Data-driven identification of environmental factors in cardiovascular disease

项目概要/摘要： 该奖项的目的是提供Chirag Patel博士，必要的支持，使他过渡到一个 独立调查员研究环境和遗传相互作用的疾病相关性状有关， 心血管疾病（CVD）。心血管疾病，如冠心病，是最沉重的负担之一 疾病在美国/世界，是多因素的，产生于环境之间的相互作用， 和遗传因素。通过全基因组关联研究（GWAS），研究人员已经能够 将1000多个遗传变异与CVD相关联;然而， 疾病没有跟上。此外，有必要描述环境和遗传因素如何相互作用 最终导致CVD。 博士帕特尔的长期研究目标是进行生物信息学研究，使跨学科 调查整合流行病学，环境健康科学和基因组学，以确定基因， 环境相互作用，为慢性病诊断和最终预防提供信息。帕特尔医生 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。 职业发展活动将侧重于巩固和扩大他的专业知识，应用 生物信息学方法，以流行病学数据，以确定相互作用的环境暴露和遗传 心血管风险特征的变异，如血压，2.）设计流行病学研究， 确定生物信息学衍生预测与未来冠状动脉风险相关的临床效用 心脏病，（3）。参加课程，以扩大他的知识，环境卫生科学， 心血管和代谢相关疾病遗传学，以及先进的生物信息学方法。一个 顾问委员会，其中包括他的导师，博士约翰PA扬，沿着与专家在环境 健康科学和生物信息学（Stephen A Rappaport和Atul J Butte博士）将监测他的进展 走向独立 该研究建议建立在现有的方法学工作的基础上， 环境流行病学，被称为“全环境关联研究”（EWAS）。EWAS类似于 现在的标准GWAS，用于搜索与风险特征和疾病相关的环境因素。 有了EWAS，调查人员现在能够全面扫描个人层面的因素，如工业和 消费者为基础的污染物，传染性病原体，膳食营养素，和药物，同时 与复杂性状的联系帕特尔博士的研究提案的假设是，有可能使用数据- 驱动的信息技术，如EWAS，以寻找环境因素，以及遗传和 环境因素，与CVD定量风险因素性状相关（例如，血压和胆固醇 水平），描述了相当大的临床疾病风险。为了评估疾病风险的大小，帕特尔博士将测试 来自生物信息学方法的发现可以预测临床CVD事件，如冠心病。 在此背景下，具体目标1是确定与CVD风险特征相关的环境暴露，例如 血压，使用全环境协会研究（EWAS）方法。这一目标将检验 在社区环境中存在与CVD危险因素相关的外部环境暴露。在特定 目标2，候选人将通过环境关联研究开发一种整合的遗传变体 （GxEWAS）方法，以确定与CVD相关的相互作用的环境因素和遗传变异 风险特征，包括血压和胆固醇水平。候选人声称，GxEWAS，一个综合的 一种结合GWAS和EWAS发现的方法，将能够识别协同组合 环境因素和遗传变异，将描述显着的CVD风险性状变异， 目前在GWAS中“失踪”。在将在独立R 00阶段执行的具体目标3中，Patel博士 将确定环境和遗传因素对冠心病发病的综合风险。在 目标3，候选人声称EWAS和GWAS中发现的因素组合将预测 他将设计一项流行病学研究来验证这一假设。 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R 01水平的调查有关的作用，环境因素在心血管疾病的病因，检查 遗传变异和环境暴露对疾病基因表达的共同影响。

Project Summary/Abstract: 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, investigators are 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 for Dr. Patel's research proposal is that is possible to use data- driven informatics technologies such as EWAS to find 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 "missing" 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 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.