Table 1:

An overview of the methods implemented for PRS development.

Type	Method	Required training data source*	Features	Tuning parameters
Single-ancestry method	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 method	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		Bayesian (Strawderman–Berger prior), linear combination strategy	$\varphi$ (global shrinkage parameter), weight of each ancestry-specific PRS in the final model
	XPASS**		Bayesian (bivariate normal prior), infinitesimal model
	PolyPred+**		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		Empirical Bayes, EL via SL	$p_t$ (p-value threshold), $d$ (genetic distance) for C+T step, parameters in the SL
	MUSSEL		Bayesian (multi-variate spike-and-slab prior), EL via SL	$\text{Pr(}{\delta }_1,\dots ,{\delta }_K),\hspace{0.17em}{\rho }_{k_1{k}_2},1\hspace{0.17em}\le k_1\hspace{0.17em}<k_2\le \hspace{0.17em}K$, parameters in the SL

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

^**Results from PolyPred+ and XPASS on all simulated and real datasets (except for PAGE + UKBB + BBJ) were reported in Zhang et al. (2022)¹².