DNA methylation-based predictors of health: applications and statistical considerations.
Nat Rev Genet
; 23(6): 369-383, 2022 06.
Article
de En
| MEDLINE
| ID: mdl-35304597
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Méthylation de l'ADN
/
Tumeurs
Type d'étude:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limites:
Humans
Langue:
En
Journal:
Nat Rev Genet
Sujet du journal:
GENETICA
Année:
2022
Type de document:
Article
Pays d'affiliation:
Royaume-Uni