Epismoker: A Robust Classifier to Determine Smoking Status from DNA Methylation Data

Abstract

<b>Aim:</b> Smoking strongly influences DNA methylation, with current and never smokers exhibiting different methylation profiles. <b>Methods:</b> To advance the practical applicability of the smoking-associated methylation signals, we used machine learning methodology to train a classifier for smoking status prediction. <b>Results:</b> We show the prediction performance of our classifier on three independent whole-blood datasets demonstrating its robustness and global applicability. Furthermore, we examine the reasons for biologically meaningful misclassifications through comprehensive phenotypic evaluation. <b>Conclusion:</b> The major contribution of our classifier is its global applicability without a need for users to determine a threshold value for each dataset to predict the smoking status. We provide an R package, EpiSmokEr (Epigenetic Smoking status Estimator), facilitating the use of our classifier to predict smoking status in future studies.