Abstract：
In some criminal cases, the identity of suspect is unknown and there is no matching DNA profile in the DNA database. Forensic DNA Phenotyping can provide useful investigative information for these cases. Most forensic studies focus on visible characteristics rather than behavioral characteristics. However, smoking is prevalent in the Chinese population, and DNA methylation is the most promising biomarker for smoking. We collected 204 whole blood samples from the Chinese population and measured methylation levels of 9 smoking-related CpG loci using the methylation-sensitive single-nucleotide primer extension method (Ms-SnuPE). But the single-base extension primers of loci cg12803068 and cg21566642 contained other CpG sites, which may introduce bias, and only the other 7 CpG loci were included in subsequent statistical analysis. The methylation level of locus cg05575921 near the aromatic hydrocarbon receptor repressor (AHRR) gene was much lower in the current smoker group than in the never smoker group. To evaluate the ability of each of 7 CpG loci to predict smoking status, the logistic regression (LR) models were established separately, and locus cg05575921 had the best ability to predict smoking status compared with the other 6 loci. Then, combined (including loci cg19572487, cg05575921, cg23480021, cg23576855, cg21161138, cg01940273, and cg09935388) and stepwise (including loci cg05575921 and cg01940273) multinomial logistic regression (MLR) models were also established. Both combined and stepwise MLR models had good efficiencies in predicting smoking status, and outperformed the above 7 LR models. However, the accuracy, specificity and area under the curve (AUC) of stepwise MLR model in the testing dataset were slightly higher than those of combined MLR model, and the stepwise MLR model required less loci information. Therefore, the stepwise MLR model based on 2 significant CpG loci was more recommended model for predicting smoking status in the Chinese population, and the formula was as follow: P = 1/(1 +e-(10.621-10.005*cg05575921-8.770*cg01940273)). Mainly 2 CpG loci (cg05575921 and cg01940273) played a major role in the prediction of smoking status, and the other 5 CpG loci contributed less. Moreover, for evaluating the ability of each of 7 CpG loci to predict cigarette consumption, the polynomial regression formulas were established separately. As the adjusted R2 was between 0.00 and 0.20, the methylation levels of these 7 loci were not closely associated with the cigarette consumption. Our methylation assay is simple, economical, and available in conventional forensic laboratories, and may be useful in assessing the smoking status of unknown suspects.
摘要：
在一些刑事案件中,嫌疑人身份不明,DNA数据库中没有匹配的DNA图谱.法医DNA表型分析可以为这些病例提供有用的调查信息。大多数法医研究侧重于可见特征而不是行为特征。然而,吸烟在中国人口中很普遍，DNA甲基化是最有希望的吸烟生物标志物。我们从中国人群中收集了204份全血样本，并使用甲基化敏感的单核苷酸引物延伸法（Ms-SnuPE）测量了9个吸烟相关CpG基因座的甲基化水平。但基因座cg12803068和cg21566642的单碱基延伸引物含有其他CpG位点,这可能会引入偏见，其他7个CpG位点仅纳入随后的统计分析。当前吸烟者组的芳烃受体阻遏物（AHRR）基因附近的基因座cg05575921的甲基化水平远低于从不吸烟者组。为了评估7个CpG基因座中的每一个预测吸烟状况的能力，分别建立逻辑回归(LR)模型，与其他6个基因座相比，cg05575921基因座对吸烟状况的预测能力最好。然后，联合（包括基因座cg19572487，cg05575921，cg23480021，cg23576855，cg21161138，cg01940273和cg09935388）和逐步（包括基因座cg05575921和cg01940273）多项逻辑回归（MLR）模型。组合和逐步MLR模型在预测吸烟状况方面均具有良好的效率，并优于上述7个LR模型。然而,准确性,测试数据集中逐步MLR模型的特异性和曲线下面积(AUC)略高于组合MLR模型,逐步MLR模型需要较少的基因座信息。因此,基于2个显著CpG位点的逐步MLR模型是更推荐的预测中国人群吸烟状况的模型，公式如下:P=1/(1+e-(10.621-10.005*cg05575921-8.770*cg01940273))。主要有2个CpG位点(cg05575921和cg01940273)在预测吸烟状况中起主要作用,其他5个CpG位点的贡献较少。此外，为了评估7个CpG基因座中每个基因座预测卷烟消费量的能力，分别建立了多项式回归公式。由于调整后的R2在0.00和0.20之间,这7个基因座的甲基化水平与香烟消耗不密切相关。我们的甲基化检测很简单,经济,并且可以在传统的法医实验室中使用，并可能有助于评估不明嫌犯的吸烟状况。
