Age prediction from DNA has been a topic of interest in recent years due to the promising results obtained when using epigenetic markers. Since DNA methylation gradually changes across the individual’s lifetime, prediction models have been developed accordingly for age estimation. The tissue-dependence for this biomarker usually necessitates the development of tissue-specific age prediction models, in this way, multiple models for age inference have been constructed for the most commonly encountered forensic tissues (blood, oral mucosa, semen). The analysis of skeletal remains has also been attempted and prediction models for bone have now been reported. Recently, the VISAGE Enhanced Tool was developed for the simultaneous DNA methylation analysis of 8 age-correlated loci using targeted high-throughput sequencing. It has been shown that this method is compatible with epigenetic age estimation models for blood, buccal cells, and bone. Since when dealing with decomposed cadavers or postmortem samples, cartilage samples are also an important biological source, an age prediction model for cartilage has been generated in the present study based on methylation data collected using the VISAGE Enhanced Tool. In this way, we have developed a forensic cartilage age prediction model using a training set composed of 109 samples (19–74 age range) based on DNA methylation levels from three CpGs in FHL2, TRIM59 and KLF14, using multivariate quantile regression which provides a mean absolute error (MAE) of ± 4.41 years. An independent testing set composed of 72 samples (19–75 age range) was also analyzed and provided an MAE of ± 4.26 years. In addition, we demonstrate that the 8 VISAGE markers, comprising EDARADD, TRIM59, ELOVL2, MIR29B2CHG, PDE4C, ASPA, FHL2 and KLF14, can be used as tissue prediction markers which provide reliable blood, buccal cells, bone, and cartilage differentiation using a developed multinomial logistic regression model. A training set composed of 392 samples (n = 87 blood, n = 86 buccal cells, n = 110 bone and n = 109 cartilage) was used for building the model (correct classifications: 98.72%, sensitivity: 0.988, specificity: 0.996) and validation was performed using a testing set composed of 192 samples (n = 38 blood, n = 36 buccal cells, n = 46 bone and n = 72 cartilage) showing similar predictive success to the training set (correct classifications: 97.4%, sensitivity: 0.968, specificity: 0.991). By developing both a new cartilage age model and a tissue differentiation model, our study significantly expands the use of the VISAGE Enhanced Tool while increasing the amount of DNA methylation-based information obtained from a single sample and a single forensic laboratory analysis. Both models have been placed in the open-access Snipper forensic classification website.

近年来，由于使用表观遗传标记获得了有希望的结果，基于 DNA 的年龄预测一直是人们感兴趣的话题。由于 DNA 甲基化在人的一生中逐渐发生变化，因此相应地开发了预测模型来估计年龄。这种生物标志物的组织依赖性通常需要开发组织特异性年龄预测模型，这样，针对最常见的法医组织（血液、口腔粘膜、精液）构建了多种年龄推断模型。还尝试了对骨骼遗骸的分析，并且现已报道了骨骼的预测模型。最近，开发了 VISAGE 增强工具，用于使用靶向高通量测序对 8 个年龄相关基因座进行同步 DNA 甲基化分析。事实证明，该方法与血液、口腔细胞和骨骼的表观遗传年龄估计模型兼容。由于在处理腐烂尸体或死后样本时，软骨样本也是重要的生物来源，因此本研究基于使用 VISAGE 增强工具收集的甲基化数据生成了软骨年龄预测模型。通过这种方式，我们开发了一个法医软骨年龄预测模型，使用由 109 个样本（19-74 岁范围）组成的训练集，基于FHL2、TRIM59和KLF14中三个 CpG 的 DNA 甲基化水平，使用多元分位数回归提供了平均绝对误差 (MAE) 为 ± 4.41 年。还对由 72 个样本（19-75 岁范围）组成的独立测试集进行了分析，得出的 MAE 为 ± 4.26 年。此外，我们还证明了 8 个 VISAGE 标记，包括EDARADD、TRIM59、ELOVL2、MIR29B2CHG、PDE4C、ASPA、FHL2和KLF14，可用作组织预测标记，使用开发的多项逻辑回归模型提供可靠的血液、口腔细胞、骨和软骨分化。使用由 392 个样本（n = 87 个血液、n = 86 个颊细胞、n = 110 个骨骼和 n = 109 个软骨）组成的训练集来构建模型（正确分类：98.72%，敏感性：0.988，特异性：0.996）使用由 192 个样本（n = 38 个血液、n = 36 个颊细胞、n = 46 个骨骼和 n = 72 个软骨）组成的测试集进行验证，显示与训练集相似的预测成功率（正确分类：97.4%，灵敏度：0.968，特异性：0.991）。通过开发新的软骨年龄模型和组织分化模型，我们的研究显着扩大了 VISAGE 增强工具的使用范围，同时增加了从单个样本和单个法医实验室分析中获得的基于 DNA 甲基化的信息量。这两种模型均已放置在开放式 Snipper 法医分类网站中。