Poly-omic Study of Asthma Exacerbations in Diverse Populations

哮喘急性发作导致相当多的疾病发病率，占所有哮喘的近一半- 相关费用。此外，某些人口群体，如非裔美国人和拉丁美洲人， 这些并发症与哮喘相关急诊就诊率不成比例， 住院率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急性发作的个体具有遗传易感性：1）既往事件 是未来病情加重的最强预测因素之一，2）遗传祖先已被证明是一个 急性加重的独立预测因子，以及3）急性加重遗传力的计算表明，30-55%的 这一性状的变异可能归因于加性遗传效应。然而，我们目前没有 遗传生物标志物，可用于临床可靠地预测哮喘急性发作的易感性。等 如果这些措施能导致早期识别和适当治疗， 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） 我们的哮喘转化基因组学合作组织（ATGC）正在生成的数据， NHLBI的Trans-Omic精准医学（TOPMed）计划。ATGC包括8项队列研究， 10，819例哮喘患者（7，530例非裔美国人和3，081例拉丁美洲人）。的遗传流行病学 具有WGS数据的哥斯达黎加哮喘患者（CRA）队列也将参与研究（n= 1，765）。在目标1中，我们将重点 通过以下子目标对哮喘急性发作的基因组学进行研究：a） 使用WGS数据的加重遗传性; B）使用混合作图来鉴定可能 c）使用现有的风险变量精细映射上述区域的风险变量 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 使用可用的前瞻性临床数据，评估其与未来加重的相关性。在目标2中， 将聚焦于哮喘急性发作的转录组学。即，我们将使用推导出的RNA序列数据 从全血转录组中鉴定其表达与严重急性加重相关的基因 (Aim 2a），并且我们将鉴定其表达与2a）中鉴定的变体的基因型相关的基因。 目标1（目标2b）。目标3将集中于哮喘急性发作的蛋白质组学。库存血清将用于 从表型极端（即，从个人收集的血清之前， 严重急性发作与来自未经历急性发作的哮喘个体的血清）。利用大众 光谱法，我们将广泛地评估血清中在这些组之间差异表达的蛋白质（即，一个 非靶向蛋白质组学方法），我们将使用从基因组学，转录组学， 和非靶向蛋白质组学分析以评估特定蛋白质（即，靶向蛋白质组学方法）， 在表型极端的另外的个体组中的表达差异。

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.