PRS-CS19 is a Bayesian polygenic prediction method that infers posterior SNP effect sizes from summary statistics using a continuous shrinkage prior, which is robust to varying genetic architectures, accurate in LD modeling and computationally efficient. PRS-CS has one hyper-parameter, the global shrinkage parameter, which models the overall sparseness of the genetic architecture. We used default parameter settings and the pre-calculated 1KG LD reference panel (EUR, EAS, AFR or AMR) that matched the ancestry of the discovery sample, which was constructed for HapMap3 variants with MAF >1%. The global shrinkage parameter among 10−6, 10−4, 10−2 and 1.0 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.
