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.
Cardiovascular-related diseases are the most burdensome diseases in the world and are caused by the interplay between environmental exposures and inherited genetic factors. This project aims to describe ways to examine the interplay between common environmental exposures (e.g., dietary nutrients and pollutant factors) and inherited genetic factors in risk for future cardiovascular disease. A better understanding of this interplay between environmental and genetic factors will help improve efforts to better diagnose and prevent diseases of highest public health priority.
|Chung, Ming Kei; Kannan, Kurunthachalam; Louis, Germaine M et al. (2018) Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment. Environ Sci Technol 52:8801-8810|
|Tangirala, Sivateja; Patel, Chirag J (2018) Integrated Analysis of Gene Expression Differences in Twins Discordant for Disease and Binary Phenotypes. Sci Rep 8:17|
|Brown, Adam S; Patel, Chirag J (2018) A review of validation strategies for computational drug repositioning. Brief Bioinform 19:174-177|
|Brown, Adam S; Rasooly, Danielle; Patel, Chirag J (2018) Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning. CPT Pharmacometrics Syst Pharmacol 7:124-129|
|Sagers, Jessica E; Brown, Adam S; Vasilijic, Sasa et al. (2018) Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma. Sci Rep 8:5437|
|Luber, Jacob M; Tierney, Braden T; Cofer, Evan M et al. (2018) Aether: leveraging linear programming for optimal cloud computing in genomics. Bioinformatics 34:1565-1567|
|Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770|
|Patel, Chirag J; Manrai, Arjun K; Corona, Erik et al. (2017) Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length. Int J Epidemiol 46:44-56|
|Patel, Chirag J (2017) Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era. Curr Epidemiol Rep 4:22-30|
|Magni, Paolo; Bier, Dennis M; Pecorelli, Sergio et al. (2017) Perspective: Improving Nutritional Guidelines for Sustainable Health Policies: Current Status and Perspectives. Adv Nutr 8:532-545|
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