Linking genetic risk factors of complex disorders and health burden in populations

Genome-wide association studies (GWAS) have expanded our understanding of complex disorders and their population health implications to encompass the influence of genetic risk factors. However, the impact of the discovered genetic risk factors on the related health burden has been less investigated. This thesis unites three distinct studies, each one with a distinct focus on diverse aspects of genetic determinants and their impacts on health: alcohol-related morbidity and mortality, the use of medications for cardiometabolic diseases, and the overall burden of disease. In our first study, we examined the predictive potential of a polygenic risk score (PRS) of alcohol consumption for related health burdens. We constructed a novel PRS of 1.1 million variants for alcohol consumption and found significant associations between the PRS and alcohol-related morbidity and all-cause mortality. Our findings highlight the important role of heritable factors in alcohol-related health burdens, emphasizing the potential of measured genetic risk of behavior for predicting health outcomes. In our second study, we explored the genetic architectures of medication use for hyperlipidemia, hypertension, and type 2 diabetes. We performed genome-wide analyses of longitudinal medication use patterns, resulting in 333 independent loci associated with medication use. Our results not only revealed cardiometabolic risk loci through medication-use strategies but also reinforced the importance of polygenic risk for cardiometabolic risk factors in predicting medication-use behavior. Finally, in our third study, we quantified the effect of genetic risk factors on disability-adjusted life years (DALYs), using genetic data from an extensive cohort and considering a range of diseases. We discovered that both rare and common genetic variants significantly influence DALYs, with certain common variants exerting population-level effects comparable to modifiable risk factors such as high sodium intake and low physical activity. Collectively, our work connects the genetic architectures of polygenic health-related traits to disease burden, providing a more comprehensive understanding of complex disorders in the context of population health. Our work demonstrates the potential of genetic insights in predicting health outcomes and highlights the significant role of genetic risk factors in health and disease.