Correcting for Population Structure in Gene-by-Environment Interaction Studies

描述(由申请人提供)：在过去的几年里，全基因组关联研究(GWAS)已经确定了许多与常见人类疾病相关的基因。在这些研究中，收集了数千个个体的基因变异，并将其与这些个体的疾病状况相关联。GWAS的一个具有挑战性的方面是，收集到的个体在不同程度上相互关联。这可能会导致虚假的关联，即表面上与疾病相关的基因，但实际上是个体之间相关性的人工制品。已经提出了几种方法来解决这个问题，并在公开可用的软件包中实施了这些方法。环境因素往往与遗传变异相互作用，从而增加疾病风险。识别这些相互作用，被称为基因-环境相互作用(GxE)，现在是人类研究和模式生物研究中的一个主要研究重点。发现GxE相互作用可以提供对疾病途径的洞察、对环境因素在疾病中的影响的理解、更好的风险预测和个性化治疗。小鼠等模型生物是研究GxE相互作用的理想环境，因为环境暴露可以仔细控制。不幸的是，与关联性研究中的关联性可能导致虚假关联的原因相同，关联性也可能导致虚假的基因与环境的相互作用。在这项提案中，我们建议开发一种方法论，在寻找基因与环境相互作用的研究中纠正相关性。我们项目的结果将是一套方法，即使研究中的个人是相关的，也可以一致地检测基因与环境的相互作用。然后，这些方法可以被参与研究的许多研究人员广泛使用，以发现基因与环境的相互作用。我们将把我们开发的方法应用于明尼苏达双胞胎和家庭研究中心(MCTFR)的数据，以调查基因-环境相互作用如何影响物质滥用(SA)的发展，并应用于小鼠遗传学研究，调查影响对高脂肪饮食的反应和心力衰竭易感性的遗传因素。我们将通过公开提供的软件包和网络服务器资源向研究界提供实施我们的方法。

DESCRIPTION (provided by applicant): Over the past few years genome-wide association studies (GWASs) have identified numerous genes associated with common human diseases. In these studies, genetic variation in thousands of individuals is collected and correlated with the disease status in these individuals. A challenging aspect of GWAS is that the collected individuals are related to each other by differing degrees. This can lead to spurious associations which are genes that appear to be associated with the disease, but in fact are an artifact of the relatedness between individuals. Several methods have been proposed to address this problem and are implemented in publicly available software packages. Environmental factors often interact with genetic variation to increase risk of disease. Identifying these interactions, referrd to as gene-by-environment (GxE) interactions, is now a major focus of research in both human studies and model organism studies. Discovering GxE interactions can provide insight into disease pathways, an understanding of the effect of environmental factors in disease, better risk prediction and personalized therapies. Model organisms such as mouse are ideal environments for studying GxE interactions because environmental exposures can be carefully controlled. Unfortunately, for the same reasons that relatedness can cause spurious associations in association studies, relatedness can cause spurious gene-by- environment interactions. In this proposal we propose to develop methodology that corrects for relatedness in studies that search for gene-by-environment interactions. The results of our project will be a set of methods that are can detect gene-by-environment interactions consistently even when the individuals in the study are related. These methods can then be widely used by many researchers involved in studies to discovery gene-by-environment interactions. We will apply our developed methods to the Minnesota Center for Twin and Family Research (MCTFR) data to investigate how gene-environment interplay influences the development of substance abuse (SA) and to mouse genetic studies investigating the genetic factors which influence response to high fat diet and susceptibility to heart failure. We will make implement our methods available to the research community through publicly available software packages and webserver resources.