A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits.

Dennis Khodasevich, Nina Holland, Lars van der Laan, Andres Cardenas

Department of Epidemiology and Population Health, Stanford University School of Medicine, Palo Alto, California, United States of America.Department of Epidemiology and Population Health, Stanford University School of Medicine, Palo Alto, California, United States of America. · pubmed

Paper

Abstract

DNA methylation (DNAm) provides a window to characterize the impacts of environmental exposures and the biological aging process. Epigenetic clocks are often trained on DNAm using penalized regression of CpG sites, but recent evidence suggests potential benefits of training epigenetic predictors on principal components.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper claims that a SuperLearner-based pipeline can improve the development of DNA methylation-derived predictors of phenotypic traits. This research is relevant as it explores the role of DNA methylation in understanding biological aging and environmental impacts, which are central to longevity studies.

Author of this paper? Send a correction.