Epigenetic clocks, DNA methylation-based predictive models of chronological age, are often utilized to study aging associated biology. Despite their widespread use, these methods do not account for other factors that also contribute to the variability of DNA methylation data. For example, many CpG sites show strong sex-specific or cell-type-specific patterns that likely impact the predictions of epigenetic age. To overcome these limitations, we developed a multidimensional extension of the Epigenetic Pacemaker, the Multi-state Epigenetic Pacemaker (MSEPM). We show that the MSEPM is capable of accurately modeling multiple methylation-associated factors simultaneously, while also providing site-specific models that describe the per site relationship between methylation and these factors. We utilized the MSEPM with a large aggregate cohort of blood methylation data to construct models of the effects of age-, sex-, and cell-type heterogeneity on DNA methylation. We found that these models capture a large faction of the variability at thousands of DNA methylation sites. Moreover, this approach allows us to identify sites that are primarily affected by aging and no other factors. An analysis of these sites reveals that those that lose methylation over time are enriched for CTCF transcription factor chip peaks, while those that gain methylation over time are associated with bivalent promoters of genes that are not expressed in blood. These observations suggest mechanisms that underlie age-associated methylation changes and suggest that age-associated increases in methylation may not have strong functional consequences on cell states. In conclusion, the MSEPM is capable of accurately modeling multiple methylation-associated factors, and the models produced can illuminate site-specific combinations of factors that affect methylation dynamics.

表观遗传时钟是基于 DNA 甲基化的实际年龄预测模型，通常用于研究与衰老相关的生物学。尽管它们被广泛使用，但这些方法并未考虑同样导致 DNA 甲基化数据可变性的其他因素。例如，许多 CpG 位点显示出强烈的性别特异性或细胞类型特异性模式，这可能会影响表观遗传年龄的预测。为了克服这些限制，我们开发了表观遗传起搏器的多维扩展，即多态表观遗传起搏器 （MSEPM）。我们表明 MSEPM 能够同时准确模拟多个甲基化相关因子，同时还提供描述甲基化与这些因子之间每个位点关系的位点特异性模型。我们利用 MSEPM 和大量血液甲基化数据的聚合队列来构建年龄、性别和细胞类型异质性对 DNA 甲基化影响的模型。我们发现这些模型捕获了数千个 DNA 甲基化位点的一大类变异。此外，这种方法使我们能够识别主要受衰老影响而不受其他因素影响的部位。对这些位点的分析表明，那些随时间失去甲基化的位点富集了 CTCF 转录因子芯片峰，而那些随着时间的推移获得甲基化的位点与血液中不表达的基因的二价启动子相关。这些观察结果表明了与年龄相关的甲基化变化的基础机制，并表明与年龄相关的甲基化增加可能不会对细胞状态产生强烈的功能影响。 总之，MSEPM 能够准确模拟多个甲基化相关因子，并且生成的模型可以阐明影响甲基化动力学的因子的位点特异性组合。