Table 1.

Overview of the methods implemented for PRS development

Method	Required training data sourcea	Features	Tuning parameters
Single-ancestry
C + T	target ancestry	model-free	$p_t$ (p value threshold)
LDpred2	target ancestry	Bayesian (spike-and-slab prior)	$p$ (causal SNP proportion), $h^2$ (heritability)
EUR C + T	EUR	model-free	$p_t$ (p value threshold)
EUR LDpred2	EUR	Bayesian (spike-and-slab prior)	$p$ (causal SNP proportion), $h^2$ (heritability)
Multi-ancestry
weighted LDpred2	ancestry-specific data from each available ancestry	Bayesian (spike-and-slab prior), linear combination strategy	$p,H^2$, weight of each ancestry-specific PRS in the final model
PRS-CSx	ancestry-specific data from each available ancestry	Bayesian (Strawderman-Berger prior), linear combination strategy	$\phi$ (global shrinkage parameter), weight of each ancestry-specific PRS in the final model
XPASSb	ancestry-specific data from each available ancestry	Bayesian (bivariate normal prior), infinitesimal model	–
PolyPred+b	ancestry-specific data from each available ancestry	Bayesian, functional annotation, linear combination of SBayesR and PolyFun	parameters in SBayesR and PolyFun, weight of SBayesR PRS and PolyFun PRS in the final model
CT-SLEB	ancestry-specific data from each available ancestry	empirical Bayes, EL via SL	$p_t$ (p value threshold), $d$ (genetic distance) for C + T step, parameters in the SL
MUSSEL	ancestry-specific data from each available ancestry	Bayesian (multivariate spike-and-slab prior), EL via SL	$\mathrm{Pr}\left({\delta }_1,\dots ,{\delta }_K\right),{\rho }_{k_1{k}_2},1\le k_1<k_2\le K$, parameters in the SL

^aAll methods require three datasets to train the PRS model: (1) discovery GWAS summary data, (2) LD reference data, and (3) tuning data.

^bResults from PolyPred+ and XPASS on all simulated and real datasets (except for PAGE + UKBB + BBJ) were reported in Zhang et al.¹³