通过快速条件期望最大化算法检测到一个表观遗传起搏器。
An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm.
机构信息
Department of Evolutionary Biology, University of Haifa, Haifa, 3498838, Israel.
Deptartment of Molecular, Cell & Developmental Biology, University of California, Los Angeles, CA 90095, USA.
出版信息
Epigenomics. 2018 Jun;10(6):695-706. doi: 10.2217/epi-2017-0130.
AIM
DNA methylation has proven to be a remarkably accurate biomarker for human age, allowing the prediction of chronological age to within a couple of years. Recently, we proposed that the Universal PaceMaker (UPM), a flexible paradigm for modeling evolution, could be applied to epigenetic aging. Nevertheless, application to real data was restricted to small datasets for technical limitations.
MATERIALS & METHODS: We partition the set of variables into to two subsets and optimize the likelihood function on each set separately. This yields an extremely efficient Conditional Expectation Maximization algorithm, alternating between the two sets while increasing the overall likelihood.
RESULTS
Using the technique, we could reanalyze datasets of larger magnitude and show significant advantage to the UPM approach.
CONCLUSION
The UPM more faithfully models epigenetic aging than the time linear approach while methylated sites accelerate and decelerate jointly.
目的
DNA 甲基化已被证明是一种非常准确的人类年龄生物标志物，能够将预测的年龄与实际年龄的差距缩小到几年内。最近，我们提出通用起搏器（UPM）这一灵活的进化建模范例也可以应用于表观遗传衰老。然而，由于技术限制，该方法在实际数据中的应用仅限于小数据集。
材料与方法
我们将变量集划分为两个子集，并分别对每个子集优化似然函数。这产生了一种极其高效的条件期望最大化算法，在两个子集之间交替，同时增加整体似然度。
结果
使用该技术，我们可以重新分析更大规模的数据集，并显示出 UPM 方法的显著优势。
结论
与时间线性方法相比，UPM 更忠实地模拟了表观遗传衰老，而甲基化位点则共同加速和减速。
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