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 context of discovery GWAS sample size (N), the number of presumed independent causal loci (M~70,000) and SNP-h2 (Daetwyler et al., 2008, Dudbridge, 2013). Simulations from these studies are sobering. For instance, in their GWAS meta-analysis of educational attainment, Rietveld and colleagues (Rietveld et al., 2013) concluded that a discovery GWAS of 1 million individuals for this phenotype might generate a polygenic score that explained 15% of the variance in the same phenotype in a new sample. The second consideration is whether a target sample size is large enough to rule out false negatives or positives. PRS analyses are often simple regressions, and typically account for 0.01% (conservatively) to 3% (optimistically) of variance in cross-trait prediction (e.g., schizophrenia PRS predicting task performance among healthy individuals; Figure 2, Tables 1–6). As such with optimistic estimates, target data sample sizes of at least 300 are needed to detect 3% of variation with 80% power. Samples of 800, 8,000, and 80,000 would be needed
