Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation 1 . Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature 2 – 5 , it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk 6 . We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues. Genome-wide polygenic risk scores derived from GWAS data for five common diseases can identify subgroups of the population with risk approaching or exceeding that of a monogenic mutation.