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

描述（由申请人提供）：本提案是对资助机会公告PAR- 11-032关于检测人类疾病中基因-环境相互作用的方法和途径（R21）的回应。该提案将由密歇根大学生物统计系的Bhramar Mukherjee博士和宾夕法尼亚大学生物统计与流行病学系的Jinbo Chen博士等多位pi领导。来自密歇根大学的Stephen B. Gruber博士、Sung Kyun Park博士和Naisyin Wang博士是该项目的主要临床和方法顾问。在本提案中，我们将有两个具体目标：(i)评估后全基因组关联研究（GWAS）时代有效的两阶段设计和分析选择，其中额外的基因分型或生物标志物数据是在现有研究基础上优先选择研究对象的子样本收集的。这包括可能只使用遗传或环境数据来补充病例和控制数据。方法以现代回顾性似然框架为指导。（ii）利用pi开发的一种称为“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。该方法基于拟合基因和环境的可加性主效应后相互作用矩阵的简化低秩表示。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效应。作为一种副产品，将开发用于识别至关重要的时间窗的可视化诊断。这一重要提案中的计划工作将对FOA的使命做出有意义的贡献，并推进研究gx 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及其博士/博士后学员之间的积极合作，并促进宾夕法尼亚大学和密歇根大学两所同行机构之间的合作。该建议是统计学、医学、流行病学和人类遗传学在方法发展方面的交叉。更广泛的影响是更好地了解疾病的病因，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.