Bayesian Modeling and Optimal Design for Studies of Gene-Environment Association

描述(由申请人提供)： 复杂疾病的现代流行病学调查的成功，特别是基因与环境倡议(GEI)的成功，将取决于应对这些挑战的方法的发展及其有效、开放源码的实施。我们建议的研究主体的目标是利用贝叶斯统计方法来确定最优设计，并为研究基因、环境和疾病之间的关联开发有效的分析策略。这项研究将组织成三个目标。目标1的重点将是开发算法和方法，为以发现或复制关联为目的的基因环境研究确定贝叶斯最优研究设计。这将包括为发现关联而确定最佳多阶段研究设计的算法、给定一组资源限制的最佳设计和基于联合体的验证研究的分析方法。目标2的重点将是开发方法论方法来分析由假设驱动和无偏见(即全基因组)关联研究产生的数据。这项工作将包括检测候选途径和候选途径-环境相互作用关联的方法，如果存在的话，说明途径结构和功能的数据，以及用于基因-环境相互作用的全基因组分析的计算机有效方法。目标3，软件开发，将重点开发在目标1和2范围内开发的有效、可移植的方法的软件实施。在拟议的工作结束时，我们将开发、编码和测试算法或方法，以研究设计和分析在基因、环境暴露和疾病研究中产生的数据。最后，为了确保我们工作的易用性，我们将把我们开发的软件及其文件打包成用户友好的R统计语言包，并将其保存在R综合档案网(CRAN)上。(摘要结束)

DESCRIPTION (provided by applicant): Success of modern epidemiological investigations into complex disease and, in particular, success of the Genes and Environment Initiative (GEI) will depend on development of methods that address these challenges and on their efficient, open source implementation. Our objective with the proposed body of research is to utilize Bayesian statistical approaches to identify optimal designs and develop efficient analytic strategies for studies of association between genes, environment and disease. This research will be organized into three aims. The focus of Aim 1 will be to develop algorithms and methods to determine Bayesian optimal study designs for gene by environment studies whose purpose is either discovery or replication of associations. This will include algorithms for determining optimal multi-phase study designs for discovery of associations given a set of resource constraints and optimal designs and methods of analysis for consortium-based validation studies. The focus of Aim 2 will be to develop methodological approaches to the analysis of data generated by both hypothesis driven and unbiased (i.e. genome-wide) association studies. This work will include methods for detection of candidate pathway and candidate pathway-environment interaction associations accounting for data on pathway structure and function, if it exists and computer efficient methods for genome-wide analysis of gene-environment interaction. Aim 3, Software Development, will focus on development of efficient, portable software implementations of the methods developed in context of Aims 1 and 2. At the conclusion of the proposed work, we will have developed, coded and tested algorithms or approaches to study design and analysis of data generated in studies of genes, environmental exposure and disease. Finally, to ensure the accessibility of our work, we will package the software we develop and its documentation in a user-friendly R statistical language package and maintain it on the Comprehensive R Archive Network (CRAN). (End of Abstract)