Metrics for Evaluating Polygenic Risk Scores

Abstract

There is growing interest in the use of polygenic risk scores based on genetic variants to predict cancer incidence. The type of metric used to evaluate the predictive performance of polygenic risk scores plays a crucial role in their interpretation. I compare 3 metrics for this evaluation: the area under the receiver operating characteristic curve (AUC), the probability of cancer in a high-risk subset divided by the prevalence of cancer in the population, which I call the subset relative risk (SRR), and the minimum test tradeoff, which is the minimum number of genetic variant ascertainments (one per person) for each correct prediction of cancer to yield a positive expected clinical utility. I show that SRR is a relabeling of AUC. I recommend the minimum test tradeoff for the evaluation of polygenic risk scores because, unlike AUC and SRR, it is directly related to the expected clinical utility.