Figure 2.
Generation and Use of Polygenic Risk Scores
Identification: genome-wide association studies are required to identify variants that are associated with CAD and early-onset myocardial infarction (EOMI). Bioinformatic tools enable the imputation of not directly genotyped variants to increase the numbers of variants that can then be compared between healthy control subjects and cases. Statistical analysis leads to the identification of variants that are associated with CAD at the genome-wide level of significance (p < 10−8). Modeling: in a second step, polygenic risk scores are based on modeling the sum of particular risk alleles, integrating their effect sizes. Polygenic risk scores follow a Gaussian distribution with most subjects carrying an intermediate number of risk alleles and a small portion either carrying a small or a large number of risk alleles. Application: genetic information is the only tool in risk prediction and management that is basically available at birth and could be used to predict risk and tailor prevention strategies. During life, the influence of risk factors and their management becomes increasingly important. In older adults, imaging to detect atherosclerosis and its complications remains the main diagnostic tool. Over time, the strategy shifts from prevention in young and middle-aged subjects to treatment and secondary prevention. However, genetic information could, be used at all stages to predict risk in young and middle-aged individuals (1), evaluate the beneficial effects of lifestyle but also pharmacological interventions (2), and predict the response to a given treatment strategy (e.g., statins or PCSK9 inhibitors) (3). For details, see text. Modified image material available at Servier Medical Art under a Creative Commons Attribution 3.0 Unsupported License. Abbreviations as in Figure 1.