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

项目 4：使用高维分析评估微生物组和表观遗传学的中介效应

• 批准号: 10091542
• 负责人: Zhigang Li
• 金额: $ 21.08万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2021-02-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10091542
• 关键词: Address; Affect; Area; Arsenic; Biological Assay; Biological Process; Birth; Cells; Child Health; Child Support; Childhood; Cohort Studies; Communities; Complex; Computer software; Consumption; Custom; DNA; DNA Methylation; Data; Development; Dimensions; Disease; Disease Pathway; Early Intervention; Environmental Exposure; Environmental Pollution; Epidemiology; Epigenetic Process; Etiology; Exposure to; Food; Health; Heritability; Human Microbiome; Human Milk; Hypersensitivity; Immune; Individual; Infant; Infant Health; Infection; Investigation; Lasso; Life; Mediating; Mediation; Mediator of activation protein; Methods; Microbe; Modeling; Molecular; Molecular Epidemiology; New Hampshire; Newborn Infant; Outcome; Pathogenesis; Pathway interactions; Phylogenetic Analysis; Research Personnel; Role; Source; Structure; Techniques; Testing; Time; Umbilical Cord Blood; atopy; breast milk microbiome; cost; data structure; design; drinking water; epidemiology study; epigenome; gut microbiota; high dimensionality; human DNA; human disease; improved; in utero; infant gut microbiome; infant infection; infection risk; methylome; microbial; microbial composition; microbiome; microbiome research; microbiota; multidimensional data; novel; open source; prenatal exposure; therapy design; tool; toxicant

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.

项目 4 摘要 高维人类微生物组和 DNA 甲基化数据为促进 了解多种人类疾病的根本病因。中介建模是一个重要的工具 用于分子流行病学以推断生物过程的因果路径。迄今为止，中介模型 尽管它可能会澄清，但尚未广泛应用于微生物组和表观基因组的研究 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 人类微生物组是否介导疾病的发生。使用中介模型的障碍出现 从微生物组的组成、系统发育层次、稀疏和高维结构 数据。另一个层次的复杂性是，中介可以通过个体微生物或微生物的变化来发生。 通过改变微生物组的整体群落结构。对于 DNA 甲基化数据、模型 存在用于分析中介效应；然而，当前的方法依赖于参考数据来调整细胞 构图效果。然而，参考数据往往无法获得，而且获取成本高昂。无参考 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采用 应用于中介分析。为了应对这些关键挑战，我们将开发新的调解方法 分析人类微生物组和 DNA 甲基化作为复杂介质的高维数据 致病途径。我们将应用我们的模型来测试婴儿肠道微生物组、乳房的影响 乳汁微生物组、脐带血 DNA 甲基化组和母乳 DNA 甲基化组在调节关联中的作用 产前暴露（例如砷暴露）和第一年的儿童感染和过敏/特应性之间 使用来自大型持续纵向分子流行病学新罕布什尔州出生的丰富数据来了解生命 队列研究。将开发 R 包来实现这两个模型。这些方法将使 识别疾病途径的复杂介质，以突出设计干预措施的机会 支持儿童的健康和发展。