A semen-specific deoxyribonucleic acid methylation model for epigenetic age estimation and its robustness under environmental challenges

Abstract

In forensic investigations, semen samples are a common form of biological evidence, especially in cases involving sexual assault. Therefore, accurately estimating the age of an individual is crucial in criminal cases. This study presents a novel age estimation model based on semen-specific CpG methylation patterns. A multiplex panel was developed, consisting of 12 CpG sites (PARP14, C5orf25, cg23488376, MXRA5, PFKFB3, DLL1, NOX4, cg12837463, TTC7B, KCNA7, NKX2-1, and SYNE4), which exhibit strong correlations with age. Additionally, this study investigates the resilience of these methylation markers under simulated environmental challenges. We collected ejaculate samples from a diverse cohort of 115 male individuals, aged 20–71 years, who underwent deoxyribonucleic acid extraction and bisulfite conversion. Methylation levels of the selected CpG sites were assessed using a SNaPshot assay, which revealed significant correlations with chronological age. We developed and validated two robust age estimation models through stepwise and enter regression analyses, achieving reliable accuracy with mean absolute errors ranging from 3.81 to 4.1 years. Additionally, the study also investigated the robustness of semen stains under diverse environmental conditions, including fabric type, washing, hematin exposure, and UV-C light. The selected methylation markers demonstrated remarkable resilience despite the challenges posed by washing procedures and environmental exposure, confirming their potential for age estimation in forensic genetics. This research presents successful age estimation models, emphasizing the strong correlations between methylation levels and chronological age. The proposed methodology's accuracy is affirmed through model validation on an independent test set, while also highlighting the resilience of semen stains on fabrics under varying storage and washing conditions.