Data-driven identification of environmental factors in cardiovascular disease

描述(由申请人提供)： 这一奖项的目的是为希拉格·帕特尔博士提供必要的支持，使他成为一名独立的研究员，研究与心血管疾病(CVD)相关的疾病相关特征中的环境和遗传相互作用。心血管疾病，如冠心病，是美国/世界上负担最重的疾病之一，是多因素的，是环境因素和遗传因素相互作用的结果。通过全基因组关联研究，研究人员已经能够将数千种遗传变异与心血管疾病联系起来；然而，与疾病相关的环境因素的识别并没有跟上步伐。此外，有必要描述环境和遗传因素如何相互作用，最终导致心血管疾病。Patel博士的长期研究目标是进行生物信息学研究，以实现整合流行病学、环境健康科学和基因组学的跨学科调查，以确定对慢性病诊断和最终预防有用的基因与环境之间的相互作用。Patel博士拥有生物信息学的高级学位，在基因组数据的统计和分析方面接受过大量培训。职业发展活动将集中于巩固和扩展他的专业知识，方法是：1)将生物信息学方法应用于流行病学数据，以确定相互作用的环境暴露和心血管风险特征的遗传变异，如血压；2)设计流行病学研究，以确定生物信息衍生预测在 3)参加课程，以扩大他在环境健康科学、心血管和代谢相关疾病遗传学以及高级生物信息学方法学方面的知识。一个包括他的导师John PA Ioannidis博士以及环境健康科学和生物信息学专家(Stephen A RapPaport博士和Atul J Butte博士)的咨询委员会将监督他走向独立的进展。这项研究建议建立在现有方法学工作的基础上，将生物信息学方法应用于环境流行病学，称为“全环境关联研究”(Ewas)。环境影响评估类似于现在的标准全球环境评估，其目的是寻找与风险特征和疾病相关的环境因素。有了Ewas，人们现在能够全面扫描个人层面的因素，如基于工业和消费者的污染物、感染剂、饮食营养素和药物，以同时与复杂的特征相关联。这里的假设是，使用数据驱动的信息学技术，如Ewas，可以开始识别与心血管疾病定量风险因素特征(例如，血液)相关的环境因素以及遗传和环境因素的组合 血压和胆固醇水平)，描述了相当大的临床疾病风险。评估…的规模 在疾病风险方面，Patel博士将测试生物信息学方法得出的结果是否可以预测临床心血管事件，如冠心病。在这方面，具体目标1是确定 环境暴露与心血管疾病风险特征，如血压，使用全环境协会研究(EWAS)方法。这一目标将测试社区环境中是否存在与心血管疾病风险因素相关的外部环境暴露。在具体目标2中，候选人将开发一种综合的全环境关联研究的遗传变异(GxEwas)方法，以确定相互作用的环境因素和与心血管疾病风险特征相关的遗传变异，包括血压和胆固醇水平。候选人声称，GxEwas是一种综合方法，它结合了GWAS和Ewas的研究结果，将能够识别环境因素和遗传变量的协同组合，这些变量将描述目前在GWAS中“错误”的重大心血管疾病风险特征变异性。在具体目标3中，将在独立的R00阶段执行，Patel博士将确定环境和遗传因素对偶发冠心病的综合风险。在具体目标3中，候选人声称在EWAS和GWAS中发现的因素组合将预测临床心脏病，他将设计一项流行病学研究来检验这一假设。为了实现这些研究目标，Patel博士将利用NIH和CDC赞助的已建立的基于人群的研究，包括基于社区的健康调查和纵向队列。帕特尔博士将整合不同的环境措施，包括暴露的生物标志物和自我报告的信息。该项目将使未来的R01级关于环境因素在心血管疾病病因中的作用的调查成为可能，检查遗传遗传变异和环境暴露在疾病基因表达中的联合影响。

DESCRIPTION (provided by applicant): The purpose of this award is to provide Chirag Patel, PhD, the support necessary to transition him to an independent investigator studying the environmental and genetic interplay in disease-related traits related to cardiovascular disease (CVD). CVDs, such as coronary heart disease, are among the most burdensome diseases in the United States/world and are multifactorial, arising out of the interplay between environmental and genetic factors. Through Genome-wide Association Studies (GWAS), investigators have been able to associate 1000s of genetic variants with CVD; however identification of environmental factors related to disease has not kept pace. Further, there is a need to describe how environmental and genetic factors interact to ultimately cause CVD. Dr. Patel's long-term research goal is to conduct bioinformatics research to enable inter-disciplinary investigations integrating epidemiology, environmental health sciences, and genomics to identify gene-by- environment interactions that are informative for chronic disease diagnosis and eventual prevention. Dr. Patel has an advanced degree in bioinformatics with significant training in statistics and analysis of genomic data. The career development activities will focus on consolidating and expanding his expertise by 1) Applying bioinformatics methods to epidemiological data to identify interacting environmental exposures and genetic variants in cardiovascular risk traits, such as blood pressure, 2) Designing an epidemiological study to ascertain the clinical utility of bioinformatically-derived predictions in their association to future risk for coronary heart disease, and, 3) Attending courses to