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

描述哮喘的个体和亚型特异性危险因素和治疗方法

• 批准号: 10684675
• 负责人: Andrew Dahl
• 金额: $ 17.3万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10684675
• 关键词: Address; Affect; Age of Onset; Asthma; Basic Science; Biological; Biology; Child; Chronic; Clinical; Clinical Data; Clinical Treatment; Collaborations; Communities; Complex; Data; Disadvantaged; Disease; Disparity population; Environmental Risk Factor; Epidemiology; Ethnic Origin; Etiology; Genetic; Genetic Heterogeneity; Genetic Risk; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Heritability; Heterogeneity; Individual; Inequity; Intervention; Learning; Link; Machine Learning; Medicine; Mentorship; Methods; Modeling; Molecular; Pathology; Pathway interactions; Persons; Phenotype; Play; Population; Precision therapeutics; Predisposition; Proxy; Pulmonology; Research; Research Personnel; Risk; Risk Factors; Role; Sample Size; Smoking; Statistical Methods; Structure; Testing; Tissues; Training; Translational Research; Work; biobank; cell type; chronic respiratory disease; clinical heterogeneity; clinical subtypes; clinically significant; cohort; comorbidity; disease classification; functional genomics; gene environment interaction; genetic association; genetic predictors; genome-wide; genomic data; improved; innovation; machine learning method; machine learning model; neutrophil; novel; optimal treatments; polygenic risk score; precision medicine; professor; programs; respiratory; sex; statistics; theories; tool; trait; translational medicine

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.

项目总结/摘要 哮喘是一种慢性呼吸道疾病，影响全球约3.4亿人，但其因果生物学， 环境风险、关键细胞类型和最佳治疗方法仍然没有得到充分表征。这种困难部分是 由于临床异质性，因为不同的风险因素导致不同的人患哮喘。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 它们是新生的、模糊的，尚未实现其在科学研究和精确测量方面的潜在效用。 治疗。特别是，遗传学还没有被充分利用哮喘亚型，虽然它有一个独特的， 能够评估亚型的因果生物学意义，并能够识别关键细胞类型;相反， 亚型研究易受与哮喘生物学不直接相关的巧合亚型的影响。 此外，以前的研究使用的基本方法，易于偏见和低功率。解决这些 局限性，我们将开发一个强大而强大的框架，以查明和遗传特征 哮喘亚型，我们将广泛应用于大，深表型，和不同的队列。我们的研究 将确定新的亚型及其人口统计学，基因组学，细胞学和临床病因学，这可能表明 精确的治疗和提高权力和解释基础和翻译研究。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 自由发布的方法将为复杂的特征分型提供广泛的模板。 为了实现这些目标，我们将研究四个大型生物库，这些生物库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， 和细胞类型特异性分子机制。我们将在我们之前的机器学习工具的基础上， 识别亚型，量化其遗传和临床意义，并推断其主要细胞类型。我们 方法是独特的，通过校正混杂的群体结构，这对遗传分型至关重要： 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疾病分类学。 该提案的一个关键目标是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，以及在医学和翻译研究所UChicago部门的教学课程 药这种再培训将通过使PI能够深入联系，最大限度地发挥我们研究的生物医学影响 定量结果的核心方面的哮喘病理学，并将建立PI作为一个独立的哮喘 研究人员谁可以最佳地应用他的统计和机器学习背景，以解决基本的 生物医学障碍