Novel Analysis of Gene-Gene and Gene-Environment Interactions in Alcoholism

描述(由申请人提供)：预防和控制复杂疾病的一个关键组成部分，如酒精依赖，是确定有助于其发展和进展的遗传和环境因素。遗传和环境因素(如性别、环境应激源)在疾病的发生和发展中相互作用，这一事实使分析变得复杂。此外，复杂疾病很可能是由遗传标记之间的相互作用引起的；因此，每个单独标记(例如单核苷酸多态(SNP))对复杂疾病风险的影响可能相对较小。这个项目的目标是1)。为分析病例对照研究的数据提供了一个框架，具有以下特点：a.涉及一组遗传标记的风险因素，这些遗传标记可能相互作用，导致一种复杂疾病的发病；b.遗传和环境因素，确定对疾病的发生和发展具有异常抵抗力或易感性的人群亚群；2)将建议的方法应用于通过基因型和表型数据库收集的数据，作为成瘾研究的一部分：遗传学与环境(SAGE)。提出的方法与传统方法的主要区别在于，PI考虑根据生物医学实验提出的功能组织的标记组，使用不同级别的遗传信息：(1)连锁不平衡模式；(2)基因；(3)途径。鉴于哪些基因共同作用影响饮酒的各个方面的信息，该项目旨在调查每个组内遗传标记的相互作用结构。PIS开发了一种新的途径分析，它提供了结合来自多个遗传变异的关联证据的机会，从而潜在地有更好的机会确定该途径与疾病之间的关联。此外，他们提出了一种在存在复杂的基因-环境相互作用的情况下进行数据分析的新方法，包括两个主要部分：1.表征遗传和环境因素之间的相关依赖结构，(在酒精依赖的情况下，双倍型效应和性别和环境应激源)；2.将依赖结构纳入回归分析。建议的方法有可能通过联合分析来提高对酒精中毒的遗传易感性和环境风险因素的理解。该方法灵活，可用于其他药物依赖障碍的分析。此外，计划数据分析的结果有可能挑战对环境风险因素的传统概念化，并对公共卫生干预、评估和政策产生影响。

DESCRIPTION (provided by applicant): A key component to prevention and control of complex disorders, such as alcohol dependence, is to identify genetic and environmental factors that contribute to their development and progression. The analysis is complicated by the fact that genetic and environmental factors (e.g., gender, environmental stressors) interplay in disorder onset and progression. Further, complex disorders are likely to be caused by an interaction between genetic markers; hence the effect of each individual marker (e.g., single-nucleotide polymorphism (SNP)) on risk of a complex disorder is likely to be relatively small. The goals of this project are 1). provide a framework for the analysis of data from case-control studies with the following features: A. risk factors involving a panel of genetic markers that are likely to interact causing the onset of a complex disorder; B. genetic and environmental factors defining subgroups of population with unusual resistance or susceptibility to disorder onset and progression ; 2). apply the proposed methodology to the data collected as a part of Study of Addiction: Genetics and Environment (SAGE) available through the database of Genotypes and Phenotypes. The main difference of proposed approach from the traditional is that PIs consider groups of markers organized based on their function suggested by biomedical experiments, using various levels of genetic information: (1) linkage disequilibrium patterns; (2) genes; (3) pathways. Given the information about what genes work together to influence various aspects of alcohol use, the project aims to investigate the interaction structure of the genetic markers within each group. PIs develop a novel pathway analysis which offers the opportunity to combine association evidence from multiple genetic variants and thus potentially has a better chance of identifying the association between the pathway and the disease. Moreover, they propose a novel methodology for analysis of data in the presence of complex gene-environment interactions, consisting of two major parts: 1. characterize the relevant dependence structure between genetic and environmental factors, (in the case of alcohol dependency, diplotype effects and gender and environmental stressors); 2. Incorporate the dependence structure into the regression analysis. The proposed approach has the potential to improve understanding of genetic predisposition and environmental risk factors of alcoholism through their joint analysis. The approach is flexible and can be adapted for the analysis of other drug dependence disorders. Moreover, the findings of planned data analysis have the potential to challenge the traditional conceptualizations of environmental risk factors and have implications for public health interventions, evaluations and policies.