Exogenous and Genetic Determinants of the Internal Environment

摘要 在过去的十年里，不可知论方法导致了新的遗传关联的显著成果 全基因组关联研究(GWAS)的进展还没有与之相匹配的 环境方面。Chris Wild在2005年引入的“Exposome”概念是一种“全面的 对外部暴露(例如，化学、物理和生物制剂)的终生暴露史的描述， 一般外部环境(例如，气候、城乡、社会经济状况)和内部暴露(例如， 代谢物，肠道微生物区系)已经在内部化学物质的测量方面开始运作 特定的时间点，通常使用质谱学来描述“代谢物”。有了这个 机械全环境关联研究现在是可行的，但仍然有许多 在环境和环境影响评估概念之前的方法学挑战可以被认为是全球水资源评估的真正伙伴， 包括外部和内部环境的动态性，反向因果关系问题， 非遗传寄主和环境混杂因素的控制，测量误差(时间变异性， 仪器错误、未知化学物质的识别等)，以及进行全基因环境的方法 互动研究(GEWIS)。曝光组的一个重要组成部分是微生物组。证据是 越来越多的人将多种癌症类型中的肿瘤促进作用与细菌微生物区系的影响联系起来。本地 环境条件，受饮食、抗生素、前和益生菌等的影响，可能会影响 微生物群落，影响疾病风险和治疗反应。高吞吐量的到来 测序已经允许相对廉价地识别和量化数千个可操作的 分类单位(OTU)在单个生物谱系中，提供了关于复杂结构的丰富信息 常驻微生物群落。微生物组提出了许多相同的方法学挑战 暴露，如时间依存性、逆因果性、非遗传性混杂，也有一些不同 它们喜欢用来描述多样性和韧性等社区影响的方法。虽然GxE和GXG是 同样相关、同样令人感兴趣的是宿主-微生物相互作用和暴露组-微生物组相互作用。我们 提出一种综合的方法来开发统计方法来研究内部的决定因素 与外部环境和宿主有关的环境(代谢组和微生物组) 基因组和内部环境与疾病风险的关系。作为这一点的一部分，我们建议开发 贝叶斯网络方法将所有这些变量联系起来，并调查调解。我们将运用我们的方法 例如，来自多种族队列、ColoCare联盟和一项双胞胎结肠息肉研究的数据。

ABSTRACT The remarkable yield of novel genetic associations over the last decade resulting from the agnostic approach of genome-wide association studies (GWAS) had not been matched by comparable advances on the environmental side. The "exposome" concept introduced by Chris Wild in 2005 as a "comprehensive description of lifelong exposure history" of external exposures (e.g., chemical, physical, and biological agents), general external environment (e.g., climate, urban-rural, socioeconomic position), and internal exposures (e.g., metabolites, gut microflora) has been operationalized in terms of the measurement of internal chemicals at particular points in time, typically using mass spectrometry to characterize the "metabolome." With this machinery Environment-Wide Association Studies (EWAS) are now feasible, but there remain numerous methodological challenges before the EWAS concept can be considered a real companion to GWASs, including the dynamic nature of the external and internal environment, the problem of reverse causation, control of non-genetic host and environmental confounders, measurement error (temporal variability, instrument error, identification of unknown chemicals, etc.), and ways of conducting Gene-Environment-Wide Interaction Studies (GEWIS). An important component of the exposome is the microbiome. Evidence is mounting linking tumor promotion in a broad array of cancer types to the effects of bacterial microbiota. Local environmental conditions, affected by diet, antibiotics, pre- and probiotics, etc., could affect the structure of microbial communities, affecting risk of disease and response to therapy. The advent of high-throughput sequencing has allowed the relatively inexpensive identification and quantification of thousands of operational taxonomic units (OTUs) in a single biospecimen, providing a wealth of information on the complex structure of resident microbial communities. The microbiome raises many of the same methodological challenges as the exposome, such as time dependency, reverse causation, and non-genetic confounding, but also some different ones like ways of characterizing community effects like diversity and resilience. Although GxE and GxG are also relevant, equally interesting are host-microbial interactions and exposome-microbiome interactions. We propose an integrated approach to developing statistical methods for studying the determinants of the internal environment (the metabolome and the microbiome jointly) in relation to the external environment and the host genome and the relationship of the internal environment to disease risk. As part of this we propose to develop Bayesian network methods to relate all these variables and investigate mediation. We will apply our methods to data from, e.g., the Multi-Ethnic Cohort, the ColoCare Consortium, and a study of colorectal polyps in twins.