The advent of precision medicine largely depends on the creation of precise and accurate predictive tools. While most late-onset diseases are moderately to highly heritable, using genetic information to make individual predictions about risk of disease has proven to be challenging. Recent technological and methodological advances have established the importance of the polygenic model of inheritance for complex diseases, whereby dozens of thousands of functional variants might contribute to disease risk. Genome-wide association studies have identified thousands of variants associated with disease, yet each individual variant tends to only have a modest effect on risk. We and others have proposed using polygenic risk scores combining information from hundreds to millions of variants to improve genetic risk prediction and identify individuals at very high risk of disease. We illustrate this approach by studying coronary artery disease (CAD), a leading cause of mortality worldwide. A genetic cause is identified in less than 5% of patients with early onset disease, mainly Mendelian mutations leading to Familial Hypercholesterolemia. Our data show that belonging to the extreme tail of polygenic risk is associated with a greater risk of CAD than carrying a Mendelian mutation, both in terms of risk and prevalence. Indeed, more individuals are at high risk of CAD because of polygenic risk, and such individuals have higher risk than carriers of routinely tested Familial Hypercholesterolemia mutations. Similar findings have been reported for other common diseases such as breast and prostate cancer, among others. These findings support clinical use of polygenic risk scores for common complex disease in the near future.
1. Understand the polygenic nature of complex traits
2. Understand principles of polygenic risk scores
3. Clinical applications of polygenic risk scores