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（由申请人提供）：食物过敏（FA）是由免疫球蛋白（IG）电子介导的对食物的超敏反应引起的疾病。 FA影响约5-8％的儿童和1-4％的成年人，并且是美国和全球范围内日益增长的临床和公共卫生问题。防止和治疗FA的主要障碍是我们对其病因和生物学机制的不完全理解。现有证据表明，FA是由多个环境和遗传因素决定的复杂特征。该提案建立在我们对FA环境和遗传因素的持续研究基础上，旨在探索一个新的领域：与FA的表观基因组联系。表观遗传学被定义为对没有改变DNA序列的基因表达的可遗传变化的研究。表观基因组学是对全基因组量表的表观遗传学的研究。尽管没有可用于FA的表观遗传学或表观基因组学数据，但表观遗传学似乎在细胞分化和细胞因子基因表达中起重要作用。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理解FA的病因学和生物学机制的有前途的方法，这可能会提供机械的洞察力，以了解遗传变异和环境暴露如何改变FA的风险，严重程度和长期结果。该建议的主要重点是进行全基因组映射以识别FA的表观遗传改变，并将此类数据与表型，环境和遗传数据相结合。我们将利用芝加哥队列的广泛资源来解决以下特定目的：AIM 1A：我们将使用芝加哥同类术的600名白人儿童的表观分子图：300个牛奶过敏（CMA）病例（CMA）案例（CMA）案例（CMA）案例（CMA）案例（CMA）案例，使用Illumina hommethylation 27 DNA分析Beadchip（Inviminium）。我们将测试以下假设：在控制潜在的重要协变量和混杂因子之后，静脉血液样本的表观遗传改变与CMA的风险有关。 AIM 1B（探索性）：我们将比较两组CMA病例之间的表观基因组模式：没有与过敏反应的史。我们将在控制潜在重要的协变量和混杂因素之后，测试表观基因组模式因CMA的严重程度而有所不同的假设。 AIM 2（探索性）：我们将将表观基因组数据与芝加哥队列中可用的广泛表型，环境和SNP数据整合在一起，这使我们能够同时评估环境，遗传学和表观基因组学以及它们在CMA中的相互作用。这项拟议研究的发现可能会改变我们对FA的病因和生物学机制的理解。与遗传变异不同，表观遗传替代可能可逆。更好地了解表观遗传机制可能会导致发展新的范式，以预测，预防和治疗FA。食品过敏（FA）是美国和全球范围内日益增长的临床和公共卫生问题。防止和治疗FA的主要障碍是我们对其病因和生物学机制的不完全理解。该提案旨在执行全基因组映射，以识别FA中的表观基因组改变，并将此类数据与临床，环境和遗传数据相结合。研究结果应该对发展FA的预测，预防和治疗方面的策略具有重要意义。

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