Novel Statistical Methods for Gene-Environment Interactions in Complex Diseases

描述（由申请人提供）： 与单基因疾病不同，遗传和环境因素在控制冠心病、哮喘、癌症和精神疾病等复杂疾病的风险方面发挥着至关重要的相互作用。全基因组关联（GWA）扫描和更有针对性的研究所面临的主要挑战是难以检测到嵌入在大量统计和技术变异中的真实遗传信号，以及与检查大量标记相关的多重性。受这些问题和机会的激励，我们将开发一套互补的方法来解决基因环境（GXE）在许多层面上的相互作用，包括GWA研究，通过连锁基因定位，和候选基因的方法。对于每个级别，我们将开发新技术，填补现有方法的空白，或开发和评估新的战略方法。这里提出的研究特别关注增强方法来识别GxE相互作用，以寻找控制风险的生物学重要基因。我们提出了以下具体目标：（1）开发和评估新的统计方法，以优先考虑基因，通过适当的排名在全基因组关联研究，解决GxE相互作用;（2）开发和评估新的统计方法，定位因果基因的一部分，连锁和精细定位研究，同时考虑GxE相互作用;（3）开发和评估新的统计方法，以确定候选基因研究中环境变量和SNP之间的高阶相互作用;（4）调整现有的统计方法并开发新的统计方法，以解决不精确和缺失的环境和遗传测量;（5）开发和推广GxE分析的有效算法，并将这些方法应用于几项正在进行的复杂疾病的遗传学研究。我们的新的统计方法将适应多样性，识别和纳入测量的不确定性，利用基因组结构和丰富的信息，从相关的连锁，精细定位，和候选基因的研究，并通过生物相关的统计模型的结构推断。他们将有效利用现有信息，并在科学相关的框架内报告调查结果。我们提出的研究将赋予科学的好处，许多与公共卫生的影响，改善基因和环境因素之间的相互作用的特征控制复杂疾病的风险。特别是，量化GxE相互作用可以帮助识别高危人群进行重点干预和预防，并提高我们对特定疾病病因机制的理解。(End摘要）

DESCRIPTION (provided by applicant): Unlike monogenic diseases, both genetic and environmental factors play essential and interactive roles in controlling risk to complex diseases such as coronary heart disease, asthma, cancer and psychiatric disorders. A principal challenge facing genome-wide association (GWA) scans and more focused investigations is the difficulty in detecting true genetic signals embedded in considerable statistical and technical variation, and the multiplicity associated with examining a large number of markers. Motivated by these issues and opportunities, we will develop a suite of complementary methods to address gene environment (GxE) interactions at many levels, including GWA studies, gene localization via linkage, and candidate gene approaches. For each level we will develop new techniques that fill gaps in currently available methods or develop and evaluate new strategic approaches. The research proposed here specifically focuses on enhancing methods to identify GxE interactions in the search for biologically important genes controlling risk. We propose to address the following specific aims: (1) Develop and evaluate new statistical methods to prioritize genes through proper ranking in genome-wide association studies that address GxE interactions; (2) Develop and evaluate new statistical methods to localize causal genes as part of linkage and fine mapping studies while considering GxE interactions; (3) Develop and evaluate new statistical methods to identify higher order interactions between environmental variables and SNPs in candidate gene studies; (4) Adapt existing and develop new statistical methods to address imprecise and missing environmental and genetic measurements; and (5) Develop and disseminate efficient algorithms for GxE analyses, and apply these methods in several ongoing genetic studies of complex diseases. Our novel statistical approaches will accommodate multiplicity, identify and incorporate measurement uncertainty, take advantage of genomic structure and the wealth of information from related linkage, fine mapping, and candidate gene studies, and structure inferences through biologically relevant statistical models. They will make efficient use of available information and report findings in a scientifically relevant framework. Our proposed research will confer scientific benefits, many with public health implications, of improved characterization of the interaction between genes and environmental factors controlling risk to complex diseases. In particular, quantifying GxE interaction can help identify high risk groups for focused intervention and prevention, and improve our understanding of etiologic mechanisms for specific diseases. (End of Abstract)