cover all methods that sum genetic data to provide individual risk measures and will assume that these are transformed to have a standard normal distribution. The measures used to assess the predictive ability of a PRS are summarised in Table 1. Table 1Assessing the clinical utility of polygenic risk scoresA: Population level The predictive ability of polygenic risk scores can be measured in research studies, where differences between cases and controls (Fig. 1) or of a continuous trait in a population are assessed. Here, the disease status or trait is pre-established, and the studies measure the extent to which this is determined by the PRS. Outcome measures from such studies include: (1) R2 from linear regression, which quantifies the proportion of variance in a continuous trait captured by the PRS, or equivalently Nagelkerke’s R2 for logistic regression for case-control disease status. (2) R2 on a liability scale, which transforms Nagelkerke’s R2 to reflect disease prevalence, instead of the case-control ratio of the research study [15]. (3) The area under the receiver operating characteristic curve (AUC) [16], which takes a value from 0.5 to 1. This gives an overall summary of the predictive ability of the model. It is most easily
