Inferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications.... Show moreInferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 +/- 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUC(crossvalidation) 0.925 +/- 0.021, AUC(externalvalidation)0.914), former (0.766 +/- 0.023, 0.699) and never smoking (0.830 +/- 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 +/- 0.068, 0.796; 15 pack-years 0.767 +/- 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 +/- 0.024, 0.760; 10 years 0.766 +/- 0.033, 0.764; 15 years 0.767 +/- 0.020, 0.754). Model application to children revealed highly accurate inference of the true non- smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications. Show less
