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Data-driven identification of environmental factors in cardiovascular disease

心血管疾病环境因素的数据驱动识别

基本信息

批准号：
8804261
负责人：
CHIRAG J. PATEL
金额：
$ 12.25万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-03-01 至 2015-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8804261
关键词：
Accounting Address Advisory Committees Air Pollution Award Bioinformatics Biological Markers Blood Pressure Cardiovascular Diseases Cardiovascular system Centers for Disease Control and Prevention (U.S.)Chlorinated Hydrocarbons Cholesterol Chronic Disease Clinical Communities Complex Coronary heart disease Data Diagnosis Diet Disease Doctor of Philosophy Environment Environmental Epidemiology Environmental Exposure Environmental Health Environmental Risk Factor Epidemiologic Methods Epidemiologic Studies Epidemiology Etiology Event Future Gene Expression Genetic Genetic Determinism Genome Genomics Goals Health Health Surveys Heart Diseases Hereditary Disease Heritability Human Hypertension Infectious Agent Informatics Inherited Investigation Joints Knowledge Measurement Measures Mentors Metabolic Methodology Methods Monitor Non-Insulin-Dependent Diabetes Mellitus Nutrient Pathway interactions Patient Self-Report Pharmacologic Substance Phase Phenols Platelet Factor 4 Prevention Research Research Personnel Research Proposals Risk Risk Factors Role Scanning Scientist Smoking Technology Testing Training United States United States National Institutes of Health Variant Work analog base cardiovascular disorder risk cardiovascular risk factor career development cohort community setting design disease diagnosis disorder risk gene environment interaction genetic risk factor genetic variant genome wide association study heart disease risk improved insulin sensitivity phthalates pollutant population based prevent prognostic public health priorities statistics trait

项目摘要

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.

描述（由申请人提供）：该奖项的目的是为Chirag Patel博士提供必要的支持，使他成为一名独立的研究人员，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/世界上最沉重的疾病之一，是多因素的，由环境和遗传因素之间的相互作用引起。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，与疾病相关的环境因素的识别并没有跟上。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。Patel博士的长期研究目标是进行生物信息学研究，使跨学科的调查整合流行病学，环境健康科学和基因组学，以确定基因与环境的相互作用，为慢性病诊断和最终预防提供信息。Patel博士拥有生物信息学高级学位，在基因组数据的统计和分析方面接受过重要培训。职业发展活动将侧重于巩固和扩大他的专业知识，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（如血压）的遗传变异，2）设计流行病学研究，以确定生物信息学预测的临床效用，他们的协会，以未来的冠心病风险，和，3）参加课程，以扩大他的知识，环境健康科学，心血管和代谢相关的疾病遗传学，和先进的生物信息学方法。一个咨询委员会，其中包括他的导师，约翰·帕·约恩尼斯博士，沿着与环境健康科学和生物信息学专家（斯蒂芬·A·拉帕波特博士和阿图尔·J·布特博士）将监测他走向独立的进展。该研究建议建立在现有的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）。EWAS是现在标准的GWAS的类似物，用于搜索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，如工业和消费者污染物，传染性病原体，饮食营养素和药物，以同时与复杂的性状相关联。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CVD定量风险因素性状相关的环境因素以及遗传和环境因素的组合（例如，血液压力和胆固醇水平），描述了相当大的临床疾病风险。评估…的大小为了研究心血管疾病的风险，Patel博士将测试来自生物信息学方法的发现是否可以预测临床心血管疾病事件，如冠心病。在这方面，具体目标1是确定环境暴露与CVD风险特征相关，如血压，使用环境广泛协会研究（EWAS）方法。这一目标将测试是否有外部环境暴露与心血管疾病的危险因素在社区设置。在具体目标2中，候选人将通过全环境关联研究（GxEWAS）方法开发综合遗传变异，以确定与CVD风险特征（包括血压和胆固醇水平）相关的相互作用的环境因素和遗传变异。该候选人声称，GxEWAS是一种综合方法，结合了GWAS和EWAS的发现，将能够识别环境因素和遗传变异的协同组合，这些因素将描述目前在GWAS中“缺失”的显著CVD风险性状变异。在将在独立R 00阶段执行的特定目标3中，Patel博士将确定环境和遗传因素对冠心病事件的综合风险。在具体目标3中，候选人声称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。Patel博士将整合各种环境措施，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水平的调查有关环境因素在心血管疾病病因学中的作用，研究遗传变异和环境暴露对疾病基因表达的联合影响。