We estimated the cumulative contribution of SNPs to polygenic risk of schizophrenia using a series of leave-one-out polygenic prediction analyses based on LD-clumping and P-value thresholding (P+T)14 (also known as C+T) using PLINK11. For calculating polygenic scores, we included the most significant SNP for any pair of SNPs within <500kb and with LD R2 >0.1. We included only those with minor allele frequency >1%. We considered a range of P-value thresholds; 5×10−8, 1×10−6, 1×10−4, 1×10−3, 1×10−2, 5×10−2, 1×10−1, 2×10−1, 5×10−1 and 1.0. We performed logistic regression analysis within each case-control sample, to assess the relationship between case status and PRS (P+T) quantiles. The same principal components used for each GWAS were used as covariates for this analysis. Whenever the number of controls at a quantile was fewer than 5 times the number of covariates15, or if the higher bound for the PRS Odds Ratio (OR) became infinity, Firth’s penalised likelihood method was used to compute regression statistics, as implemented in the R package “logistf”16. ORs from these calculations were then meta-analysed using a fixed-effects model in the R package “metafor”17.
