Department of Forensic Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
Department of Forensic Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
Forensic Sci Int Genet. 2019 Jan;38:39-47. doi: 10.1016/j.fsigen.2018.10.005. Epub 2018 Oct 9.
Age prediction has been in the spotlight recently because it can provide an important information about the contributors of biological evidence left at crime scenes. Specifically, many researchers have actively suggested age-prediction models using DNA methylation at several CpG sites and tested the candidates using platforms such as the HumanMethylation 450 array and pyrosequencing. With DNA methylation data obtained from each platform, age prediction models were constructed using diverse statistical methods typically with multivariate linear regression. However, because each developed model is based on single-platform data, the prediction accuracy is reduced when applying DNA methylation data obtained from other platforms. In this study, bisulfite sequencing data for 95 saliva samples were generated using massively parallel sequencing (MPS) and compared with methylation SNaPshot data from the same 95 individuals. The predicted age obtained by applying MPS data to an age-prediction model built for methylation SNaPshot data differed greatly from the chronological age due to platform differences. Therefore, novel variables were introduced to indicate the platform type, and construct platform-independent age predictive models using a neural network and multivariate linear regression. The final neural network model had a mean absolute deviation (MAD) of 3.19 years between the predicted and chronological age, and the mean absolute percentage error (MAPE) was 8.89% in the test set. Similarly, the linear regression model showed 3.69 years of MAD and 10.44% of MAPE in the same test set. The platform-independent age-prediction model was made extensible to an increasing number of platforms by introducing platform variables, and the idea of platform variables can be applied to age prediction models for other body fluids.
年龄预测最近备受关注，因为它可以提供关于犯罪现场留下的生物证据来源的重要信息。具体来说，许多研究人员积极提出了使用多个 CpG 位点的 DNA 甲基化来预测年龄的模型，并使用 HumanMethylation 450 阵列和焦磷酸测序等平台对候选者进行了测试。使用从每个平台获得的 DNA 甲基化数据，使用多种统计方法构建了年龄预测模型，通常使用多元线性回归。然而，由于每个开发的模型都是基于单平台数据，因此当应用来自其他平台的 DNA 甲基化数据时，预测精度会降低。在这项研究中，使用大规模平行测序（MPS）生成了 95 个唾液样本的亚硫酸盐测序数据，并与来自同一 95 个个体的甲基化 SNaPshot 数据进行了比较。由于平台差异，将 MPS 数据应用于为甲基化 SNaPshot 数据构建的年龄预测模型中获得的预测年龄与实际年龄有很大差异。因此，引入了新的变量来表示平台类型，并使用神经网络和多元线性回归构建了平台独立的年龄预测模型。最终的神经网络模型在测试集中，预测年龄与实际年龄之间的平均绝对偏差（MAD）为 3.19 岁，平均绝对百分比误差（MAPE）为 8.89%。同样，线性回归模型在同一测试集中的 MAD 为 3.69 岁，MAPE 为 10.44%。通过引入平台变量，使平台独立的年龄预测模型可以扩展到越来越多的平台，并且平台变量的思想可以应用于其他体液的年龄预测模型。
