Project 4: Evaluating mediation effects of the microbiome and epigenetics using high dimensional assays

项目4摘要 高维人类微生物组和DNA甲基化数据为促进 对无数人类疾病的潜在病因学的理解。中介建模是一个重要的工具 在分子流行病学中用于推断生物过程的因果路径。到目前为止，中介建模 尚未被广泛应用于微生物组和表观基因组的研究，尽管它可能会澄清 它们在疾病发病机制中的关键作用。据我们所知，没有可用的中介模型可供测试 人类微生物群是否参与疾病的发生。出现了使用中介建模的障碍 从微生物组的组成、系统发育等级、稀疏和高维结构 数据。另一种复杂程度是，中介可以通过改变单个微生物或 通过改变微生物群落的整体结构。对于DNA甲基化数据，模型 存在用于分析中介效应的方法；然而，当前的方法依赖于参考数据来调整细胞- 合成效果。然而，参考数据往往无法获得，而且获取成本高昂。无参考文献 已经提出了关联分析的方法来解决这个问题，但这些方法还没有被提出 适用于调解分析。为了应对这些严峻挑战，我们将开发新的调解方法，以 分析作为复杂介体的人类微生物组和DNA甲基化的高维数据 致病途径。我们将应用我们的模型来测试婴儿肠道微生物群、乳房 乳汁微生物组、脐带血DNA甲基组和母乳DNA甲基组之间的关系 产前暴露(如砷暴露)与第一年的儿童感染和过敏/特应性之间的关系 使用来自正在进行的大型纵向分子流行病学新汉普郡出生的丰富数据 队列研究。将开发R包来实现这两个模型。这些方法将使 确定疾病途径的复杂介体，以突出设计干预措施的机会 支持儿童健康和发展。

PROJECT 4 ABSTRACT High dimensional human microbiome and DNA methylation data offer great promise to contributing to the understanding of the underlying etiology of a myriad of human diseases. Mediation modeling is a critical tool used in molecular epidemiology to infer causal pathways for biological processes. As yet, mediation modeling has not been extensively applied to studies of the microbiome and epigenome even though it is likely to clarify their critical roles in disease pathogenesis. To our knowledge, there are no available mediation models to test whether the human microbiome mediates disease occurrence. Impediments to using mediation modeling arise from the compositional, phylogenetically hierarchical, sparse, and high dimensional structure of microbiome data. Another level of complexity is that mediations can occur through changes in individual microbes or through alterations to the overall community structure of the microbiome. For DNA methylation data, models exist for analyzing mediational effects; however, current methods rely on reference data to adjust for cell- composition effects. Yet reference data are often not available and are costly to obtain. Reference-free approaches have been proposed for association analyses to resolve this issue, but these have not been applied to mediation analyses. To address these critical challenges, we will develop new mediation methods to analyze high-dimensional data on the human microbiome and DNA methylation as complex mediators in disease causing pathways. We will apply our models to test the effects of the infant gut microbiome, breast milk microbiome, cord blood DNA methylome, and breast milk DNA methylome in mediating the associations between prenatal exposures (e.g. arsenic exposure) and childhood infections and allergy/atopy in the first year of life using the rich data from the large ongoing longitudinal molecular epidemiologic New Hampshire Birth Cohort Study. R packages will be developed to implement these two models. These methods will enable the identification of complex mediators of disease pathways to highlight opportunities for designing interventions to support children's health and development.