Exogenous and Genetic Determinants of the Internal Environment

内部环境的外源和遗传决定因素

• 批准号: 9359372
• 负责人: Duncan C. Thomas
• 金额: $ 17.24万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9359372
• 关键词: Address; Affect; Antibiotics; Anus; Bayesian Modeling; Biological Markers; Biological Process; Biological Products; Blood Circulation; Case Study; Cell Proliferation; Chemical Agents; Chemicals; Climate; Clinical; Cohort Studies; Colonic Polyps; Colonoscopy; Colorectal Cancer; Colorectal Polyp; Communities; Companions; Complex; Constitutional; Data; Dependency; Descriptor; Diet; Environment; Environmental Exposure; Etiology; Evolution; Genes; Genetic; Genetic Determinism; Genome; Genomics; Genotype; Goals; High-Throughput Nucleotide Sequencing; Incidence; Infection; Inherited; Intestines; Lamina Propria; Link; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Mediating; Mediation; Methodology; Methods; Microbial Genetics; Modeling; Monozygotic twins; Mucous Membrane; Mutation; Nature; Obesity; Oral; Organ; Outcome; Phenotype; Phylogenetic Analysis; Pilot Projects; Play; Polyps; Probability; Probiotics; Process; Program Research Project Grants; Recording of previous events; Risk Factors; Role; Rural; Series; Side; Socioeconomic Status; Statistical Methods; Structure; Taxonomy; Techniques; Time; Tumor Promotion; Twin Multiple Birth; Uncertainty; Variant; cancer type; cohort; colon cancer patients; computer based statistical methods; disease phenotype; disorder risk; epidemiology study; genetic association; genetic makeup; genome wide association study; gut microbiome; gut microbiota; high dimensionality; host-microbe interactions; instrument; interest; metabolome; metabolomics; microbial; microbial community; microbial host; microbiome; microbiota; non-genetic; novel; outcome forecast; outcome prediction; predictive modeling; resilience; response; treatment response; tumor

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.

摘要 在过去十年中，由于不可知论的方法， 全基因组关联研究（GWAS）的进展与 环境方面。克里斯·怀尔德在2005年提出的“麻烦”概念， 外部暴露的"终生暴露史”的描述（例如，化学、物理和生物制剂）， 一般外部环境（例如，气候、城乡、社会经济地位）和内部暴露（例如， 代谢物，肠道菌群）已在内部化学物质的测量方面操作化， 在特定的时间点，通常使用质谱来表征“代谢组”。“有了这个 机械全环境协会研究（EWAS）现在是可行的，但仍然有许多 在EWAS概念可以被认为是GWAS的真实的伴侣之前的方法学挑战， 包括外部和内部环境的动态性质，反向因果关系的问题， 非遗传宿主和环境混杂因素的控制，测量误差（时间变异性， 仪器误差、未知化学品的识别等），以及进行全基因环境研究的方法 相互作用研究（GEWIS）。微生物组的重要组成部分是微生物组。证据 越来越多的人将一系列癌症类型中的肿瘤促进与细菌微生物群的影响联系起来。当地 受饮食、抗生素、益生菌和益生菌等影响的环境条件，可能会影响 微生物群落，影响疾病的风险和对治疗的反应。高通量的出现 测序已经允许相对便宜的鉴定和定量成千上万的可操作的 分类单位（OTU）在一个单一的生物标本，提供了丰富的信息的复杂结构， 常驻微生物群落。微生物组提出了许多相同的方法学挑战， 麻烦的，如时间依赖性，反向因果关系和非遗传混杂，但也有一些不同的 比如描述社区影响的方式，比如多样性和弹性。虽然GxE和GxG是 同样相关、同样令人感兴趣的是宿主-微生物相互作用和微生物组-微生物组相互作用。我们 提出一个综合办法，以制定统计方法，研究内部 环境（代谢组和微生物组）与外部环境和宿主的关系 基因组和内部环境与疾病风险的关系。作为其中的一部分，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中介。我们会用我们的方法 到来自，例如，多种族队列、ColoCare联盟和一项双胞胎结直肠息肉研究。