This review underscores the rational range of effect sizes that should be considered when determining sample sizes that would be amenable to PRS analyses (Figure 2). There are two issues to consider here: how predictive is a PRS, and how large of a target sample will usefully detect the effects of that PRS on an outcome. Several technical papers discuss the predictive utility of PRS - So and Sham (So and Sham, 2017) found that SCZ PRS (Schizophrenia Working Group of the Psychiatric Genomics, 2014) had the greatest predictive power of all psychiatric disorders (max Area Under the Curve, AUC = 0.82), which was even higher than some health-related traits [e.g., Type 2 Diabetes and Coronary Artery Disease max AUC = 0.61 (So and Sham, 2017)]. Predictive power for psychiatric disorder PRS appears to increase at more inclusive p-value thresholds, unlike metabolic traits, which is consistent with a greater degree of expected polygenicity. Further, psychiatric PRS are modestly superior in performance when drawn from studies with greater phenotypic precision. Other expositions on the topic have considered R2 estimation in the
