Integration of Genomics and the Environment

基因组学与环境的整合

• 批准号: 9763602
• 负责人: ANDREW P. FEINBERG
• 金额: $ 106.7万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-28 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9763602
• 关键词: Address; Adipose tissue; Affect; Animals; Azoxymethane; Baltimore; Behavioral; Biochemical; Biochemistry; Biological; Biometry; Blood; Body Composition; Carcinogen exposure; Cardiovascular Diseases; Cells; Childhood; Chromatin; Cohort Studies; Collaborations; Collection; Colon; Colon Carcinoma; Colonic Neoplasms; Communities; Computer software; Data; Data Set; Diabetes Mellitus; Diet; Disease; Disease Outcome; Environment; Environmental Exposure; Epidemiology; Epigenetic Process; Experimental Models; Exposure to; Foundations; Gene Expression; Gene Expression Profiling; Genetic; Genetic Materials; Genetic Variation; Genetic study; Genome; Genomic Segment; Genomic medicine; Genomics; Genotype; Glean; Glucose; Head; Health; Hippocampus (Brain); Human; Human Genetics; Imagery; Immunologics; In Situ; Information Theory; Intervention Studies; Liver; Longitudinal Studies; Malignant Neoplasms; Mathematics; Measurement; Measures; Mediator of activation protein; Metabolic; Metabolic Diseases; Methods; Modeling; Modification; Mus; Nature; Noise; Obesity; Organism; Outcome; Phenotype; Physics; Population; Prefrontal Cortex; Pregnancy; Prevention Research; Psychological Stress; Reproducibility; Resolution; Resources; Shapes; Statistical Mechanics; Statistical Methods; Stress; System; Technology; Testing; Thinness; Tissues; Translating; Uncertainty; Ursidae Family; Women' s Health; adipokines; bariatric surgery; base; behavioral outcome; bisulfite sequencing; body system; cohort; computer science; computerized tools; cytokine; diet and cancer; disease phenotype; epigenome; epigenomics; experimental study; gene environment interaction; gene function; genome-wide; human disease; improved; information model; insulin tolerance; ketogentic; mouse model; neuropsychiatric disorder; novel; novel strategies; public health relevance; response; restraint; sex; single cell analysis; tool; trait; transcriptome; transcriptomics; virtual; western diet; whole genome

 DESCRIPTION (provided by applicant): The environment is perhaps the major contributor to human disease, yet its effect is largely ignored in whole genome approaches to genetics. In this CEGS, we will attack head-on the mechanisms through which environment influences genomic function, focusing on two extremely important exposures highly relevant to human health: diet and its relationship to metabolic disease and cancer; and stress related to neuropsychiatric disease. This requires nearly complete control of the experimental system in a way that cannot be done in humans. Our first aim is develop a new foundational experimental mouse model for understanding gene-environment interaction (GxE). We will expose crosses of the genetically heterogeneous Collaborative Cross population of mice to three well-controlled diets - Western, Mediterranean, ketogenic - and measure phenotypes across organ systems representing metabolic and cardiovascular disease. We will also determine the effects of diet on azoxymethane-induced colon tumors; as well as stress on phenotypes relevant to neuropsychiatric disease. We will couple these analyses to whole genome bisulfite sequencing, chromatin accessibility analysis and transcriptomics of target tissues including liver, adipose tissue, colon, hippocampus, prefrontal cortex, and blood, measuring the nature and flow of information among environment, genome, epigenome, and transcriptome in determining phenotype. Our second aim is to develop new statistical methods as well as a novel mathematical framework for handling the interaction between genetics, epigenetics, and exposure, which will allow us to model how information is passed between these three domains to ultimately shape phenotype. These methods include causal inference testing, analysis of genomically discontiguous genotype-epigenotype relationships, and novel stochastic approaches based on fundamental concepts of statistical physics and information theory. Our third aim is to perform replication in naïve animals and in highly relevant human epidemiologic cohorts: DWH and ALSPAC for diet exposures and metabolic disorders; NHSII and EPIC for diet and colon cancer; and ALSPAC and PIRC for stress and behavioral traits. Our fourth aim is to develop and promulgate new measurement, analytical, and computational technologies for comprehensive genomic analysis of GxE. These include new biological resources and software packages, as well as biochemical, cellular and computational tools to test and improve the models and conclusions from the other Aims, and of broad general use to the genomics community, including: analysis of single or multiple marks for determining long-range GxE relationships; single cell and in situ analysis of gene expression and epigenetic modifications; and novel computational approaches including ultrafast alignment of large datasets. This highly interdisciplinary proposal involves completely novel combinations of mouse genomics, statistical physics, biostatistics, computer science, biochemistry, epidemiology, and single cell analysis, in order to understand the essential question of how genomic function is shaped by the environment.

 描述（由申请人提供）：环境可能是人类疾病的主要贡献者，但其影响在遗传学的全基因组方法中很大程度上被忽视。在CEGS中，我们将正面攻击环境影响基因组功能的机制，重点关注与人类健康高度相关的两个极其重要的暴露：饮食及其与代谢疾病和癌症的关系;以及与神经精神疾病相关的压力。这需要对实验系统进行几乎完全的控制，而这在人类身上是做不到的。我们的第一个目标是建立一个新的基础实验小鼠模型，以了解基因-环境相互作用（GxE）。我们将使遗传异质性协作杂交小鼠群体的杂交小鼠暴露于三种控制良好的饮食-西方，地中海，生酮-并测量代表代谢和心血管疾病的器官系统的表型。我们还将确定饮食对氧化偶氮甲烷诱导的结肠肿瘤的影响，以及对神经精神疾病相关表型的压力。我们将把这些分析与全基因组亚硫酸氢盐测序、染色质可及性分析和靶组织（包括肝脏、脂肪组织、结肠、海马、前额叶皮层和血液）的转录组学相结合，测量环境、基因组、表观基因组和转录组之间的信息性质和流动，以确定表型。我们的第二个目标是开发新的统计方法以及一个新的数学框架来处理遗传学，表观遗传学和暴露之间的相互作用，这将使我们能够模拟信息如何在这三个领域之间传递，最终形成表型。这些方法包括因果推理测试，分析基因组不连续的基因型表观基因型的关系，和新的随机统计物理和信息论的基本概念的基础上的方法。我们的第三个目标是在幼稚动物和高度相关的人类流行病学队列中进行复制：DWH和ALSPAC用于饮食暴露和代谢紊乱; NHSII和EPIC用于饮食和结肠癌; ALSPAC和PIRC用于应激和行为特征。我们的第四个目标是开发和颁布新的测量、分析和计算技术，用于GxE的全面基因组分析。这些包括新的生物资源和软件包，以及生物化学、细胞和计算工具，以测试和改进其他目标的模型和结论，并广泛用于基因组学界，包括：分析单个或多个标记以确定长距离GxE关系;基因表达和表观遗传修饰的单细胞和原位分析;和新的计算方法，包括大数据集的超快对齐。这个高度跨学科的建议涉及小鼠基因组学，统计物理学，生物统计学，计算机科学，生物化学，流行病学和单细胞分析的全新组合，以了解基因组功能如何被环境塑造的基本问题。