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: 10615583
• 负责人: Soyeon Kim
• 金额: $ 13.9万
• 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/10615583
• 关键词: Affect; Asthma; Award; Bioinformatics; Biological; CISH gene; Child; Childhood; Childhood Asthma; Classification; DNA; DNA Methylation; Data; Disease; Distant; Doctor of Philosophy; Education; 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; biomarker identification; 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：金素妍博士获得博士学位。莱斯大学统计学学位。为了她的博士学位 研究，她开发了统计机器学习方法，以确定精准医学的生物标志物 治疗肺癌的方法。她目前是加州大学伯克利分校儿童医院的博士后研究员。 匹兹堡（CHP）和T32赠款授予她的导师之一，博士胡安C。塞勒东目标 她的研究是通过发展综合统计方法来了解疾病的发病机制。 多组学数据分析。她是八份手稿的作者和合著者，其中包括备受瞩目的手稿。 《自然遗传学》等期刊。 环境：卫生防护中心，附属于匹兹堡大学医学院，是一个领先的儿科中心 临床护理、研究和卓越教育。金博士在Rangos研究中心工作， 九层楼的最先进的实验室，办公室和会议设施。除了高超的指导， 作者：Drs. Wei Chen，Juan C.塞勒顿和乔治曾，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出色的计算能力。 研究：虽然单核苷酸多态性（SNP）和DNA甲基化都起着重要作用， 在调节基因表达方面，SNPs/DNA甲基化与疾病表达之间的关系 儿童哮喘的基因大多是未知的。由于多重测试问题，大多数研究集中在 通过顺式（邻近）DNA特征识别基因表达的调节，而不是顺式- 和反式（远离基因）调控。为了克服主要的多重测试问题，我们将开发一种新的 统计机器学习方法，可以处理组学数据的大量变量，而无需多个 测试校正问题。在目标1中，我将确定顺式和反式遗传（SNP）和表观遗传 调节哮喘患者和健康人鼻上皮基因表达的甲基化因子 科目通过这项分析，我将发现新的SNP和甲基化CpG，这些SNP和甲基化CpG可能还没有被发现。 在全基因组关联研究（GWAS）或表观基因组关联研究（EWAS）中发现。在目标2中， 我将估计遗传和表观遗传对相关基因表达的贡献， 过敏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， 这些基因主要受包括环境因素在内的表观遗传因素的影响。在目标3中，我将 开发统计方法，以确定SNPs，间接调节基因表达， 哮喘和健康受试者鼻上皮中的甲基化。这项工作的成果将是一个 更好地了解哮喘发生的异常分子和细胞机制， 应用这些知识来帮助开发哮喘的新疗法。

项目成果

Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus.

多组学共定位与全基因组关联研究揭示了儿童哮喘发病风险位点的特定背景遗传机制。

• DOI: 10.1186/s13073-021-00967-y
• 发表时间: 2021-10-10
• 期刊: Genome medicine
• 影响因子: 12.3
• 作者: Soliai MM;Kato A;Helling BA;Stanhope CT;Norton JE;Naughton KA;Klinger AI;Thompson EE;Clay SM;Kim S;Celedón JC;Gern JE;Jackson DJ;Altman MC;Kern RC;Tan BK;Schleimer RP;Nicolae DL;Pinto JM;Ober C
• 通讯作者: Ober C