Efficient Design and Analytic Strategies for Enhancing the Power of Detecting G X

描述(由申请人提供)：本提案是对关于检测人类疾病中基因-环境相互作用的方法和途径的资助机会公告PAR-11-032的回应(R21)。该提案将由多名私人助理牵头，他们是密歇根大学生物统计系的布拉马尔·慕克吉博士和宾夕法尼亚大学生物统计和流行病学系的陈金波博士。密歇根大学的Stephen B.Gruber博士、Sung Kyun Park博士和Naisdin Wang博士是该项目的关键临床和方法学顾问。在这项建议中，我们将有两个具体的目标：(I)在后全基因组关联研究(Gwas)时代，评估有效的两阶段设计和分析选择，在现有研究基地的研究对象的子样本的优先选择上收集额外的基因分型或生物标记物数据。这包括使用仅有遗传或环境数据的病例和对照的补充数据的可能性。这些方法是以现代回溯似然框架为指导的。(Ii)利用由私人投资机构开发的名为“主要相互作用分析”的新技术，开发筛选队列研究中相互作用的方法。该方法基于对基因和环境的加性主效应进行拟合后的交互作用矩阵的简约低阶表示。该提案计划将这种方法扩展到纵向研究，以捕捉相互作用的时变影响。将开发视觉诊断来确定至关重要的时间窗口，作为副产品。这一重要提案中计划开展的工作将对这次全球气候变化大会的使命做出有意义的贡献，并推动研究G×E效应的研究设计和分析技术。该提案将涉及陈博士和慕克吉博士之间的积极合作，他们是他们的博士/博士后实习生，并促进宾夕法尼亚大学和密歇根大学这两个同行机构之间的合作。该建议涉及统计学、医学、流行病学和人类遗传学在方法学发展方面的交叉。更广泛的影响是更好地理解疾病病因，并确定有针对性的干预策略的潜力。

DESCRIPTION (provided by applicant): This proposal is in response to the Funding Opportunity Announcement PAR- 11-032 on Methods and Approaches for Detection of Gene- Environment Interactions in Human Disease (R21). The proposal will be led by multiple PIs, Dr. Bhramar Mukherjee at the Department of Biostatistics, University of Michigan and Dr. Jinbo Chen at the Department of Biostatistics and Epidemiology, University of Pennsylvania. Dr. Stephen B. Gruber, Dr. Sung Kyun Park and Dr. Naisyin Wang from the University of Michigan are key clinical and methodological consultants on the project. In this proposal, we will have two specific aims: (i) Evaluate efficient two-phase design and analysis choices in the post genomewide association studies (GWAS) era where additional genotyping or biomarker data is collected on a prioritized selection of a sub-sample of study subjects in an existing study base. This includes the possibility of using supplementary data on cases and controls with only genetic or environmental data. The methods are guided by modern retrospective likelihood framework. (ii) Develop methods for screening of interaction in cohort studies using a novel technique developed by the PIs called "Principal Interactions Analysis". This method is based on a parsimonious low rank representation of the interaction matrix after fitting additive main effects of gene and environment. The proposal plans to extend this method to longitudinal studies to capture time- varying effects of interaction. Visual diagnostics to identify time-windows of critical importance will be developed as a byproduct. The planned work in this important proposal will meaningfully contribute to the mission of this FOA, and advance study design and analytical techniques for studying G x E effects. The proposal will involve active collaboration between Dr. Chen and Dr. Mukherjee, their doctoral/post-doctoral trainees and foster collaboration between two peer institutions: University of Pennsylvania and University of Michigan. The proposal lies in the intersection of statistics, medicine, epidemiology and human genetics in terms of methodology development. The broader impact is better understanding of disease etiology and identify potentials for targeted intervention strategies.