Epigenome Association Study of Food Allergy

食物过敏的表观基因组关联研究

• 批准号: 8327887
• 负责人: XIAOBIN WANG
• 金额: $ 6.17万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8327887
• 关键词: Address; Adult; Affect; Age; Anaphylaxis; Area; Biological; Blood specimen; Cattle; Cell Differentiation process; Chicago; Child; Clinical; Clinical Data; Code; Collection; Complex; Coupled; Cytokine Gene; DNA; DNA Methylation; DNA Sequence; DNA analysis; Data; Databases; Development; Diagnosis; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Epigenetic Process; Etiology; Food; Food Hypersensitivity; Funding; Future; Gender; Gene Expression; Genetic; Hypersensitivity; ICD-9; IgE; Investigation; Laboratories; Lead; Maps; Mediating; Milk Hypersensitivity; National Institute of Allergy and Infectious Disease; Outcome; Participant; Pattern; Phenotype; Physicians; Play; Population; Population Study; Prevention; Public Health; Questionnaires; Reaction; Recording of previous events; Resources; Risk; Role; Sample Size; Severities; Source; Testing; Venous blood sampling; airborne allergen; base; cohort; cost; data integration; design; epigenomics; genetic variant; genome wide association study; genome-wide; high throughput technology; insight; medical food; novel; prevent; repository; skin prick test; trait

DESCRIPTION (provided by applicant): Food allergy (FA) is a condition caused by an immunoglobulin (Ig) E-mediated hypersensitivity reaction to food. FA affects approximately 5-8% of children and 1-4% of adults and is a growing clinical and public health problem in the U.S. and worldwide. The major obstacle in preventing and treating FA has been our incomplete understanding of its etiology and biological mechanisms. Available evidence suggests that FA is a complex trait determined by multiple environmental and genetic factors. This proposal is built on our ongoing investigation of environmental and genetic factors of FA, and is designed to explore a novel area: epigenomic associations with FA. Epigenetics is defined as the study of heritable changes in gene expression that occur without changing DNA sequences. Epigenomics is the study of epigenetics on a genome-wide scale. While there is no epigenetic or epigenomic data available for FA, epigenetics appears to play an important role in Th cell differentiation and cytokine gene expression. We hypothesize that an integration of clinical, environmental, genetic and epigenetic data represents a promising approach to understand the etiology and biological mechanisms of FA, in particular, this may provide mechanistic insight into how genetic variants and environmental exposures can modify the risk, severity and long-term outcome of FA. The central focus of this proposal is to perform genome-wide mapping to identify epigenetic alterations in FA, and to integrate such data with phenotypic, environmental and genetic data. We will utilize the extensive resources of the Chicago Cohort to address the following specific aims: Aim 1A: We will conduct epigenomic mapping of 600 white children: 300 cow milk allergy (CMA) cases and 300 non-allergic controls from the Chicago Cohort, using the Illumina HumanMethylation27 DNA Analysis BeadChip (Infinium). We will test the hypothesis that epigenetic alterations in venous blood samples are associated with the risk of CMA, after controlling for potentially important covariates and confounders. Aim 1B (Exploratory): We will compare epigenomic patterns between two groups of CMA cases: without vs. with history of anaphylaxis. We will test the hypothesis that epigenomic patterns differ by severity of CMA, after controlling for potentially important covariates and confounders. Aim 2 (Exploratory): We will integrate epigenomic data with our extensive phenotypic, environmental and SNPs data available in the Chicago Cohort, which allow us to simultaneously evaluate the roles of environment, genetics, and epigenomics as well as their interactions in CMA. The findings from this proposed study may transform our understanding of the etiology and biological mechanisms of FA. Unlike genetic variants, epigenetic alternations are potentially reversible. A better understanding of epigenetic mechanisms may lead to the development of new paradigms for prediction, prevention and treatment of FA. Food allergy (FA) is a growing clinical and public health problem in the U.S. and worldwide. The major obstacle in preventing and treating FA has been our incomplete understanding of its etiology and biological mechanisms. This proposal aims to perform genome-wide mapping to identify epigenomic alterations in FA, and to integrate such data with clinical, environmental and genetic data. The study findings should have important implications for developing strategies in prediction, prevention and treatment of FA.

描述（由申请方提供）：食物过敏（FA）是由免疫球蛋白（IG）E介导的食物超敏反应引起的疾病。FA影响大约5-8%的儿童和1-4%的成人，在美国和世界范围内是一个日益严重的临床和公共卫生问题。目前防治FA的主要障碍是对其病因学和生物学机制的认识不足。现有证据表明，FA是一个复杂的性状，由多种环境和遗传因素决定。这个建议是建立在我们正在进行的调查的环境和遗传因素的FA，旨在探索一个新的领域：表观基因组协会与FA。表观遗传学被定义为研究在不改变DNA序列的情况下发生的基因表达的可遗传变化。表观基因组学是在全基因组范围内研究表观遗传学。虽然没有表观遗传学或表观基因组数据可用于FA，表观遗传学似乎在Th细胞分化和细胞因子基因表达中发挥重要作用。我们假设，临床，环境，遗传和表观遗传数据的整合代表了一种有前途的方法来了解FA的病因学和生物学机制，特别是，这可能会提供遗传变异和环境暴露如何改变FA的风险，严重程度和长期结局的机制见解。这项建议的中心重点是进行全基因组定位，以确定表观遗传改变FA，并整合这些数据与表型，环境和遗传数据。我们将利用芝加哥队列的广泛资源来解决以下具体目标：目标1A：我们将使用Illumina HumanMethylation 27 DNA Analysis BeadChip（Infinium）对来自芝加哥队列的600名白色儿童（300名牛奶过敏（CMA）病例和300名非过敏对照）进行表观基因组作图。在控制了潜在的重要协变量和混杂因素后，我们将检验静脉血样中的表观遗传学改变与CMA风险相关的假设。目的1B（探索性）：我们将比较两组CMA病例之间的表观基因组模式：无过敏反应史与有过敏反应史。在控制了潜在的重要协变量和混杂因素后，我们将检验表观基因组模式因CMA严重程度而异的假设。目标2（探索性）：我们将表观基因组学数据与我们在芝加哥队列中获得的广泛的表型，环境和SNP数据相结合，这使我们能够同时评估环境，遗传学和表观基因组学的作用以及它们在CMA中的相互作用。这项研究的结果可能会改变我们对FA病因和生物学机制的理解。与遗传变异不同，表观遗传改变是潜在可逆的。更好地了解表观遗传机制可能会导致FA的预测，预防和治疗的新范式的发展。食物过敏（FA）在美国和世界范围内是一个日益严重的临床和公共卫生问题。目前防治FA的主要障碍是对其病因学和生物学机制的认识不足。该提案旨在进行全基因组定位，以确定FA中的表观基因组改变，并将这些数据与临床，环境和遗传数据相结合。研究结果对制定FA的预测、预防和治疗策略具有重要意义。

项目成果

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

EnsembleCNV：一种集成机器学习算法，用于使用 SNP 阵列数据识别拷贝数变异并对其进行基因分型。

• DOI: 10.1093/nar/gkz068
• 发表时间: 2019
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Zhang,Zhongyang;Cheng,Haoxiang;Hong,Xiumei;DiNarzo,AntonioF;Franzen,Oscar;Peng,Shouneng;Ruusalepp,Arno;Kovacic,JasonC;Bjorkegren,JohanLM;Wang,Xiaobin;Hao,Ke
• 通讯作者: Hao,Ke