Obesity and Asthma Subphenotypes and Underlying Genetic and Genomic Pathways

 描述(申请人提供)：应聘者：埃里克·福尔诺博士是一位儿科肺科医生，在哮喘研究方面有很强的背景。他毕业于卡耶塔诺·赫雷迪亚大学医学院(PERú)，2006年在丹佛儿童医院做儿科住院医师，2009年在波士顿/哈佛医学院儿童医院担任研究员。他还拥有哈佛大学公共卫生学院公共卫生硕士学位(2009年)。他的主要科学兴趣是儿童肥胖和哮喘之间的联系。他是31篇手稿的作者或合著者，其中26篇是关于儿童哮喘的流行病学和遗传流行病学。2013年，他当选为儿科研究学会(SPR)成员。 环境：匹兹堡大学附属于匹兹堡大学医学院的匹兹堡儿童医院(CHP)是一家领先的儿科临床护理、研究和教育中心。Forno博士在新的仰哥斯研究中心工作，该中心由10层最先进的实验室、办公室和会议设施组成。通过在CHP、医学院和公共卫生研究生院举行的各种科学会议和会议，以及世界级的研究人员，匹兹堡大学为年轻的研究人员提供了难得的机会。除了福尔诺博士的主要导师胡安·塞隆博士的卓越指导外，机构承诺包括：80%的研究保护时间；专门的办公空间和资源分配；共享人员，包括遗传统计学家、数据库经理和实验室工作人员；以及到2016年7月的保证工资支持。 匹兹堡大学基因组学和蛋白质组学核心实验室(GPCL)在Illumina和Affymetrix平台上为全基因组(GW)基因分型、DNA甲基化和基因表达分析提供全面的实验室和生物信息学支持。 研究： 意义：儿童哮喘和肥胖是主要的公共卫生问题。趁有足够的资金 尽管有证据表明两者之间存在联系，但“肥胖哮喘”表型的认知度增加，其潜在机制仍不清楚。在这个项目中，我们的目标是更好地描述儿童肥胖哮喘的亚型，并确定潜在的遗传、表观遗传和基因组机制。这将使我们能够更好地识别特定的患者群体，并确定新的生物标记物和治疗方法。 创新：拟议的研究与现状有很大不同，现状是单纯根据高体重指数(BMI)定义肥胖哮喘，并假设所有肥胖哮喘儿童都是相似的。它还具有创新性，因为它将把增强的表型分析与全基因组(GW)表观遗传学和基因组数据相结合，以便识别生物学上可信的遗传变异。 目的：我们的假设是“肥胖哮喘”是异质性的，由几个亚型组成，每一个亚型都有特定的遗传和基因组途径。我们的具体目标是：1)通过非监督分析确定肥胖哮喘亚型，并在外部队列中验证它们；2)利用鼻黏膜上皮和白细胞(WBC)中的GW DNA甲基化和表达谱，确定与这种增强表型相关的表观遗传学和基因组途径；以及3)确定表达和甲基化的数量性状基因座(eQTL和mQTL)，并对遗传关联进行mQTL/eQTL加权研究，以确定与“肥胖哮喘”表型相关的遗传变异。 方法：在目标1中，我们将使用聚类分析、判别分析和递归划分来定义具有相似表型特征的肥胖哮喘儿童的集群，使用来自波多黎各有哮喘和没有哮喘的儿童队列的数据(波多黎各人分担哮喘和肥胖的高负担)。这些分组将在两个独立的队列中得到验证：CAMP(儿童哮喘管理计划，n=1,041)和GARCS(哥斯达黎加哮喘遗传学研究，n=1,150)。在目标2中，我们将从500名招募的儿童的鼻黏膜上皮和白细胞中获得GW DNA甲基化和基因表达谱。我们将分析鼻黏膜上皮细胞和白细胞甲基化的变化，以及与目标1中确定的簇相关的基因表达。显著上调或下调的基因将 包括在基因本体论分析中，以识别与亚表型相关的途径。在目标3中，我们将确定与目标1中的簇相关的eQTL和mQTL。然后，我们将在mQTL/eQTL加权的Gwas数据中查看目标2中的基因和途径，以检测这些“肥胖哮喘”亚型背后的SNPs。 未来方向：将包括扩大样本量、多个“基因组”整合、功能验证，以及招募专门用于研究肥胖和哮喘的独立队列。

 DESCRIPTION (provided by applicant): Candidate: Dr. Erick Forno is a pediatric pulmonologist with a strong background in asthma research. He graduated medical school from Cayetano Heredia University (Perú), did residency in Pediatrics at Children's Hospital of Denver (2006) and fellowship at Children's Hospital Boston / Harvard Medical School (2009). He also has a Master's in Public Health (MPH) from Harvard School of Public Health (2009). His main scientific interest is the association between childhood obesity and asthma. He is author or co-author of 31 manuscripts, 26 of which are on the epidemiology and genetic epidemiology of childhood asthma. In 2013 he was elected to the Society of Pediatric Research (SPR). Environment: Children's Hospital of Pittsburgh (CHP) of UPMC, affiliated with the University of Pittsburgh School of Medicine, is a leading pediatric center for clinical care, research, and education excellence. Dr. Forno works in the new Rangos Research Center, comprised of 10 floors of state-of-the-art laboratories, offices, and conference facilities. With a variety of scietific conferences and meetings held at CHP, the School of Medicine, and the Graduate School of Public Health, as well as world-class research faculty, the University of Pittsburgh offers exceptional opportunities to young investigators. In addition to superb mentoring by Dr. Juan Celedón, Dr. Forno's primary mentor, institutional commitment includes >80% research protected time; dedicated office space and resource allocation; shared personnel including a genetic statistician, database manager, and laboratory staff; and guaranteed salary