Leisure-time physical activity (LTPA) is a well-established modifiable lifestyle factor related to multiple cardio-metabolic outcomes, including obesity, type 2 diabetes (T2D), cardiovascular diseases (CVD) and stroke. Both physical inactivity and cardio-metabolic diseases are the epidemics of our time, inflicting millions of individuals on the global scale. Various psychological, biological, social, and environmental correlates of LTPA have been identified, and many studies have examined the extent to which these correlates influence LTPA. However, the role of genetic factors in affecting LTPA, and thereby, influencing the risk of cardio-metabolic diseases, has not been investigated comprehensively and in-depth. To better understand the genetic architecture of LTPA across diverse groups, we propose to identify the genetic variants, biological pathways and gene networks associated with human LTPA by incorporating both common and rare variants into the analysis and by adopting innovative systems biology and bioinformatics approaches that integrate multidimensional data from well-characterized multi-ethnic populations. In order to illustrate the LTPA-mediated mechanisms linking the genetic component to cardio-metabolic health, we will further explore the causal paradigm for PA genetics, LTPA levels, and the risk of cardio-metabolic diseases by applying mediation methodologies within the counterfactual framework. Leveraging the unparalleled sample size and rich data from the Women's Health Initiative (WHI), the Framingham Heart Study (FHS), the Jackson Heart Study (JHS), along with the sequencing data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), we hope that the efforts outlined in the current application will help build the foundation of PA genetics, provide insight into the pathogenesis of many chronic diseases related to insufficient LTPA, point to new molecular targets to improve cardio-metabolic health through personalized lifestyle interventions, and also serve as a successful model for future behavioral genetics research, particularly for those traits with a genetic architecture featuring hundreds or thousands of variants with subtle effects.
|Program type||Institute - Uncovering New Patterns Fellowship|
|Effective start/end date||04/02/2018 → 07/13/2018|