DNA methylation analysis for smoking status prediction in the Chinese population based on the methylation-sensitive single-nucleotide primer extension method

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.