Human genetics and big data to guide cardiovascular drug development

In this thesis, a number of studies are described that are centred around obtaining a deeper understanding of the role of various risk factors and therapeutic targets for cardiovascular disease, with genetic techniques being the overarching approach. In part one, we used Mendelian randomization to assess the presence and extent of causality between a number of risk factors and/or therapeutic targets and cardiovascular disease (CVD). Among others, the results of chapter 2 suggest that for the same level of LDL-C reduction, men might experience more CVD benefit compared to women, illustrating the need for sex-specific investigations into the causal associations of cardiovascular risk factors on disease. We also show that there are causal associations between IL-6 levels and ASGR-1 levels with the risk for CVD, and found that combined lowering of CETP and PCSK9 results in an additive association on the risk for CVD, rather than a multiplicative association. We also show that there is no suggestion of a causal association between the neutrophil-to-lymphocyte ratio and coronary artery disease. In part two, we investigated and reviewed the clinical relevance of polygenic risk scores (PGS) for Low Density-Lipoprotein cholesterol (LDL-C) in the prediction of cardiovascular disease. Our data suggest that, in the setting of extreme lipid disorders, a small LDL PGS has limited added value in CVD risk prediction. We also discuss LDL-C PGS in general: LDL-C PGS are currently not widely used in clinical care, and it seems that general CVD PGS might be more sensitive tools to identify patients with a severely increased genetic risk on CVD.