Locus-specific DNA methylation prediction in cord blood and placenta

DNA methylation is known to be responsive to prenatal exposures, which may be a part of the mechanism linking early developmental exposures to future chronic diseases. Many studies use blood to measure DNA methylation, yet we know that DNA methylation is tissue specific. Placenta is central to fetal growth and development, but it is rarely feasible to collect this tissue in large epidemiological studies; on the other hand, cord blood samples are more accessible. In this study, based on paired samples of both placenta and cord blood tissues from 169 individuals, we investigated the methylation concordance between placenta and cord blood. We then employed a machine-learning-based model to predict locus-specific DNA methylation levels in placenta using DNA methylation levels in cord blood. We found that methylation correlation between placenta and cord blood is lower than other tissue pairs, consistent with existing observations that placenta methylation has a distinct pattern. Nonetheless, there are still a number of CpG sites showing robust association between the two tissues. We built prediction models for placenta methylation based on cord blood data and documented a subset of 1,012 CpG sites with high correlation between measured and predicted placenta methylation levels. The resulting list of CpG sites and prediction models could help to reveal the loci where internal or external influences may affect DNA methylation in both placenta and cord blood, and provide a reference data to predict the effects on placenta in future study even when the tissue is not available in an epidemiological study.

已知DNA甲基化（DNA methylation）可响应产前暴露，这可能是连接早期发育暴露与后续慢性疾病的机制之一。诸多研究采用血液样本检测DNA甲基化，但DNA甲基化具有组织特异性。胎盘是胎儿生长发育的核心器官，但在大型流行病学研究中，获取胎盘组织样本往往可行性极低；与之相对，脐带血样本则更易于采集。本研究基于169名受试者的胎盘与脐带血配对样本，探究了二者之间的甲基化一致性。随后，我们构建了基于机器学习的预测模型，利用脐带血的DNA甲基化水平预测胎盘的位点特异性DNA甲基化水平。研究发现，胎盘与脐带血的甲基化相关性低于其他组织配对，这与已有研究中“胎盘甲基化具有独特模式”的观测结果一致。尽管如此，二者之间仍存在大量CpG位点（CpG sites）表现出强相关性。本研究基于脐带血数据构建了胎盘甲基化预测模型，并筛选出1012个CpG位点，其实测与预测的胎盘甲基化水平具有高度相关性。本研究得到的CpG位点列表与预测模型，有助于揭示同时影响胎盘与脐带血DNA甲基化的内外部影响位点，并可为未来流行病学研究中无法获取胎盘组织时，预测其对胎盘的影响提供参考数据。