Characterizing individual- and subtype-specific risk factors and treatments in asthma

项目摘要/摘要 哮喘是一种慢性呼吸道疾病，全世界约有3.4亿人受到影响，但其病因生物学， 环境风险、关键细胞类型和最佳治疗方法仍未得到充分说明。这种困难的部分原因是 由于临床上的异质性，不同的危险因素导致不同人群的哮喘。哮喘亚型研究 已经开始揭示这种异质性的重要方面。然而，哮喘亚型仍然存在 初露头角，模棱两可，尚未认识到它们在科学研究和精确度方面的潜在用途 治疗。特别是，遗传学还没有被充分利用于哮喘亚型，尽管它有独特的 评估亚型的因果生物学意义并识别关键细胞类型的能力；相反，优先 亚型研究容易受到与哮喘生物学没有直接关系的巧合亚型的影响。 此外，以前的研究使用的是容易产生偏差和低功耗的基本方法。要解决这些问题 限制，我们将开发一个强大和强大的框架，以准确定位和基因特征 哮喘亚型，我们将在大规模、深表型和多样化的队列中广泛应用。我们的研究 将确定新的亚型及其人口统计、基因组、细胞和临床病因，这可能表明 精准处理，提高基础研究和翻译研究的能力和解释力。我们的工作将 改善哮喘的遗传预测，特别是在研究不足的人群中。最后，我们的方法和 免费发布的方法将为复杂的特征亚型提供一个广泛的模板。 为了实现这些目标，我们将研究四个大型生物库，它们提供了前所未有的样本量，临床 深度和人口多样性。我们将使用功能基因组学将遗传异质性与因果关系联系起来 和特定细胞类型的分子机制。我们将在以前的机器学习工具的基础上进行构建 确定亚型，量化它们的遗传和临床意义，并推断它们的主要细胞类型。我们的 通过校正混淆的种群结构，方法是独一无二的，这对基因亚型至关重要： 虚假的遗传关联导致先前的研究提出了严重偏见和倒退的病因学。 这项建议的一个关键目标是PI在哮喘生物学、肺病学和功能基因组学方面的再培训。这 将在Carole Ober、Julian Solway、Yoav Gilad和Matthew教授的密切指导下实现 斯蒂芬斯，以及芝加哥大学医学院和翻译研究所的授课课程 医学。这种再培训将最大限度地发挥我们研究的生物医学影响，使PI能够深入联系 哮喘病理的核心环节的量化结果，并将PI确立为独立的哮喘 能够以最佳方式应用他的统计学和机器学习背景来解决基本问题的研究人员 生物医学上的障碍。

Project Summary/Abstract Asthma is a chronic respiratory disease affecting about 340 million people worldwide, yet its causal biology, environmental risks, key cell types, and optimal treatments remain under-characterized. This difficulty is partly due to clinical heterogeneity, as different risk factors drive asthma for different people. Asthma subtype studies have already begun to reveal important aspects of this heterogeneity. However, asthma subtypes remain nascent and ambiguous and have not yet realized their potential utility for scientific studies and precision treatments. In particular, genetics has not been fully exploited for asthma subtyping, though it has a unique ability to assess the causal biological significance of subtypes and can identify key cell types; conversely, prior subtyping studies are susceptible to coincidental subtypes that are not directly relevant to asthma biology. Furthermore, prior studies have used basic methods which are liable to bias and low power. To address these limitations, we will develop a powerful and robust framework to pinpoint and genetically characterize asthma subtypes, and we will broadly apply it in large, deeply phenotyped, and diverse cohorts. Our study will identify novel subtypes and their demographic, genomic, cellular, and clinical etiologies, which can suggest precision treatments and improve power and interpretation in basic and translational research. Our work will improve genetic prediction of asthma, particularly in understudied populations. Finally, our approach and freely released methods will provide a broad template for complex trait subtyping. To accomplish these goals, we will study four large biobanks, which offer unprecedented sample size, clinical depth, and demographic diversity. We will use functional genomics to link genetic heterogeneity to causal and cell type-specific molecular mechanisms. We will build on our prior machine learning tools to identify subtypes, quantify their genetic and clinical significance, and infer their dominant cell types. Our methods are unique by correcting for confounding population structure, which is crucial for genetic subtyping: spurious genetic associations led prior studies to propose severely biased and regressive nosology. A key goal of this proposal is the PI’s retraining in asthma biology, pulmonology, and functional genomics. This will be achieved by close mentorship from Professors Carole Ober, Julian Solway, Yoav Gilad, and Matthew Stephens, as well as didactic courses in the UChicago Department of Medicine and Institute for Translational Medicine. This retraining will maximize the biomedical impact of our study by enabling the PI to deeply connect quantitative results to core facets of asthma pathology and will establish the PI as an independent asthma researcher who can optimally apply his statistics and machine learning background to tackle essential biomedical hurdles.