support until July 2016. The University of Pittsburgh Genomics and Proteomics Core Laboratories (GPCL) offers full laboratory and bioinformatics support for genome-wide (GW) genotyping, DNA methylation, and gene expression analysis, on both Illumina and Affymetrix platforms. Research: SIGNIFICANCE: Childhood asthma and obesity are major public health problems. While there is ample evidence of a relationship between both and increased recognition of an "obese asthmatic" phenotype, the underlying mechanisms are still unclear. In this project we aim to better characterize sub-phenotypes of obese asthma in children, and to identify underlying genetic, epigenetic, and genomic mechanisms. This will allow us to better recognize specific groups of patients and identify novel biomarkers and treatment approaches. INNOVATION: The proposed research represents a significant departure from the status quo, which defines obese asthma solely based on high body mass index (BMI) and assumes all obese asthmatic children are similar. It is also innovative because it will integrate enhanced phenotyping with genome-wide (GW) epigenetic and genomic data in order to identify biologically plausible genetic variants. AIMS: Our hypothesis is that "obese asthma" is heterogeneous and comprised of several sub-phenotypes, each with specific genetic and genomic pathways. Our specific aims are: 1) To define obese asthmatic subphenotypes via unsupervised analysis and validate them in external cohorts; 2) To identify epigenetic and genomic pathways associated with such enhanced phenotypes, using GW DNA methylation and expression profiling in both nasal epithelium and white blood cells (WBCs); and 3) To identify expression and methylation quantitative trait loci (eQTLs and mQTLs) and perform an mQTL/eQTL-weighted study of genetic association to identify genetic variants associated with "obese asthma" phenotypes. APPROACH: In Aim 1, we will use cluster analysis, discriminant analysis, and recursive partitioning to define clusters of obese asthmatic children with similar phenotypic characteristics using data from a cohort of Puerto Rican children with and without asthma (Puerto Ricans share high burdens of both asthma and obesity). These clusters will be validated in two independent cohorts: CAMP (Childhood Asthma Management Program, n=1,041) and GARCS (Genetics of Asthma in Costa Rica Study, n=1,150). In Aim 2, we will obtain GW DNA methylation and gene expression profiling from nasal epithelium and WBCs in 500 children that are being recruited. We will analyze changes in nasal epithelial and WBC methylation and gene expression associated with the clusters identified in Aim 1. Genes significantly up- or down-regulated will be included in gene ontology analyses to identify pathways related to the sub-phenotypes. In Aim 3, we will identify eQTL and mQTL that are also associated with clusters from Aim 1. Then, we will look at genes and pathways from Aim 2 in mQTL/eQTL-weighted GWAS data to detect SNPs that underlie these "obese asthma" sub-phenotypes. FUTURE DIRECTIONS: Will include expanding sample size, multi-"omic" integration, functional validation, and recruitment of an independent cohort specifically designed to study obesity and asthma.