Epigenetic drift score captures directional methylation variability and links aging to transcriptional, metabolic, and genetic alterations
- Xiu Fan1,2,10,
- Qili Qian3,4,10,
- Wenran Li3,4,
- Tianzi Liu3,4,
- Changqing Zeng1,2,5,
- Peilin Jia1,2,
- Huandong Lin6,
- Xin Gao6,
- Li Jin4,7,
- Mingfeng Xia6,
- Sijia Wang3,8 and
- Fan Liu1,9
- 1China National Center for Bioinformation, Beijing 100101, China;
- 2Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China;
- 3CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
- 4Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai 200433, China;
- 5Institute of Biomedical Research, Henan Academy of Sciences, Zhengzhou 450046, China;
- 6Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Disease, Human Phenome Institute, Fudan University, Shanghai 200032, China;
- 7Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fulocal University, Shanghai 200433, China;
- 8Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China;
- 9Department of Forensic Sciences, College of Criminal Justice, Naif Arab University for Security Sciences, Riyadh 11452, Kingdom Saudi Arabia
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↵10 These authors contributed equally to this work.
Abstract
Epigenetic drift refers to the gradual and stochastic accumulation of epigenetic changes, such as DNA methylation variability, with advancing age. Although increasingly recognized for its potential role in aging biology, its extent, biological significance, and population specificity remain insufficiently characterized. Here, we present the first comprehensive epigenome-wide drift study (EWDS) in a large Chinese cohort (n = 3538), with replication in two independent Chinese (total n = 1467) and two European cohorts (total n = 956), to investigate the scale and relevance of epigenetic drift across populations. Through simulation, we identify White's test as the most powerful method among four alternatives for detecting age-associated methylation variability. Our EWDS reveals that 10.8% (50,385 CpGs) of sites on the 850 K EPIC array exhibit epigenome-wide significant drift, with 99% showing increased interindividual variability (positive drift) and 1% showing decreased variability (negative drift). Integration with single-cell RNA-seq data demonstrates that positive drift-CpGs are associated with increased transcriptional variability and upregulation in specific cell types, whereas negative drift-CpGs exhibit the opposite effect. We develop epigenetic drift scores (EDSs) to quantify individual drift burden; these scores are strongly age-associated and correlate with lipidomic profiles and clinical aging indicators. Longitudinal data confirm within-individual accumulation of drift over time. Finally, a GWAS of EDS identifies genetic determinants of drift magnitude, including heritable loci (e.g., ASTN2, SOCS5). Collectively, these findings establish epigenetic drift as a pervasive, directional, and biologically meaningful feature of human aging.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.280155.124.
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Freely available online through the Genome Research Open Access option.
- Received October 25, 2024.
- Accepted July 23, 2025.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
