Much of the research to motivate moving polygenic risk scores from research studies to clinical implementation comes from cardiovascular disease, type 2 diabetes, breast and prostate cancers, and Alzheimer’s disease [47]. Khera et al. [2] recently demonstrated in the UK Biobank that PRS can identify which percentage of the sample have at least 3-fold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, with the proportion of individuals identified varying between 1.5 and 8% depending on the disorder. Although these effects appear modest, PRS can identify substantial larger fractions of the population at high disease risk than monogenic mutations, making PRS potentially more clinically relevant. Apart from the generic prediction of case-control status, specific applications of PRS have been proposed. For example, the PRSs of left ventricular cardiovascular magnetic resonance phenotypes are predictive of heart failure events independently of clinical risk factors in the UK Biobank [48], whilst elevated genetic score for albuminuria is strongly associated with increased risk of hypertension [49].
