HIERARCHICAL MODELING OF INTERACTIONS IN GENOME-WIDE AND PATHWAY-BASED STUDIES

全基因组和基于通路的研究中相互作用的层次建模

• 批准号: 7508637
• 负责人: David V Conti
• 金额: $ 53.84万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-16 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7508637
• 关键词: Accounting; Address; Air Pollution; Alcohols; Biological; Biological Markers; California; Candidate Disease Gene; Child health care; Chronic; Cohort Studies; Colon Carcinoma; Complex; DNA Repair; Data; Data Analyses; Databases; Dependency; Diet; Disease; Drug Kinetics; Environment; Environmental Risk Factor; Epidemiologic Studies; Estrogens; Etiology; Evaluation; Exons; Genes; Genetic; Genetic Polymorphism; Genetic Predisposition to Disease; Genome; Genomics; Goals; Grant; Haplotypes; Hawaii; Health; Inflammation; Investigation; Kinetics; Knowledge; Lung diseases; Malignant Neoplasms; Measurement; Measures; Metabolic Pathway; Methods; Methylation; Modeling; Non-Steroidal Anti-Inflammatory Agents; Obesity; Ontology; Oxidative Stress; Passive Smoking; Pathway interactions; Performance; Phase; Physical activity; Proteomics; Research Design; Resources; Sampling; Scheme; Smoking; Somatic Mutation; Statistical Models; Structure; Techniques; Testing; United States National Institutes of Health; Work; base; breast cancer family registry; colon cancer family registry; data mining; data modeling; design; gene environment interaction; gene interaction; genetic epidemiology; genome wide association study; genome-wide; improved; metabolomics; prevent; public health relevance; respiratory; simulation; software development

DESCRIPTION (provided by applicant): Hierarchical Modeling of Interactions in Genome-Wide and Pathway-Based Association Studies: The overarching goal of this grant is to investigate the use of hierarchical modeling in the study of gene-environment and gene-gene interactions in both genome-wide association studies and pathway-based candidate gene studies. We will apply our methods to data available from two large NIH-supported projects: the Colon CFR and Children's Health Study. Data from these association studies often follows a natural hierarchical structure with polymorphisms within gene regions, genes within sub-pathways, and sub-pathways within etiologic networks. By building a statistical model to reflect this natural hierarchy we aim to better account for the dependencies between factors and to better incorporate our knowledge of the underlying etiology. In this proposal, in addition to evaluating the statistical form and structure of such models we also aim to gauge the impact of various types of prior information and intermediate measurements on inference. For genome-wide association studies we will develop analytic approaches for the incorporation of GxE interactions that deal with the multiple testing problem and extend to potentially more efficient 2-phase study designs. These methods will be expanded to test for the interaction of an environmental factor with multiple-SNPs, as well. Specifically with pathway-based studies, we aim to explore the feasibility and performance of mechanistic models (e.g. kinetic models) and hierarchical regression models with model selection for genes and environmental factors within sub-pathways and across networks of pathways. We use prior knowledge in the form of ontologies or expert-based relational databases to help formulate priors for the data analysis. Furthermore, we will investigate various multistage sampling schemes and their interplay with potential genomics data, including whole-exon expression, whole-genome somatic mutations and potential biomarker measures. Finally, we will compare our methods to various data mining techniques to allow genes to act within multiple pathways. Overall, we aim to develop statistical techniques that make it feasible to detect which genes involved in disease and, importantly, in which environmental context they act. By identifying both genetic and environmental factors, we will make progress in understanding the underlying mechanism that leads to disease and potentially identify ways in which to both prevent and treat complex diseases. PUBLIC HEALTH RELEVANCE: Overall, we aim to develop statistical techniques that formally incorporate our biologic knowledge and make it feasible to detect which genes are involved in disease and, importantly, in which environmental context they act. By identifying both genetic and environmental factors, we will make progress in understanding the underlying mechanism that leads to disease and potentially identify ways in which to both prevent and treat complex diseases.

描述（由申请人提供）：全基因组和基于通路的关联研究中相互作用的分层建模：这项资助的总体目标是研究分层模型在基因-环境和基因-基因相互作用研究中的使用全基因组关联研究和基于通路的候选基因研究。我们将把我们的方法应用于 NIH 支持的两个大型项目的数据：结肠 CFR 和儿童健康研究。这些关联研究的数据通常遵循自然的层次结构，其中包括基因区域内的多态性、子通路内的基因以及病因网络内的子通路。通过建立一个统计模型来反映这种自然层次结构，我们的目标是更好地解释因素之间的依赖性，并更好地结合我们对潜在病因学的了解。在这个提案中，除了评估此类模型的统计形式和结构之外，我们还旨在衡量各种类型的先验信息和中间测量对推理的影响。对于全基因组关联研究，我们将开发合并 GxE 相互作用的分析方法，处理多重测试问题并扩展到可能更有效的两阶段研究设计。这些方法也将扩展到测试环境因素与多 SNP 的相互作用。具体来说，通过基于通路的研究，我们的目标是探索机械模型（例如动力学模型）和层次回归模型的可行性和性能，以及子通路内和跨通路网络的基因和环境因素的模型选择。我们使用本体或基于专家的关系数据库形式的先验知识来帮助制定数据分析的先验知识。此外，我们将研究各种多阶段采样方案及其与潜在基因组学数据的相互作用，包括全外显子表达、全基因组体细胞突变和潜在的生物标志物测量。最后，我们将我们的方法与各种数据挖掘技术进行比较，以允许基因在多种途径中发挥作用。总体而言，我们的目标是开发统计技术，使检测哪些基因与疾病有关，以及更重要的是，检测它们在何种环境背景下发挥作用成为可能。通过识别遗传和环境因素，我们将在理解导致疾病的潜在机制方面取得进展，并有可能找到预防和治疗复杂疾病的方法。公共卫生相关性：总体而言，我们的目标是开发统计技术，正式融入我们的生物学知识，并使检测哪些基因与疾病有关，以及更重要的是，检测它们在何种环境背景下发挥作用成为可能。通过识别遗传和环境因素，我们将在理解导致疾病的潜在机制方面取得进展，并有可能找到预防和治疗复杂疾病的方法。