Genetics of Asthma and COPD

哮喘和慢性阻塞性肺疾病（COPD）是两种最常见的慢性疾病， 呼吸道了解这些疾病的遗传基础将使我们能够剖析 机制，评估风险，并最终导致个性化治疗。在动物中发现疾病基因 复杂的人类疾病模型更容易，更符合成本效益， 很好地描述了人类队列。在小鼠品系间观察到两种天然 气道反应性和吸烟诱导的COPD的发展，表明遗传因素的作用， 小鼠中这些表型的决定因素。然而，在哮喘和COPD研究领域， 缺乏全面的品系调查，动物QTL研究也太少，无法利用 最新的基因组和蛋白质组学研究，已开发和利用，以寻找QTL在其他复杂的 人类疾病在这个项目中，我们将确定影响气道高反应性（AHR）的基因， 通过对36个近交系小鼠AHR和COPD的检测，观察香烟烟雾诱导的小鼠COPD。 随后，我们将采用硅计算QTL分析来检测含有AHR和 COPD基因，特别关注那些影响AHR和COPD的基因组区域。基于 在计算QTL分析之后，我们接下来将选择亲本菌株并进行实际的QTL杂交。 最后，一旦QTL已经确定，我们将集中在那些位于同源位置， 人类QTL，并使用几种数据挖掘技术和额外的遗传杂交来寻找候选 基因.这些候选基因将在项目1（哮喘）的人群中进行相关性测试 和2（COPD），从而结合了人类和小鼠系统的优势。我们预计， 这一应变调查信息的科学界将刺激更多的研究模型， 哮喘和COPD的研究，将加速哮喘和COPD基因的发现。因为高 在人类和小鼠常见疾病（包括哮喘）QTL位置之间的同源性， 假设使用动物模型和人类的基因发现的协调方法 人口将更加具有成本效益和成功，并将提供重要的基础设施， 哮喘和慢性阻塞性肺病的遗传学研究的继续。

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are the two most common chronic diseases of the airways. Understanding the genetic basisfor these diseaseswill allow us to dissect pathogenetic mechanisms, assess risk, and ultimately lead to individualized therapy. Finding disease genes in animal models of complex human diseases is easier and more cost effective then relying only upon investigation in well characterized human cohorts. Significant variability across mouse strains is observed for both native airways responsiveness and the development of smoke-induced COPD, suggesting a role for genetic determinants of these phenotypes in the mouse. However, in the fields of asthma and COPDresearch, there is a lack of comprehensive strain surveysand too few animalQTL studies to take advantage of the latest genomic and proteomic research that have been developed and utilized to find QTL in other complex human disease. In this project, we will identify genes that influence airway hyperresponsiveness (AHR) and cigarette smoke-induced COPD in mice by surveying36 inbred mousestrains for AHR and COPD. Subsequently, we will employ in silica computationalQTL analysisto detect regions that containAHR and COPD genes, particularly focusing on those genomic regions that influence both AHR and COPD. Based on the computational QTL analysis, we next will select parental strains and carry out actual QTL crosses. Finally, once the QTL have been identified, we will focus on those that are located in positions homologous to human QTL and use several data mining techniques and additionalgenetic crossesto find candidate genes. These candidate genes will be tested for associationin human populations in Projects 1 (asthma) and 2(COPD), thus combining the strengths of the human and mouse systems. We anticipate that providing this strain survey information to the scientific communitywill stimulate additional research in models of asthma and COPD, and will acceleratethe finding of asthma and COPDgenes. Becauseof the high homology between human and mouse locationsfor QTL for commondiseases, including asthma,we hypothesize that a coordinated approach to gene finding using both the animal model and human populations will be more cost-effectiveand successful,and will provide an important infrastructurefor the continuation of genetic research in asthmaandCOPD.