Integrating polygenic and environmental risk factors for asthma in diverse populations

项目摘要/摘要 哮喘是一种复杂的炎症性呼吸道疾病，全世界数以百万计的人健康状况良好。 差距。哮喘的相对较高的遗传率表明，遗传因素在很大程度上有助于 哮喘风险。仅在过去的几年里，我们对这些遗传风险因素的了解就迅速扩大， 大规模全基因组关联研究(GWAS)共同识别数百个共同的基因 与哮喘相关的变异体。来自这些研究的丰富信息表明，遗传学 可能会改进哮喘风险预测模型，这可能在干预、预防和定向方面有用 疾病管理战略。然而，现有的模型目前具有适度的临床潜力，主要是 仅从少数几个个人、家庭和环境变量中得出结论。因此，我们建议适用于 评估(A)全基因组遗传风险的量化指标和(B)的效用的统计方法 累积的环境风险暴露，单独和组合，用于预测哮喘病例状态 不同祖先的种群。具体地说，在目标1中，我们将得出哮喘的多基因风险评分(PR) 来自迄今为止规模最大、最多样化的哮喘GWA。在目标2中，我们将开发利用 来自相关疾病和特征的遗传信息。在目标3中，我们将利用表观范围的方法来 从丰富的表型资源中选择和整理与哮喘风险相关的环境暴露， 最终目标是建立一个综合风险模型，考虑遗传和环境风险因素 不同的人群。这些目标将提供对这些全面的 模型，以及用于构建人群中哮喘的PR和环境风险评分的工具 在基因组研究中代表性不足。总之，这些分析将有助于开发更准确的 基于人群的哮喘风险预测工具。拟议的研究将为研究金协会提供一个 在哈佛博士项目、麻省理工学院和哈佛大学的丰富培训经验，以及 麻省总医院。在她的赞助商和合作者的指导下，她将实现几个 培训目标将为她作为独立研究人员的发展奠定基础，并最终成为 首席调查员。

Project Summary/Abstract Asthma is a complex, inflammatory airway disease that affects millions of people worldwide with striking health disparities. The relatively high heritability of asthma suggests that genetic factors contribute substantially to asthma risk. Our understanding of these genetic risk factors has rapidly expanded in just the past few years, with large-scale genome-wide association studies (GWAS) collectively identifying hundreds of common genetic variants associated with asthma. The wealth of information coming from these studies suggests that genetics may improve asthma risk prediction models, which may be useful in intervention, prevention, and targeted disease management strategies. However, existing models currently have modest clinical potential and primarily draw from only a handful of personal, family, and environmental variables. Therefore, we propose to apply statistical approaches to assess the utility of (a) quantitative indexes of genome-wide genetic risk and (b) aggregated environmental risk exposures, individually and combined, for predicting asthma case status in populations of different ancestries. Specifically, in Aim 1, we will derive polygenic risk scores (PRS) for asthma from the largest and most diverse GWAS of asthma to date. In Aim 2, we will develop asthma PRS that leverage genetic information from correlated diseases and traits. In Aim 3, we will utilize phenome-wide approaches to select and collate environmental exposures relevant to asthma risk from a rich phenotypic resource, with the ultimate goal of building an integrated risk model that considers genetic and environmental risk factors across diverse populations. These Aims will provide insights into the predictive potential of these comprehensive models, as well as tools for constructing PRS and environmental risk scores for asthma in populations underrepresented in genomic studies. Together, the analyses will facilitate the development of more accurate population-based risk prediction tools for asthma. The proposed research will provide the fellowship PI with a rich training experience in the Harvard PhD Program, the Broad Institute of MIT and Harvard, and the Massachusetts General Hospital. With the mentorship of her sponsors and collaborators, she will achieve several training goals that will lay the foundation for her development as an independent researcher and ultimately, a principal investigator.