ABSTRACT
Epigenome-wide association studies (EWAS) seek to quantify associations between traits/exposures and DNA methylation measured at thousands or millions of CpG sites across the genome. In recent years, the increase in availability of DNA methylation measures in population-based cohorts and case-control studies has resulted in a dramatic expansion of the number of EWAS being performed and published. To make this rich source of results more accessible, we have manually curated a database of CpG-trait associations (with p<1x10 -4) from published EWAS, each assaying over 100,000 CpGs in at least 100 individuals. From January 7, 2022, The EWAS Catalog contained 1,737,746 associations from 2,686 EWAS. This includes 1,345,398 associations from 342 peer-reviewed publications. In addition, it also contains summary statistics for 392,348 associations from 427 EWAS, performed on data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Gene Expression Omnibus (GEO). The database is accompanied by a web-based tool and R package, giving researchers the opportunity to query EWAS associations quickly and easily, and gain insight into the molecular underpinnings of disease as well as the impact of traits and exposures on the DNA methylome. The EWAS Catalog data extraction team continue to update the database monthly and we encourage any EWAS authors to upload their summary statistics to our website. Details of how to upload data can be found here: http://www.ewascatalog.org/upload. The EWAS Catalog is available at http://www.ewascatalog.org.
ABSTRACT
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.
