Gene-environment interaction in association analysis

描述（由申请人提供）：复杂人类表型中的基因x环境相互作用（GxE）可能是常见、重要且难以检测的。特别是，对于行为和精神表型的理解，需要将遗传，生物，心理和社会因素的多个层面结合在一起的方法，允许这些因素的相互作用和相关性。然而，交互作用的统计检验容易出现假阳性（因为统计交互作用的存在取决于如何定义主效应）和假阴性结果（因为交互作用的检验具有低功效）。此外，在大规模关联分析的背景下（其中多重测试已经是一个关键问题），如果缺乏适当的统计严谨性，GxE的纳入可能只会加剧相当多的现有问题。该应用旨在开发和评估新的方法，特别强调对非线性效应的鲁棒性;扩展现有方法以适应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，以改进临时阳性关联，而不是增加多重测试负担;在发展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;创建自由分发的软件实现。扩大的初步研究，目标是开发和实施一个最大似然方法，提供一个通用的框架，用于测试GXE在家庭和无关的个人，二分和连续性状，等位基因，基因型或单倍型关联。模拟研究（使用基于渐近理论的标准方法以及动态的，非线性的发展方法）的设计，将评估在广泛的遗传模型的方法的性能。

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.