Using Networks to Assign Gene Function in Lung Disease

描述(申请人提供)：全基因组关联研究(GWAS)分析了数千名受试者的基因组变异模式，并确定了大量与各种肺部疾病相关的基因和染色体区域。由于这些基因中的许多仍然没有得到很好的描述，肺脏研究面临的一个主要挑战是系统地研究它们的生物学功能、它们参与的途径以及基因突变对两者的影响。幸运的是，有大量的基因组、转录组和表观基因组数据可以用来帮助阐明基因功能。在这里，我们建议利用这些数据，使用先进的计算方法来系统地表征通过以慢性阻塞性肺疾病(COPD)为重点的Gwas肺部疾病研究确定的基因的功能。几个研究小组，包括参与这一拟议项目的研究人员，已经对COPD进行了详细的遗传、基因组和表观基因组研究，并确定了与疾病相关的多个遗传位点。另一方面，这些研究产生的多种数据类型提供了一个独特的令人兴奋的机会，通过使用数据整合计算方法来系统地阐述可检验的假设。为此，我们组建了一个跨学科的团队，其中包括GWAS、医学、实验室生物学和生物信息学方面的专家。我们将应用最新发展的基于系统生物学的方法来整合多种数据类型，并构建以Gwas候选基因为中心的基因调控网络(GRN)，并从这些网络中使用局部网络来预测未表征基因的功能。然后将对这些新的功能分配进行实验验证，并在必要时通过额外的几轮网络推理和实验评估进一步完善。最后，我们将创建一个可公开访问的网站，使肺部社区可以访问功能预测和网络模型。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have analyzed patterns of genomic variation in thousands of subjects and identified a large number of genes and chromosomal regions that are associated with various lung diseases. Since many of these genes remain poorly characterized, a major challenge facing pulmonary research is to systematically investigate their biological functions, the pathways in which they are involved, and the effects of genetic mutations on both. Fortunately, there is a large and growing body of genomic, transcriptomic, and epigenomic data that can be used to help shed light on gene function. Here we propose to leverage these data, using advanced computational methods to systematically characterize the functions of genes identified through GWAS studies for lung diseases with an emphasis on Chronic Obstructive Pulmonary Disease (COPD). Several research groups, including investigators involved in this proposed project, have conducted detailed genetic, genomic, and epigenomic studies on COPD and identified multiple genetic loci that are associated with disease. On the other hand, the multiple data-types generated from these studies have provided a uniquely exciting opportunity to systematically formulate testable hypotheses by using data-integration computational methods. To this end, we have assembled an interdisciplinary team including experts in GWAS, medicine, laboratory biology, and bioinformatics. We will apply recently developed systems biology-based approaches to integrate multiple data-types and construct gene regulatory networks (GRN) centered on GWAS candidates and from these, use the local network to predict functions of the uncharacterized genes. These new functional assignments will then be experimentally validated and, if necessary, further refined through additional rounds of network inference and experimental assessment. Finally, we will create a publicly accessible website to make the functional predictions and network models accessible to the pulmonary community.