven age-correlated markers and a mean absolute deviation between predicted and chronological ages of 6.06 years. Using the SNaPshot assay, a model with three markers has been developed revealing a mean absolute deviation between predicted and chronological ages of 6.49 years. Our results showed the usefulness of DNA methylation age estimation in forensic contexts and brought new insights into the development of multi-tissue models applied to blood, bones and teeth. In the future, we expected that these procedures can be applied to the Medico-Legal facilities to use DNA methylation in routine practice for age estimation. ABSTRACT: 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.