Identifying Genetic and Epigenetic Risk Factors Regulating Gene Expression for Childhood Asthma

识别调节儿童哮喘基因表达的遗传和表观遗传风险因素

• 批准号: 10229584
• 负责人: Soyeon Kim
• 金额: $ 14.12万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-05 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10229584
• 关键词: Affect; Asthma; Award; Bioinformatics; Biological; Biological Markers; CISH gene; Child; Childhood; Childhood Asthma; DNA; DNA Methylation; Data; Disease; Distant; Doctor of Philosophy; Environment; Environmental Risk Factor; Epigenetic Process; Extrinsic asthma; Floor; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Goals; Grant; Health Care Costs; Journals; Knowledge; Laboratories; Malignant neoplasm of lung; Manuscripts; Measures; Mediation; Mentors; Methylation; Modeling; Molecular Abnormality; Morbidity - disease rate; Multiomic Data; Nasal Epithelium; Nature; Outcome; Pathogenesis; Pediatric Hospitals; Play; Postdoctoral Fellow; Puerto Rican; Quality of life; RNA; Regulation; Research; Rice; Risk Factors; Role; Single Nucleotide Polymorphism; Source; Statistical Methods; Supercomputing; Testing; Transcript; Universities; Work; asthmatic; base; clinical care; clinical center; differential expression; epigenetic regulation; epigenome-wide association studies; genome wide association study; learning strategy; machine learning method; machine learning model; medical schools; novel; novel therapeutics; precision medicine; prevent; statistical and machine learning; statistical learning; statistics; symposium; targeted treatment

Candidate: Dr. Soyeon Kim obtained her Ph.D. degree in Statistics at Rice University. For her doctoral research, she developed statistical machine learning methods to identify biomarkers for precision medicine approaches to treating lung cancer. She is currently a post-doctoral fellow at UPMC Children’s Hospital of Pittsburgh (CHP) and supported by a T32 grant awarded to one of her mentors, Dr. Juan C. Celedón. The goal of her research is to understand the pathogenesis of diseases by developing statistical methods for integrative analysis of multi-omics data. She is an author and co-author of eight manuscripts, including ones in high-profile journals such as Nature Genetics. Environment: CHP, affiliated with the University of Pittsburgh, School of Medicine, is a leading pediatric center for clinical care, research, and educational excellence. Dr. Kim works in the Rangos Research Center, comprised of nine floors of state-of-the-art laboratories, offices, and conference facilities. In addition to superb mentoring by Drs. Wei Chen, Juan C. Celedón and George Tseng, she will use the Pittsburgh supercomputing center (PSC), an excellent source of computing powers for intensive bioinformatics work. Research: Although both single-nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression, the associations between SNPs/DNA methylation and expression of disease genes in childhood asthma are mostly unknown. Due to the multiple-testing problem, most studies focus on identifying the regulation of gene expression by cis- (nearby) DNA features, rather than a combination of cis- and trans (far from genes) regulation. To overcome the major multiple-testing problem, we will develop a novel statistical machine learning method that can handle a large number of variables of omics data without multiple- testing correction issues. In Aim 1, I will identify cis and trans genetic (SNPs) and epigenetic (methylation) factors that regulate gene expression in the nasal epithelium of asthmatic and healthy subjects. Through this analysis, I will uncover novel SNPs and methylation CpGs that may have not been found in genome-wide association studies (GWAS) or epigenome-wide association studies (EWAS). In Aim 2, I will estimate the genetic and epigenetic contributions to the expressions of genes that are associated with atopic asthma. This study also will classify genes that are affected the most by genetic factors and genes that are affected mostly by epigenetic factors, which include environmental factors. In Aim 3, I will develop statistical methods to identify SNPs that indirectly regulate gene expression through methylation in nasal epithelium of asthmatic and healthy subjects. The outcome of this work will be a better understanding of the abnormal molecular and cellular mechanisms through which asthma develops, and the application of this knowledge to help to produce new therapies for asthma.

候选人：金秀妍博士在莱斯大学获得统计学博士学位。为了她的博士学位 在研究过程中，她开发了统计机器学习方法来识别精确医学的生物标记物 治疗肺癌的方法。她目前是UPMC儿童医院的博士后研究员 匹兹堡(CHP)，并由她的一位导师胡安·C·塞隆博士获得T32拨款支持。 她的研究之一是通过开发综合的统计方法来了解疾病的发病机制 多组学数据分析。她是八篇手稿的作者和合著者，其中一篇是备受瞩目的 《自然·遗传学》等期刊。 环境：CHP隶属于匹兹堡大学医学院，是一家领先的儿科中心 临床护理、研究和教育方面的卓越成就。金博士在仰光研究中心工作，成员包括 由九层最先进的实验室、办公室和会议设施组成。除了出色的指导之外 由魏晨、胡安·C·塞隆和乔治·曾博士撰写，她将使用匹兹堡超级计算中心(PSC)， 为密集的生物信息学工作提供极佳的计算能力来源。 研究：尽管单核苷酸多态(SNPs)和DNA甲基化都起着重要作用 在基因表达调控中，SNPs/DNA甲基化与疾病表达的关系 儿童哮喘的基因大多未知。由于多重测试问题，大多数研究都集中在 识别基因表达的调控是通过顺式(邻近)DNA特征，而不是顺式-(邻近)DNA特征的组合 和反式(远离基因)调控。为了克服多重测试的主要问题，我们将开发一种新的 一种统计机器学习方法，可以处理组学数据的大量变量，而不需要多个- 测试更正问题。在目标1中，我将确定顺式和反式遗传(SNPs)和表观遗传学 哮喘患者和健康人鼻上皮基因表达的甲基化调节因子 研究对象。通过这一分析，我将发现可能还没有发现的新的SNPs和甲基化CPGS 在全基因组关联研究(GWAS)或表观基因组全关联研究(EWAS)中发现。在目标2中， 我将估计基因和表观遗传学对相关基因表达的贡献。 患有特应性哮喘。这项研究还将对受遗传因素影响最大的基因进行分类 主要受表观遗传因素影响的基因，包括环境因素。在《目标3》中，我会 开发统计方法来识别通过以下途径间接调节基因表达的SNPs 哮喘患者和健康人鼻黏膜上皮细胞甲基化的研究这项工作的结果将是 更好地了解哮喘发生的异常分子和细胞机制，以及 这一知识的应用有助于产生治疗哮喘的新疗法。