Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Andrew E Teschendorff, Steve Horvath

CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. [email protected] Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. [email protected]. · pubmed

Paper

Abstract

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

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 discusses the development and challenges of epigenetic clocks, which are tools that can potentially provide insights into biological aging and interventions aimed at longevity. While it addresses important computational and statistical issues that could enhance the understanding of epigenetic aging, the focus is primarily on methodological challenges rather than direct advancements in the biological mechanisms of aging or lifespan extension. Thus, it represents solid research but with limited immediate impact on the field.

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