BC Children's Hospital Research Institute (BCCHR), 950 W 28th Ave, Vancouver, BC, V5Z 4H4, Canada.
Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3N1, Canada.
Epigenetics Chromatin. 2023 Oct 4;16(1):37. doi: 10.1186/s13072-023-00507-5.
Genome-wide DNA methylation (DNAme) profiling of the placenta with Illumina Infinium Methylation bead arrays is often used to explore the connections between in utero exposures, placental pathology, and fetal development. However, many technical and biological factors can lead to signals of DNAme variation between samples and between cohorts, and understanding and accounting for these factors is essential to ensure meaningful and replicable data analysis. Recently, "epiphenotyping" approaches have been developed whereby DNAme data can be used to impute information about phenotypic variables such as gestational age, sex, cell composition, and ancestry. These epiphenotypes offer avenues to compare phenotypic data across cohorts, and to understand how phenotypic variables relate to DNAme variability. However, the relationships between placental epiphenotyping variables and other technical and biological variables, and their application to downstream epigenome analyses, have not been well studied.
Using DNAme data from 204 placentas across three cohorts, we applied the PlaNET R package to estimate epiphenotypes gestational age, ancestry, and cell composition in these samples. PlaNET ancestry estimates were highly correlated with independent polymorphic ancestry-informative markers, and epigenetic gestational age, on average, was estimated within 4 days of reported gestational age, underscoring the accuracy of these tools. Cell composition estimates varied both within and between cohorts, as well as over very long placental processing times. Interestingly, the ratio of cytotrophoblast to syncytiotrophoblast proportion decreased with increasing gestational age, and differed slightly by both maternal ethnicity (lower in white vs. non-white) and genetic ancestry (lower in higher probability European ancestry). The cohort of origin and cytotrophoblast proportion were the largest drivers of DNAme variation in this dataset, based on their associations with the first principal component.
This work confirms that cohort, array (technical) batch, cell type proportion, self-reported ethnicity, genetic ancestry, and biological sex are important variables to consider in any analyses of Illumina DNAme data. We further demonstrate the specific utility of epiphenotyping tools developed for use with placental DNAme data, and show that these variables (i) provide an independent check of clinically obtained data and (ii) provide a robust approach to compare variables across different datasets. Finally, we present a general framework for the processing and analysis of placental DNAme data, integrating the epiphenotype variables discussed here.
利用 Illumina Infinium 甲基化 bead 阵列对胎盘进行全基因组 DNA 甲基化 (DNAme) 分析，常用于探索宫内暴露、胎盘病理学和胎儿发育之间的联系。然而，许多技术和生物学因素会导致样本之间和队列之间的 DNAme 变异信号，理解和考虑这些因素对于确保有意义和可重复的数据分析至关重要。最近，已经开发了“表型推断”方法，通过这些方法可以利用 DNAme 数据推断出与表型变量（如胎龄、性别、细胞组成和祖源）相关的信息。这些表型推断变量为跨队列比较表型数据以及了解表型变量与 DNAme 变异性之间的关系提供了途径。然而，胎盘表型推断变量与其他技术和生物学变量之间的关系，以及它们在下游表观基因组分析中的应用，尚未得到很好的研究。
使用三个队列的 204 个胎盘的 DNAme 数据，我们应用 PlaNET R 包来估计这些样本中的表型推断变量胎龄、祖源和细胞组成。PlaNET 祖源估计值与独立的多态性祖源信息标记高度相关，平均而言，表观遗传学胎龄在报告的胎龄的 4 天内被估计，突出了这些工具的准确性。细胞组成估计值在队列内和队列间以及非常长的胎盘处理时间内都存在差异。有趣的是，滋养细胞与合胞体滋养细胞的比例随着胎龄的增加而降低，而且在母亲的种族（白人比非白人低）和遗传祖源（更高概率的欧洲祖源低）方面略有不同。基于它们与第一主成分的关联，原始队列和滋养细胞比例是该数据集 DNAme 变异的最大驱动因素。
这项工作证实了队列、阵列（技术）批次、细胞类型比例、自我报告的种族、遗传祖源和生物学性别是任何 Illumina DNAme 数据分析中需要考虑的重要变量。我们进一步展示了为使用胎盘 DNAme 数据而开发的表型推断工具的具体用途，并表明这些变量（i）为临床获得的数据提供了独立的检查，（ii）为跨不同数据集比较变量提供了稳健的方法。最后，我们提出了一个用于处理和分析胎盘 DNAme 数据的通用框架，整合了这里讨论的表型推断变量。
