Consistent with their widespread use, PRS approaches offer many strengths. First, they model psychiatric disease liability within a polygenic framework. Second, they are easy to compute and analyze making them amenable to research extensions. Relatedly, their results are intuitive and readily interpretable by a large audience. Third, while PRS require large discovery samples, they can be tested within much smaller samples, making them highly appealing to studies with deeper phenotyping on fewer subjects (e.g., neuroimaging). How small is large enough? This will depend upon the precision of PRS and target sample phenotypes and questions. However, given that current observed effects from cross-trait associations suggest that PRS predict 0.01–3.00% of variance across traits, sample sizes would need to be >300 given optimistic estimates of effect size (3%), and in all likelihood, much larger (see Technical Considerations; Figure 2). However, the application of novel techniques such as LDpred and MTAG may increase their precision and allow for their application in smaller samples (Vilhjalmsson et al., 2015, Turley et al., 2017). Regardless, research using smaller samples should consider the possibility of false positive
