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: 10662490
• 负责人: Tesfaye B. Mersha
• 金额: $ 70.5万
• 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/10662490
• 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 analysis; DNA methylation profiling; Data; Disease; Economic Conditions; Environmental Exposure; Epigenetic Process; European; Exposure to; Gene Frequency; Genetic; Genetic Predisposition to Disease; Genome; Genotype; Geography; Goals; Green space; Hospitalization; 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; biobank; 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 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.

摘要

项目成果

Multilevel Analysis of Racial and Ethnic Disparities in COVID-19 Hospitalization among Children with Allergies.

• DOI: 10.1513/annalsats.202207-580oc
• 发表时间: 2023-06
• 期刊: Annals of the American Thoracic Society
• 影响因子: 8.3

Racial differences in emergency encounters related to pediatric allergic diseases during the COVID-19 pandemic.

• DOI: 10.1016/j.jaip.2023.04.007
• 发表时间: 2023-07
• 期刊: JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE
• 影响因子: 9.4
• 作者: Correa-Agudelo, Esteban;Beck, Andrew F.;Mersha, Tesfaye B.
• 通讯作者: Mersha, Tesfaye B.

Understanding racial disparities in childhood asthma using individual- and neighborhood-level risk factors.

• DOI: 10.1016/j.jaci.2022.07.024
• 发表时间: 2022-12
• 期刊: JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY
• 影响因子: 14.2
• 作者: Correa-Agudelo, Esteban;Ding, Lili;Beck, Andrew F.;Brokamp, Cole;Altaye, Mekibib;Kahn, Robert S.;Mersha, Tesfaye B.
• 通讯作者: Mersha, Tesfaye B.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.

使用共表达基因模块和机器学习方法的哮喘风险预测模型的开发和验证。

• DOI: 10.1038/s41598-023-35866-2
• 发表时间: 2023-07-12
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Dessie, Eskezeia Y.;Gautam, Yadu;Ding, Lili;Altaye, Mekibib;Beyene, Joseph;Mersha, Tesfaye B.
• 通讯作者: Mersha, Tesfaye B.

Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function.

• DOI: 10.1186/s13148-023-01611-9
• 发表时间: 2024-01-20
• 期刊: Clinical epigenetics
• 影响因子: 5.7