Novel genetic risk factors in coronary artery disease and related biomarkers

Coronary artery disease (CAD) is the most common of the cardiovascular diseases and the leading cause of premature death and disability worldwide, with both environmental and genetic factors impacting on individual’s risk of developing CAD. Aim of this thesis is to improve understanding of the genetic background of CAD and its risk factors as well as improve the risk estimation for CAD 1) by developing statistical methods and tools used in multivariate genetic analysis of biomarkers, 2) by expanding the traditionally used biomarker set for coronary artery disease, 3) by studying an isolated population to identify high-impact variants either very rare or absent in other populations and 4) by identifying individuals in high- risk for CAD using both traditional and genetic risk factors. We enabled further investigation of known association between CAD and KIV2-CN, a copy number repeat in LPA with known causal effect on CAD in larger sample sizes without costly sequencing. This was done by developing an imputation method to estimate KIV2-CN using information on the genetic variants in close proximity. We additionally developed multivariate methods used in genetic studies to enable essential follow-up analysis tools also for multivariate GWAS. We both improved the detection of genetic associations for the correlated inflammatory biomarkers and enabled fine-mapping of the observed associations. In addition, by extending our research outside of the traditional CAD biomarkers with Lp(a) and inflammation we gained relevant information on genetic determinants of Lp(a) and their impact on CAD risk, as well as potential novel shared genetic link between inflammation and venous thromboembolism. In a large Finnish biobank study, we identified a highly Finnish-enriched variant in MFGE8 gene to associate with CAD. Interestingly, this variant was not associated with traditional risk factors for CAD, such as blood lipids, but instead, our results are in line with previous literature on MFGE8 and inflammatory aging process of the arteries. Lastly, we estimated and communicated risk for CAD using both traditional risk factors for CAD as well as by combining information on traditional risk factors and genetic risk information. We saw that knowledge of one’s risk for CAD did have impact on health behaviour that reflected as improved risk factor profile, both genetic and traditional risk having independent effect on the behaviour change. Findings in this thesis show how we can gain important knowledge on the genetic background of coronary artery disease and its risk factors by methodological advances, by expanding biomarkers analyzed as well as by investigating isolated populations with enrichment of rare high-impact variants. This knowledge can further be applied to other common, complex diseases.