expand his knowledge of the environmental health sciences, cardiovascular- and metabolic-related disease genetics, and advanced bioinformatics methodology. An Advisory Committee, which includes his mentor, Dr. John PA Ioannidis, along with experts in environmental health sciences and bioinformatics (Drs. Stephen A Rappaport and Atul J Butte) will monitor his progress towards independence. The research proposal builds on existing methodological work applying bioinformatics methods in environmental epidemiology, called an "Environment-wide Association Study" (EWAS). EWAS is an analog to the now standard GWAS, implemented to search for environmental factors associated to risk traits and disease. With EWAS, one is now able to comprehensively scan personal-level factors such as industrial and consumer-based pollutants, infectious agents, dietary nutrients, and pharmaceuticals for simultaneous association with complex traits. The hypothesis here is that the use of data-driven informatics technologies such as EWAS can begin to identify environmental factors, and combinations of genetic and environmental factors, associated with CVD quantitative risk factor traits (e.g., blood pressure and cholesterol levels) that describe sizable clinical disease risk. To assess the size of disease risk, Dr. Patel will test whether findings derived from bioinformatics methods can predict clinical CVD events, such as coronary heart disease. In this context, Specific Aim 1 is to identify environmental exposures associated with CVD risk traits, such as blood pressure, using the Environment-Wide Association Study (EWAS) approach. This aim will test whether there are external environmental exposures correlated with CVD risk factors in a community setting. In Specific Aim 2, the candidate will develop an integrative Genetic variant by Environment-Wide Association Study (GxEWAS) approach to identify interacting environmental factors and genetic variants associated with CVD risk traits, including blood pressure and cholesterol levels. The candidate claims that GxEWAS, an integrative approach that combines findings from GWAS and EWAS, will enable identification of synergistic combinations of environmental factors and genetic variants that will describe significant CVD risk trait variability that is currently "missig" in GWAS. In Specific Aim 3, to be executed during the independent R00 phase, Dr. Patel will ascertain the combined risk of environmental and genetic factors on incident coronary heart disease. In Specific Aim 3, the candidate claims that a combination of factors found in EWAS and GWAS will be predictive of clinical heart disease and he will design an epidemiological study to test this hypothesis. To achieve these research aims, Dr. Patel will utilize established NIH- and CDC-sponsored population-based studies, including community-based health surveys and longitudinal cohorts. Dr. Patel will integrate diverse environmental measures, including biomarkers of exposure and self-reported information. The project will enable future R01-level investigation regarding the role of environmental factors in CVD etiology, examining the joint influence of inherited genetic variants and environmental exposures in disease gene expression.