表观遗传年龄模型适用于下一代甲基化阵列

表观遗传时钟是用于基于特定CpG位点DNA甲基化的表观遗传年龄的数学模型。随着新的甲基化微阵列的发展并停止旧模型，现有的表观遗传钟可能会过时。在这里，我们探索了新的EPICV2 DNA甲基化阵列中引入的变化对现有表观遗传时钟的影响。我们使用10,835个样品的数据集测试了四个表观遗传时钟对EPICV2阵列探针的性能。我们开发了一个在450 K，EPICV1和EPICV2微阵列中兼容的新表观遗传预测模型，并在2095个样品上验证了它。我们使用重复采样的两个数据集估算了技术噪声和受试者内变化。我们使用了（i）经过不同疗法，（ii）乳腺癌患者和对照组以及（iii）一项基于运动的介入性研究的癌症幸存者的数据来测试我们模型检测表观遗传年龄加速的能力，以响应于理论上抗衡的干预措施而响应于四个Epiclock测试的结果25年，逐渐扭曲了ca ca ca的结果。我们的新模型产生了高度准确的年代年龄预测，与最先进的Epiclock相当。该模型报告了正常人群中表观遗传年龄加速度最低，以及来自同一受试者的技术重复和重复样本之间的最低变化。最后，我们的模型再现了以前的癌症患者表观遗传年龄加速和接受放射疗法治疗的幸存者的结果，并且基于运动的干预措施没有变化。存在的表观遗传时钟需要更新以使EPICV2完全兼容。我们的新模型将最先进的表观遗传钟的功能转化为EPICV2平台，并与较旧的微阵列互相兼容。表观遗传年龄预测变化的表征提供了有用的指标，可以将表观遗传年龄变化的相关性背景化。对受辐射，癌症和基于运动的干预措施影响的受试者数据的分析表明，尽管是年龄段的良好预测指标，既不是诸如乳腺癌，危险环境因素（辐射），锻炼（有益的干预措施）的病理状态，也导致了这些第一代模型确定的“表观遗传年龄”值的重大变化。

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.