Gene-environment interaction in association analysis

描述(由申请人提供)：基因x环境交互作用(GxE)在复杂的人类表型中很可能是常见的、重要的和难以检测的。特别是，为了理解行为和精神表型，需要将多层次的遗传、生物、心理和社会因素结合在一起，允许这些因素相互作用和相互关联。然而，交互作用的统计测试容易出现假阳性(因为统计交互作用的存在取决于主要影响的定义)和假阴性结果(因为交互作用测试的功率较低)。此外，在大规模关联分析(其中多重测试已经是一个关键问题)的背景下，纳入GxE，如果缺乏适当的统计严谨性，可能只会加剧相当多的现有问题。这项应用的目的是开发和评估新的方法，特别强调对非线性效应的稳健性；将当前的方法扩展到适合多基因和多环境的因果模型；开发和评估将GxE纳入大规模关联研究的框架，其方式是精炼临时的正相关性，而不是增加多重测试负担；在开发框架内进行探索性模拟研究，以调查非线性效应的可能性和影响；创建自由分布的软件实现。在初步研究的基础上，目标是开发和实施最大似然方法，为家系和无关个体的GxE测试、二分和连续性状测试、等位基因、基因类型或单倍型关联测试提供一个一般框架。已经设计了模拟研究(使用基于渐近理论的标准方法以及动态、非线性发展方法)，以评估这些方法在广泛的遗传模型中的性能。

DESCRIPTION (provided by Applicant): Gene x environment interaction (GxE) in complex human phenotypes is likely to be common, important and difficult to detect. In particular, for the understanding of behavioral and psychiatric phenotypes, approaches are needed that bring together multiple levels of genetic, biological, psychological and social factors, allowing for the interaction and correlation of these factors. However, statistical tests for interaction are prone to both false positive (as the presence of statistical interaction is dependent on how the main effects are defined) and false negative results (as tests for interaction suffer from low power). Furthermore, in the context of large scale association analysis (in which multiple testing is already a critical issue) incorporation of GxE, if lacking the appropriate statistical rigor, could potentially only exacerbate the considerable set of existing problems. This application aims to develop and evaluate new methodology with specific emphasis on robustness to nonlinear effects; to extend current methods to fit causal models to multiple genes and multiple environments; to develop and evaluate a framework for incorporating GxE in large-scale association studies in a way that refines provisional positive associations rather than increasing the multiple testing burden; to perform an exploratory simulation study within a developmental framework to investigate the likelihood and impact of nonlinear effects; to create freely-distributed software implementations. Expanding on preliminary studies, the objective is to develop and implement a maximum likelihood approach to provide a general framework for testing for GxE in families and unrelated individuals, for dichotomous and continuous traits, for allelic, genotypic or haplotypic association. Simulation studies (using both standard approaches based on asymptotic theory as well as a dynamic, nonlinear developmental approach) have been designed that will assess the performance of the methods across a broad range of genetic models.