Poly-omic Study of Asthma Exacerbations in Diverse Populations

不同人群哮喘加重的多组学研究

• 批准号: 10337191
• 负责人: Keoki Williams
• 金额: $ 73.95万
• 依托单位: HENRY FORD HEALTH SYSTEM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10337191
• 关键词: Accounting; Address; African American; African American population; Age; Allergic inflammation; American; Asthma; Blood; Candidate Disease Gene; Caring; Cessation of life; Child; Clinical; Clinical Data; Cohort Studies; Costa Rica; DNA analysis; Data; Detection; Disease; Dizygotic Twins; Emergency department visit; Ethnic Origin; European; Event; Exhalation; Expenditure; Facilities and Administrative Costs; Future; Genes; Genetic; Genetic Markers; Genetic Predisposition to Disease; Genetic Structures; Genetic study; Genomics; Genotype; Glean; Helper-Inducer T-Lymphocyte; Heritability; Hospitalization; IgE; Individual; Investigation; Latino; Latino Population; Life; Maps; Mass Spectrum Analysis; Measures; Medical; Minority; Minority Groups; Monozygotic twins; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Nitric Oxide; Pathway interactions; Patient Self-Report; Patients; Personal Financing; Pharmaceutical Preparations; Phenotype; Population Group; Population Heterogeneity; Predisposition; Prevalence; Protein Analysis; Proteins; Proteome; Proteomics; Quality of life; Quantitative Trait Loci; RNA Sequences; RNA analysis; Recording of previous events; Research Design; Risk; Sampling; Schools; Serum; Serum Proteins; Surveys; Time; Trans-Omics for Precision Medicine; Variant; Whole Blood; Work; admixture mapping; asthma exacerbation; cohort; cost; differential expression; economic impact; experience; gene product; genetic epidemiology; genetic risk factor; genetic variant; genome wide association study; high risk; periostin; programs; prospective; racial disparity; respiratory; risk variant; societal costs; trait; transcriptome; transcriptomics; translational genetics; translational genomics; whole genome

Asthma exacerbations contribute to considerable disease morbidity and account for nearly half of all asthma- related costs. Moreover, certain population groups, such as African American and Latino individuals, suffer disproportionately from these complications with rates of asthma-related emergency department visits, hospitalizations, and deaths nearly 3-5 times higher than those of European Americans. There are multiple reasons to believe that individuals who suffer severe exacerbations are genetically predisposed: 1) prior events are among the strongest predictors of future exacerbations, 2) genetic ancestry has been shown to be an independent predictor of exacerbations, and 3) calculations of exacerbation heritability suggest that 30-55% of this trait’s variance may be attributed to additive genetic effects. Nevertheless, we do not currently have genetic biomarkers that can be used clinically to reliably predict susceptibility to asthma exacerbations. Such measures could transform asthma care if they resulted in the early recognition and appropriate treatment of individuals at risk. In this application, we will utilize the enormous amount of whole genome sequence (WGS) data that is being generated on our Asthma Translational Genomics Collaborative (ATGC) as part of the NHLBI’s Trans-Omic Precision Medicine (TOPMed) Program. The ATGC comprises 8 cohort studies and 10,819 patients with asthma (7,530 African Americans and 3,081 Latinos). The Genetic Epidemiology of Asthma in Costa Rica (CRA) cohort with its WGS data will also participate (n=1,765). In Aim 1, we will focus on the genomics of asthma exacerbations through the following sub-aims: a) refine our estimates of exacerbation heritability using a WGS data; b) use admixture mapping to identify chromosomal regions likely to harbor risk variants for exacerbations; c) fine-map the aforementioned regions for risk variants using available WGS data; d) replicate these associations in other ATGC cohorts and in the CRA cohort; and e) assess variants for their association with future exacerbations using available prospective clinical data. In Aim 2, we will focus on the transcriptomics of asthma exacerbations. Namely, we will use RNA-sequence data derived from the whole blood transcriptome to identify genes whose expression associated with severe exacerbations (Aim 2a), and we will identify genes whose expression is associated with the genotype of variants identified in Aim 1 (Aim 2b). Aim 3 will focus on the proteomics of asthma exacerbations. Banked serum will be used to assess the proteome of individuals from phenotype extremes (i.e., serum collected from individuals prior to a severe exacerbation vs. serum from individuals with asthma who don’t experience exacerbations). Using mass spectrometry, we will broadly assess serum for proteins differentially expressed between these groups (i.e., an untargeted proteomic approach), and we will use the information gleaned from the genomic, transcriptomic, and untargeted proteomic analyses to assess specific proteins (i.e., a targeted proteomic approach) for expression differences in additional groups of individuals at phenotype extremes.

哮喘加重导致相当大的疾病发病率，占所有哮喘的近一半- 相关成本。此外，某些人口群体，如非洲裔美国人和拉丁裔个人，受到 这些并发症与哮喘相关的急诊科就诊率不成比例， 住院人数和死亡率几乎是欧洲裔美国人的3-5倍。有多个 有理由相信，患有严重恶化的人是遗传倾向的：1)既往事件 是未来病情恶化的最强有力的预测因素之一，2)基因血统已被证明是一种 恶化的独立预测因素，以及3)恶化遗传度的计算表明，30%-55%的 这一性状的变异可能归因于加性遗传效应。然而，我们目前还没有 基因生物标记物可用于临床可靠地预测哮喘恶化的易感性。是这样的 措施可以改变哮喘护理，如果它们导致早期认识到和适当治疗 处于危险中的个人。在这个应用中，我们将利用大量的全基因组序列(WGS) 在我们的哮喘转换基因组学协作(ATGC)上生成的数据是 NHLBI的跨基因组精密医学(TOPMed)计划。ATGC包括8项队列研究和 10,819名哮喘患者(7,530名非洲裔美国人和3,081名拉丁裔)。血吸虫病的遗传流行病学研究 哥斯达黎加哮喘(CRA)队列及其WGS数据也将参与(n=1,765)。在目标1中，我们将专注于 通过以下次级目标对哮喘恶化的基因组学进行研究：a)完善我们对 使用WGS数据的恶化遗传性；b)使用混合作图来识别可能 用于恶化的港湾风险变量；c)使用可用的 WGS数据；d)在其他ATGC队列和CRA队列中复制这些关联；以及e)评估 使用现有的前瞻性临床数据，研究它们与未来病情恶化的关联。在目标2中，我们 将重点放在哮喘恶化的转录组学上。也就是说，我们将使用推导出的RNA序列数据 从全血转录组中识别其表达与严重恶化相关的基因 (目标2a)，我们将确定其表达与在 目标1(目标2b)。AIM 3将重点研究哮喘恶化的蛋白质组学。储存的血清将用于 评估来自表型极端的个体的蛋白质组(即，从个体收集的血清 严重加重与未经历加重的哮喘患者的血清相比)。使用体量 ，我们将广泛评估血清中这些组之间差异表达的蛋白质(即， 非靶向蛋白质组方法)，我们将使用从基因组、转录组、 和非靶向蛋白质组分析，以评估特定蛋白质(即，靶向蛋白质组学方法) 在表型极端的其他个体群体中的表达差异。