Journal Article

DNA methylation-based predictors of health: applications and statistical considerations

Davey Smith George, Relton, Caroline L, Yousefi, Paul D, Suderman, Matthew, Langdon, Ryan, Whitehurst, Oliver

2022

Overview

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.DNA methylation-based predictors of health aim to predict outcomes such as exposure, phenotype or disease on the basis of genome-wide levels of DNA methylation. The authors review applications of existing DNA methylation-based predictors and highlight key statistical best practices to ensure their reliable performance.

• Publisher: Nature Publishing Group
• Subject: DNA methylation / Genomes / Health risk assessment / Neurological diseases / Phenotypes / Risk factors / Statistics