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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

基本信息

批准号：
10457251
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
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10457251
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 Environmental Risk Factor Epidemiology Ethnic Origin Etiology Genetic Genetic Heterogeneity Genetic Risk Genomics Genotype Genotype-Tissue Expression Project Goals Heritability Heterogeneity Individual Institutes 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 Translational Research Work asthma exacerbation biobank cell type chronic respiratory disease clinical heterogeneity clinical subtypes clinically significant cohort comorbidity disease classification functional genomics gene environment interaction genetic association 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 tool trait translational medicine

项目摘要

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/文摘