bins in the total PGC-SCZ data (Supplementary Table 5, Figure 1c). This low contribution to the total variance explained is likely to partly reflect under-representation of SNPs with low MAF in the analysis (minimum MAF 0.01) relative to those in the genome. The other four MAF bins explain approximately equal proportions of the variance, ~5% (s.e. 1%) each. Analyses of the PGC-SCZ subsets were consistent with these results (Supplementary Table 5). Based on the known relationship between allele frequencies and LD19, it is highly unlikely that the estimates of h2 reported here are caused predominantly by rare causal variants20. We performed simulations conditional on PGC-SCZ data and confirmed that a rare variants only model could not explain our results. For example, in an analysis of PGC-SCZ data using only SNPs with MAF > 0.4, 11% (s.e. 1%) of the variance in liability was explained, which is nearly half of the variance explained by all SNPs. However, in simulations which attributed 50% of variation in liability to SNPs with MAF < 0.1, SNPs with MAF > 0.4 explained only 5% (s.e. 0.3%) of the variance, which is only 10% of the variation explained by all SNPs (Figure 1c,d; Supplementary Tables 5–6).
