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Large scale analyses of geneΓÇÉsmoking interaction on cardiometabolic traits

Institution: University of California, San Diego
Investigator(s): Rany Salem,
Award Cycle: 2019 (Cycle 29) Grant #: T29IR0770 Award: $874,673
Subject Area: Cardiovascular and Cerebrovascular Disease
Award Type: High Impact Research Project Award
Abstracts

Initial Award Abstract
Diabetes, hypertension, metabolic syndrome and cardiovascular disease are diseases of significant public health importance. It is important that we understand the role of genetic and environmental factors on these diseases. Large scale genome wide association studies have identified hundreds of genetic variants associated with these diseases, but largely ignored the interplay between genetic and environmental factors. One way to improve the increase insights into disease biology and genetic risk factors, is for researchers to use consider interplay of gene and environmental factors, particularly tobacco exposure through smoking. As such, this project aims to utilize individual level genotype-phenotype data form ~755,000 subjects from existing biomedical repositories. Specifically, we propose to assemble a very large dataset and test genes x smoking exposure on the domains of cardiometabolic disease among African American, Hispanic, and Non-Hispanic White subjects. We propose the following specific aims: Aim 1, we will test gene x smoking exposure on cardiometabolic related traits, including: markers of inflammation, blood pressure, heart function, lipids and insulin related measures. In Aim 2 we propose to test gene x smoking exposure on cardiometabolic disease endpoints, including: incident cardiovascular disease events (heart attacks and stroke), high blood pressure, type 2 diabetes and metabolic syndrome. Finally, in Aim 3 we will assess the association of gene x smoking on deaths endpoints. Our proposal has a number of innovative features that take advantage of unprecedented access to large-scale genetic-phenotypic datasets. That is, we will assemble these data into a ‘mega’ resource of consisting of ~43 individual-level phenotype and genome wide association studies (GWAS) retrieved from dbGaP and UK Biobank, allowing mega analyses and consideration of lower frequency genetic variants. The large sample allows study large scale study in African American (~36K) and Hispanic Americans (~43K) ancestry samples. Finally, longitudinal follow-up data for ~100K samples will allow follow-up analyses of in aims 2 and 3. We expect this work will identify novel genes and genetic loci in gene-smoking interactions and provide insights into cardiometabolic diseases biology and susceptibility.