Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

Abstract

DNA methylation is a stable epigenetic alteration that plays a key role in cellular differentiation and gene regulation, and that has been proposed to mediate environmental effects on disease risk. Epigenome-wide association studies have identified and replicated associations between methylation sites and several disease conditions, which could serve as biomarkers in predictive medicine and forensics. Nevertheless, heterogeneity in cellular proportions between the compared groups could complicate interpretation. Reference-based cell-type deconvolution methods have proven useful in correcting epigenomic studies for cellular heterogeneity, but they rely on reference libraries of sorted cells and only predict a limited number of cell populations. Here we leverage >850,000 methylation sites included in the Methyla-tionEPIC array and use elastic net regularized and stability selected regression models to predict the circulating levels of 70 blood cell subsets, measured by standardized flow cytometry in 962 healthy donors of western European descent. We show that our predictions, based on a hundred of methylation sites or lower, are less error-prone than other existing methods, and extend the number of cell types that can be accurately predicted. Application of the same methods to age, smoking consumption and several serological responses to pathogen antigens also provide accurate estimations. Together, our study substantially improves predictions of blood cell composition based on methylation profiles, which will be critical in the emerging field of medical epigenomics. Methylation | Prediction | Elastic net | Cellular composition | EWAS | AgeCorrespondence: jacob.bergstedt @control.lth.se, epatin @pasteur.fr

The immune factors driving DNA methylation variation in human blood

“…This reinforces the view that EWAS must be interpreted with great caution, particularly when the studied diseases or conditions are known to affect unmeasured immune cell fractions. Encouragingly, our findings suggest that, when blood cell composition is not measured directly, high-resolution cell mixture deconvolution methods 29,95 provide a more complete correction for cellular heterogeneity and are therefore expected to improve the interpretation of future epigenomic studies. Second, because age, sex, CMV infection, smoking, and chronic low-grade inflammation can influence disease risk 45,[96][97][98][99] , our results emphasize the critical need to consider such factors in EWAS, as these factors can confound associations.…”