We used PRS-CSx, a recently developed Bayesian polygenic modeling method, to construct the trans-ancestry PRS [18]. PRS-CSx jointly models the three GWAS summary statistics and couples genetic effects across populations using a shared continuous shrinkage prior, which enables more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium (LD) diversity across discovery samples. The shared prior allows for correlated but varying effect size estimates across populations, retaining the flexibility of the modeling framework. In addition, PRS-CSx accounts for population-specific allele frequencies and LD patterns and inherits efficient and robust posterior inference algorithms (Gibbs sampler) from PRS-CS [24]. We used pre-computed 1000 Genomes Project (1KG) [25] reference panels that matched the ancestry of each discovery GWAS, and a fully Bayesian algorithm for model fitting, which automatically learned all model parameters from the summary statistics without the need for hyper-parameter tuning. Population-specific posterior effect size estimates were combined using an inverse-variance-weighted meta-analysis within the Gibbs sampler (via the “--meta” option provided by the software). The final PRS-CSx output included 1,259,754 HapMap3 variants and their weights, which can be applied to any genotyped individual not included in the discovery GWAS to calculate a polygenic risk score.
