Genetics of Asthma and COPD

哮喘和慢性阻塞性肺疾病(COPD)是最常见的两种慢性疾病 航空公司。了解这些疾病的遗传基础将使我们能够剖析病因 机制，评估风险，并最终导致个体化治疗。在动物身上发现疾病基因 复杂的人类疾病的模型比仅仅依靠研究更容易和更具成本效益 很好地描述了人类队列的特征。观察到两种原生小鼠品系之间存在显著差异 呼吸道反应性与吸烟诱发的COPD的发展，提示基因在其中的作用 这些表型在小鼠中的决定因素。然而，在哮喘和慢性阻塞性肺病的研究领域， 缺乏全面的品系调查，而且动物QTL研究太少，无法利用 已开发和利用的最新基因组和蛋白质组研究，用于在其他复合体中发现QTL 人类疾病。在这个项目中，我们将识别影响呼吸道高反应性(AHR)和 通过调查36个近交系小鼠的AHR和COPD，香烟烟雾诱导的小鼠慢性阻塞性肺疾病。 随后，我们将在二氧化硅计算中使用QTL分析来检测包含AHR和AHR的区域 COPD基因，特别是那些影响AHR和COPD的基因组区域。基于 在计算QTL分析中，接下来我们将选择亲本品系并进行实际的QTL杂交。 最后，一旦QTL被确定，我们将重点关注那些位于同源位置的QTL 利用多种数据挖掘技术和附加遗传杂交技术寻找候选QTL 基因。这些候选基因将在项目1(哮喘)中测试人类群体的关联性。 和2(慢性阻塞性肺病)，从而结合了人类和小鼠系统的优势。我们预计会提供 向科学界提供的这种菌株调查信息将刺激对以下模型的进一步研究 哮喘和COPD，并将加速哮喘和COPD基因的发现。因为我的性欲 包括哮喘在内的常见疾病的QTL在人和鼠之间的同源性 假设同时使用动物模型和人类进行基因发现的协调方法 人口将更具成本效益和更成功，并将为 继续开展哮喘和慢性阻塞性肺病的遗传学研究。

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.