We went on to conduct polygenic risk score analysis. Polygenic scores for CLOZUK were generated from INDEPENDENT PGC as a training set, using the same parameters for risk profile score (RPS) analysis in PGC5, arriving at a high-confidence set of SNPs for RPS estimation by removing the xMHC region and indels, and applying INFO > 0.9 and MAF > 0.1 cutoffs. Scores were generated from the autosomal imputation dosage data, using a range of P-value thresholds for SNP inclusion66 (5 × 10−8, 1 × 10−5, 0.001, 0.05 and 0.5). In this way, we can assess the presence of a progressively increasing signal-to-noise ratio in relation to the number of markers included67. As in the PGC study, we found the best P-value threshold for discrimination to be 0.05 and report highly significant polygenic overlap between the INDEPENDENT PGC and CLOZUK samples (P < 1 × 10−300, Nagelkerke r2 = 0.12; Supplementary Table 2), confirming the validity of combining the datasets. For comparison with other studies, we also report polygenic variance on the liability scale68, which amounted to 5.7% for CLOZUK at
