Obesity and Asthma Subphenotypes and Underlying Genetic and Genomic Pathways

 描述（由申请人提供）： 候选人：Erick Forno博士是一位儿科肺病学家，在哮喘研究方面有很强的背景。他毕业于卡耶塔诺埃雷迪亚大学（秘鲁）医学院，在丹佛儿童医院做儿科住院医师（2006年），并在波士顿儿童医院/哈佛医学院做研究员（2009年）。他还拥有哈佛公共卫生学院的公共卫生硕士学位（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。 工作环境：匹兹堡大学医学院附属匹兹堡儿童医院（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Rangos研究中心工作，该中心由10层楼的最先进的实验室，办公室和会议设施组成。随着各种科学会议和会议在卫生防护中心，医学院和公共卫生研究生院举行，以及世界一流的研究教师，匹兹堡大学提供了特殊的机会，年轻的研究人员。除了Forno博士的主要导师Juan Celedón博士的出色指导外，机构承诺还包括>80%的研究保护时间;专用办公空间和资源分配;共享人员，包括遗传统计学家，数据库管理员和实验室工作人员;并保证工资支持至2016年7月。 匹兹堡大学基因组学和蛋白质组学核心实验室（GPCL）在Illumina和Affytechnology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。虽然有大量 尽管有证据表明两者之间存在关系，并且增加了对“肥胖哮喘”表型的识别，但潜在的机制仍不清楚。在这个项目中，我们的目标是更好地描述儿童肥胖性哮喘的亚表型，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识别新的生物标志物和治疗方法。 创新：这项拟议的研究代表了一个显着偏离现状，它定义肥胖哮喘仅基于高体重指数（BMI），并假设所有肥胖哮喘儿童是相似的。它也是创新的，因为它将增强表型与全基因组（GW）表观遗传和基因组数据相结合，以识别生物学上合理的遗传变异。 目的：我们的假设是“肥胖性哮喘”是异质性的，由几个亚表型组成，每个亚表型都有特定的遗传和基因组途径。我们的具体目标是：2）使用鼻上皮和白色血细胞（WBC）中的GW DNA甲基化和表达谱来鉴定与这种增强的表型相关的表观遗传和基因组途径; 3）鉴定表达和甲基化数量性状基因座（eQTL和mQTL），并进行mQTL/eQTL加权的遗传关联研究，以鉴定与“肥胖性哮喘”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聚类，这些聚类使用来自一组患有和不患有哮喘的波多黎各儿童的数据（波多黎各人在哮喘和肥胖方面都有很高的负担）。这些聚类将在两个独立的队列中进行验证：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，我们将从正在招募的500名儿童的鼻上皮和白细胞中获得GW DNA甲基化和基因表达谱。 我们将分析鼻上皮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被 包括在基因本体分析中以鉴定与亚表型相关的途径。在目标3中，我们将鉴定与目标1中的聚类相关的eQTL和mQTL。然后，我们将在mQTL/eQTL加权GWAS数据中查看来自Aim 2的基因和途径，以检测这些“肥胖性哮喘”亚表型的SNP。 未来发展方向：将包括扩大样本量，多“组学”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.