Methods for Pathway Modeling with Application to Folate

描述(申请人提供)：分子流行病学领域的一个重要挑战是开发研究多种基因和多种环境因素作为相互作用的致病因素的方法。这项建议的总体目标是开发解决这一问题的新方法，将叶酸代谢作为结直肠癌发生过程中的一个重要候选途径，并作为其他候选生化和代谢途径的原型，并将其应用于几个关于结直肠癌和结直肠腺瘤的数据集。我们有以下具体目标：1.开发一个包含生物学上对复杂途径结构的理解的建模框架，以叶酸代谢为例。这将涉及叶酸代谢的微分方程式模型的扩展，使用Silico模型中对此的预测来为我们的分层建模框架推导先前的协变量，并开发一种考虑模型不确定性的综合方法。这些不同的方法将被说明，并与使用来自结肠癌家族登记(C-CFR)叶酸研究、腺瘤病例对照研究和随机腺瘤预防试验的数据的纯探索性方法进行对比。2.扩展这一框架，以利用生物标记物测量中间代谢物浓度和酶活性速率在受试者的子样本上。我们将开发分析方法并探索最佳抽样方案，对整个研究人群可用的疾病、暴露和基因类型的各种组合进行分层。这些方法将用已有的腺瘤病例对照中同型半胱氨酸水平的数据、将在未决的C-CFR拨款申请中测量的其他生物标记物和两项纵向研究来说明。3.将叶酸途径的生物学知识组织成正式的本体论。我们将开发系统的方法，从本体中提取先前的协变量，用于我们的分层建模框架，并将探索纳入跨物种路径进化的信息的方法。4.将这一候选途径方法扩展到基因组范围。我们将探索从全基因组数据推断途径的方法，以及使用全基因组关联数据进行基于途径的分析的方法。这些方法将应用于正在进行的C-CFR全球气候变化分析的数据。我们提出的四个样本数据集部分是为了说明一系列设计，包括基于家庭的、基于人群的病例对照、纵向和随机试验。 公共卫生相关性：该项目的总体目标是开发统计方法，用于模拟涉及多个相互作用的基因和环境因素的代谢途径对复杂人类特征的影响，纳入生物标记物测量并利用外部生物和基因组信息产生的本体论。我们的方法最初将被开发用于候选基因研究，后来扩展到全基因组范围。它们将被应用于与结直肠腺瘤和结直肠癌相关的叶酸代谢的观察和实验研究，并有望产生预测个体疾病风险和洞察其潜在生物学机制的改进模型。

DESCRIPTION (provided by applicant): An important challenge in the field of molecular epidemiology is to develop methods for studying multiple gene and multiple environmental factors as interacting factors contributing to disease. The overall goal of this proposal is to develop novel approaches to this problem, using folate metabolism as an important candidate pathway in colorectal carcinogenesis and as a prototype for other candidate biochemical and metabolic pathways, and to apply them to several datasets on colorectal cancer and colorectal adenomas. We have the following specific aims: 1. Develop a modeling framework incorporating biological understanding about the structure of a complex pathway, illustrated by folate metabolism. This will involve extensions to our differential equations model for folate metabolism, using the predictions of this in silico model to derive prior covariates for our hierarchical modeling framework, and developing an integrated approach allowing for model uncertainty. These various methods will be illustrated and contrasted with purely exploratory methods using data from the Colon Cancer Family Registry (C-CFR) folate study, an adenoma case-control study, and a randomized adenoma prevention trial. 2. Extend this framework to exploit biomarker measurements of intermediate metabolite concentrations and enzyme activity rates on a subsample of subjects. We will develop analytical methods and explore optimal sampling schemes stratifying on various combinations of disease, exposure, and genotypes that are available on an entire study population. These approaches will be illustrated with already available data on homocysteine levels in the adenoma case-control, additional biomarkers that will be measured in a pending C-CFR grant application, and two longitudinal studies. 3. Organize biological knowledge for the folate pathway into a formal ontology. We will develop systematic methods for extracting prior covariates from an ontology for use in our hierarchical modeling framework, and will explore ways of incorporating information on evolution of pathways across species. 4. Extend this candidate pathway approach to the genome-wide scale. We will explore methods for inferring pathways from genome-wide data and for using genome-wide association data to inform pathway-based analyses. These methods will be applied to data from the ongoing C-CFR GWAS. The four example datasets we propose were chosen in part to illustrate a range of designs, including family-based, population-based case-control, longitudinal, and randomized trial. PUBLIC HEALTH RELEVANCE: The overall goal of this project is to develop statistical methods for modeling the effects of metabolic pathways involving multiple interacting genes and environmental factors on complex human traits, incorporating biomarker measurements and leveraging ontologies generated from external biological and genomic information. Our methods will initially be developed for candidate gene studies, and later extended to a genome-wide scale. They will be applied to observational and experimental studies of folate metabolism in relation to colorectal adenomas and colorectal cancer and are expected to yield improved models for predicting individual disease risks and insight into their underlying biological mechanisms.