INTEGRATIVE GENOMICS APPROACHES TO MODEL THE GENETIC ARCHITECTURE OF ASTHMA

描述（由申请人提供）：哮喘是一种慢性呼吸道疾病，影响超过2000万美国人和全球3亿人，由多种遗传和环境因素的复杂相互作用引起。许多研究已经寻找了导致哮喘易感性的个体遗传变异，但尚未彻底了解哮喘的遗传基础。我们的主要假设是，通过考虑多个基因并结合多个基因组数据来源，包括人类哮喘和小鼠气道高反应性（AHR）数据，可以更好地理解哮喘易感性的遗传结构。这一假设将通过具体的目标来解决，我们将：（1）通过将小鼠近交系中与AHR相关的区域映射到人类全基因组关联数据来鉴定使人类易患哮喘和调节人类AHR的遗传变体，（2）使用基因表达数据、已知的蛋白质-蛋白质相互作用和已知的功能途径来增强对人类中哮喘和AHR遗传变体的搜索，以及（3）整合个体人类遗传变异以创建哮喘的多变量预测模型。通过特异性目的1和2鉴定的新变体将通过在独立的哮喘人群中复制来验证。在特定目标3中创建的预测模型将通过在独立的哮喘人群中的复制和预测来验证。通过完成这些具体目标，我们希望在哮喘遗传学的综合模型的开发方面取得进展，特别是以气道反应性增加为特征的哮喘。 公共卫生相关性：通过完成该提案，我们希望识别调节哮喘风险的遗传变异。识别这些变异可以提供生物学见解，最终可能导致更好地了解哮喘。此外，我们还将建立一个哮喘的预测模型，这可能会导致开发一种临床预后测试，以确定谁有患哮喘的风险。

DESCRIPTION (provided by applicant): Asthma, a chronic respiratory disease affecting over 20 million Americans and 300 million people worldwide, results from the complex interaction of multiple genetic and environmental factors. Many studies have searched for individual genetic variants that contribute to asthma susceptibility, but a thorough understanding of the genetic basis of asthma has not been achieved. Our main hypothesis is that the genetic architecture underlying asthma susceptibility can be better understood by considering multiple genes and incorporating multiple sources of genomic data, including human asthma and mouse airways hyperresponsiveness (AHR) data. This hypothesis will be addressed via specific aims in which we will: (1) identify genetic variants that predispose humans to asthma and modulate human AHR by mapping regions associated with AHR in inbred strains of mice to human genome-wide association data, (2) use gene expression data, known protein-protein interactions, and known functional pathways to enhance the search for asthma and AHR genetic variants in humans, and (3) integrate individual human genetic variants to create a multivariate predictive model of asthma. Novel variants identified by Specific Aims 1 and 2 will be validated by replication in independent human populations of asthmatics. The predictive model created in Specific Aim 3 will be validated through replication and prediction in independent human populations of asthmatics. By completing the specific aims, we hope to make progress towards the development of a comprehensive model of the genetics of asthma, particularly of asthma characterized by increased airways responsiveness. PUBLIC HEALTH RELEVANCE: By completing this proposal, we hope to identify genetic variants that modulate asthma risk. Identifying such variants could provide biological insights that may eventually lead to a better understanding of asthma. Additionally, we will create a predictive model of asthma, which could potentially lead to the development of a clinical prognostic test of who is at risk for developing asthma.