孟德尔随机化
表观遗传学
因果关系(物理学)
DNA甲基化
表观基因组
因果推理
医学
进化生物学
生物
计算生物学
遗传学
物理
基因型
病理
基因表达
基因
量子力学
遗传变异
作者
Kejun Ying,Hanna Liu,Andrei E. Tarkhov,Marie C Sadler,Ake T. Lu,Mahdi Moqri,Steve Horvath,Zoltán Kutalik,Xia Shen,Vadim N. Gladyshev
出处
期刊:Nature Aging
日期:2024-01-19
被引量:4
标识
DOI:10.1038/s43587-023-00557-0
摘要
Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.
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