differences between the discovery and target data. The standard PRS approach is to calculate several scores from SNPs meeting various p-value thresholds on a log scale ranging from genome-wide significant (p < 5e-8) to all independent SNPs (p < 1), then compute and report accuracy for each PRS4. In nearly all modern GWAS of complex traits, PRS computed using permissive p-value thresholds that aggregate the effects of 1,000s to 100,000s of independent SNPs typically explain more phenotypic variation than loci strictly meeting genome-wide significance. For example, no single common variant explains more than 0.1% of schizophrenia risk; however, ~10,000 SNPs together explain 18% of the variance between schizophrenia cases and controls, whereas genome-wide significant variants explain only 3% of risk (11). Table 1 describes several genetic risk prediction methods that have been developed and applied across diseases (15).
