Using Networks to Assign Gene Function in Lung Disease

描述（由申请人提供）：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，并确定了与各种肺部疾病相关的大量基因和染色体区域。由于许多这些基因的特征仍然很差，因此肺部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、它们所涉及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系统地表征通过GWAS研究鉴定的肺部疾病基因的功能，重点是慢性阻塞性肺疾病（COPD）。几个研究小组，包括参与这个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，从这些研究中产生的多种数据类型提供了一个独特的令人兴奋的机会，可以通过使用数据集成计算方法系统地制定可测试的假设。为此，我们组建了一个包括GWAS、医学、实验室生物学和生物信息学专家在内的跨学科团队。我们将应用最近开发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），并从中使用局部网络来预测未表征基因的功能。这些新的功能分配将在实验中得到验证，如有必要，将通过额外的网络推理和实验评估进一步完善。最后，我们将建立一个可公开访问的网站，使功能预测和网络模型对肺部社区开放。

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. PUBLIC HEALTH RELEVANCE: Numerous studies have probed the genetics of lung disease, analyzing variation in the genome across thousands of subjects. These studies have identified many variants within genes and other genomic regions that are strongly associated with the disease state, but often these are of unknown functional significance. Here, using COPD as a model, we propose to use "systems biology" methods to map these genes to pathways and to use these pathway-based associations to assign putative functions to these genes.