Epigenome-wide variations and socio-environmental exposures in African American asthmatic children

非裔美国哮喘儿童的表观基因组变异和社会环境暴露

• 批准号: 10494245
• 负责人: Tesfaye B. Mersha
• 金额: $ 67.69万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494245
• 关键词: Address; Affect; African American; African American population; Age; Air Pollution; Algorithms; Allergens; American; Asthma; Bioinformatics; Biological; Biometry; Caring; Cells; Child; Childhood; Childhood Asthma; Clinic; Clinical; Clinical Sciences; Communities; Complex; DNA Methylation; DNA Modification Process; DNA methylation profiling; Data; Disease; Environmental Exposure; Epigenetic Process; European; Exposure to; Gene Frequency; Genetic; Genetic Predisposition to Disease; Genome; Genotype; Geography; Goals; Green space; Hypersensitivity; Immune; Incidence; Individual; Intervention; Link; Maps; Measures; Mediating; Mediation; Methylation; Minority; Modeling; Neighborhoods; Ohio; Pathogenesis; Pediatrics; Phenotype; Public Health; Research; Research Personnel; Risk; Risk Factors; Sampling; Science; Site; Social Characteristics; Social Environment; Statistical Methods; Stress; Study models; Sum; Surrogate Markers; System; Testing; Time; Tobacco smoke; United States; United States National Institutes of Health; Variant; Violence; Work; asthma model; asthmatic; base; biobank; case control; clinical predictors; clinical risk; cohort; deprivation; epigenetic variation; epigenome; epigenome-wide association studies; gene function; genome wide methylation; genome-wide; improved; indexing; innovation; insight; methylation pattern; multi-ethnic; non-genetic; novel; population based; predictive marker; predictive modeling; programs; psychosocial; racial disparity; repository; risk prediction; risk prediction model; risk stratification; screening; sex; social; social epidemiology; socioeconomics

ABSTRACT Asthma is a major public health problem in the United States, affecting 11 million children. Despite advances in asthma care, African Americans (AAs) are 4 times more likely to be hospitalized and 5 times more likely to die from asthma than European Americans (EAs). Several factors could be responsible for the observed asthma racial disparities including genetic and non-genetic factors. While epigenetics appear to serve as a critical biological switch between genetic vulnerability and socio-environmental exposures, limited studies are available that directly map the socio- environmental exposures with asthmatic epigenome/genome information. In addition, current approach do not leverage existing geospatial data such as environmental exposure and neighborhood socioeconomic conditions to improve asthma risk prediction. In this proposal, we will utilize comprehensive geocoding algorithms, novel statistical methods to integrate social, clinical, environmental, genetic, and epigenetic data into a composite score for asthma risk stratification and prediction. The overall objective of this research is to conduct genome-wide Methyl-Seq analysis and leverage existing well-phenotyped AA pediatric asthma cohort with extensive socio-environmental exposures and ancestry-tailored multi-ethnic genotyping array (MEGA) data from Cincinnati Pediatrics Repository to accurately determine and develop ancestry- specific asthma risk stratification and prediction models. The objective of this application is to undertake an epigenome-wide association study (EWAS), incorporating geocoded neighborhood- and individual-level socio-environmental predictors, and novel analytical strategies to create a composite risk score incorporating methylation risk score (MRS), ancestry, environmental exposures and social characteristics to predict asthma. We will accomplish these objectives through the following Specific Aims: 1) Develop an ancestry-specific methylation risk score (MRS) for asthma and test its association with socio-environmental exposures contributing to asthma risk. 2) Determine the mediation effects of MRS between genetic ancestry and asthma risk. 3) Develop a multivariable risk predictive model for asthma incorporating MRS, genetic ancestry, clinical, and socio-environmental risk factors. The proposed research is innovative because this will be the first time a MRS approach will be used to develop a population-based risk profile in asthmatics. The study will provide insights in the use of risk stratification for screening and targeted interventions. This work is significant because it can serve as a model to study the composite effect of MRS, ancestry, socio-environmental, and clinical risk factors on racial disparities in other well-documented common complex diseases beyond asthma.

抽象的 哮喘是美国的主要公共卫生问题，影响了1100万儿童。尽管 哮喘护理的进步，非裔美国人（AAS）住院的可能性高4倍，并且 死于哮喘的可能性是欧洲人（EA）的5倍。可能有几个因素 负责观察到的哮喘种族差异，包括遗传和非遗传因素。 尽管表观遗传学似乎是遗传脆弱性和 社会环境暴露，有限的研究可直接映射社会 具有哮喘表观基因组/基因组信息的环境暴露。另外，当前 方法不利用现有的地理空间数据，例如环境暴露和 邻里社会经济条件改善哮喘风险预测。在这个建议中，我们 是否会利用全面的地理编码算法，新颖的统计方法来整合社会， 临床，环境，遗传和表观遗传学数据，哮喘风险的综合评分 分层和预测。这项研究的总体目的是进行全基因组 甲基序列分析并利用现有的良好型AA儿科哮喘同类 广泛的社会环境暴露和祖先的多种族基因分型阵列 （Mega）辛辛那提儿科存储库的数据准确确定和发展祖先 - 特定的哮喘风险分层和预测模型。该应用的目的是 进行一项表观基因组的协会研究（EWAS），结合了地理编码的邻里 以及个人级别的社会环境预测指标，以及创建一个新的分析策略 综合甲基化风险评分（MRS），祖先，环境的综合风险评分 暴露和社会特征以预测哮喘。我们将实现这些目标 通过以下特定目的：1）发展祖先特异性的甲基化风险评分（MRS） 哮喘并测试其与社会环境暴露的关联，导致哮喘 风险。 2）确定MRS在遗传血统和哮喘风险之间的介导作用。 3） 为哮喘纳入MRS，遗传血统，开发多变量的风险预测模型， 临床和社会环境风险因素。拟议的研究具有创新性，因为这 将是第一次使用MRS方法来开发基于人群的风险状况 哮喘患者。该研究将提供有关使用风险分层进行筛查和的见解 有针对性的干预措施。这项工作很重要，因为它可以作为研究 MRS，祖先，社会环境和临床风险因素对种族的综合作用 哮喘以外的其他有据可查的常见复杂疾病的差异。