Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

DNA甲基化 CpG站点 线性回归 甲基化 相关性 医学 人口 回归分析 中国人口 人口学 生物 内科学 肿瘤科 统计 遗传学 数学 基因型 DNA 基因 环境卫生 社会学 基因表达 几何学
作者
Xueli Han,Chao Xiao,Shaohua Yi,Ya Li,Maomin Chen,Daixin Huang
出处
期刊:International Journal Of Legal Medicine [Springer Science+Business Media]
卷期号:136 (6): 1655-1665 被引量:22
标识
DOI:10.1007/s00414-022-02865-3
摘要

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2–82 years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors’ blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models’ performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5 years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43 days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.
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