LDpred220, an improved version of the LDpred algorithm21, is a Bayesian polygenic prediction method that adjusts marginal SNP effect size estimates from GWAS summary statistics to calculate the PRS. LDpred2 assigns a point-normal prior to SNP effect sizes, where the proportion of causal variants is a tunable parameter, and infers posterior effects using a Gibbs sampler. We constrained the computation to HapMap3 variants with MAF >1%, and used 1KG super-population samples (EUR, EAS, AFR or AMR) whose ancestry matched the discovery sample as the LD reference panel. We ran LDpred2-grid using the genome-wide option with the full LD matrix, and tested the proportion of causal variants from a sequence of 17 values equally spaced from 10−4 to 1.0 on the log scale. The proportion that maximized the R2 in the validation dataset was selected, and used in the independent testing dataset to calculate the final PRS and its performance metrics.
