Coronary artery disease (CAD) is a disease in which the blood supply to the heart is restricted by fatty deposits, making it the leading cause of mortality worldwide. In 2022, CAD affected 315 million people and led to 9.2 million deaths. CAD is associated with multiple factors, including genetics, age, gender, medical conditions and lifestyle features such as smoking, physical activity and alcohol consumption. In this context, genetics refers to the unique biological blueprint inherited at birth, which determines how our body’s components are constructed and function. Although effective treatments and diagnosis tools have been developed over the past decades to address CAD, aging populations, immigration and unhealthy lifestyles continue to fuel this widespread disease. In 2007, a new diagnostic tool called polygenic risk scores (PRS) emerged. PRS is used to predict an individual’s risk of developing CAD by analyzing their genetic profile. This paper contributes to tackling CAD by comparing current used risk predictors with PRS. Currently used risk prediction methods assess a patient’s likelihood of developing CAD based on a sum of risk factors, providing a probability score for CAD. Current used risk methods perform reasonably well, by predicting between 70-80% of the people correctly. Although these scores are effective, PRS could add a certain value if it manages certain limitations: there’s a lack of ethical guidelines, standardized PRS construction and non-European population test data. If PRS proves effective at younger ages and progresses throughout the years, it could help earlier identification of high-risk patients, potentially preventing CAD.