Genetic investigations of cardiometabolic disease: pleiotropy, gene x environment and causal inference analyses

PROJECT SUMMARY Cardiometabolic disease refers to a set of clinically overlapping diseases, including coronary artery disease, stroke, type 2 diabetes, hypertension, kidney disease and liver disease. These `cardiometabolic' diseases are distinct but share common risk factors, including low density lipoprotein, triglycerides, systolic blood pressure,

central adiposity and glycemic measures. We label this set of inter-related and heritable metabolic and hemodynamic traits the `cardiometabolic' (CM) heath profile. The CM health profile builds and extends the metabolic syndrome paradigm to interrogate the disease risk heterogeneity, variables underlying

pathophysiology and predisposition to specific adverse consequences. Our focus is on examination of the shared biology of the components and their role in the pathophysiology of CM disease endpoints, rather than consideration of disease syndromes. Differences in end-organ consequences suggests that there may be

subtypes of the CM health profile with potentially different underlying pathophysiology and propensity for adverse outcomes. We hypothesize that genetic analyses can enhance biological insights into CM health profile component and disease endpoints by exploiting our mega-scale size sample of 1,365,750 subjects,

across five racial-ethnic groups, and consideration of pleiotropy, genetic subtyping, environmental modulation, and causal inference analyses. We propose the following specific aims: 1) Pleiotropy, genetic classification and association analysis of CM health profile components; 2) Gene x environment (GxE) interaction analyses of

CM disease endpoints; and 3) Causal inference analysis of CM health profile components, risk factors and disease endpoints. There is a pressing need to better understand the genetic architecture of CM health profile, relationship of components and their role in disease endpoints. If successful, this proposal will increase

genomic discovery, identify novel loci, and pathways, and enhance biological insights of CM health profile and disease morbidity. We anticipate that our comprehensive approach to understand CM health profile biology will bring us several steps closer towards identifying novel molecular targets and modifiable risks to achieve this

goal.