Table 1 ∣.

Polygenic prediction methods integrating GWAS summary statistics from diverse populations

Category	Method	Input	Variants for prediction	Need validation data set	Tuning parameters	Algorithm	Ref.
Combining approaches	Meta-analysis	Population-specific GWAS and LD	Up to all 1000 Genomes common variants	Optional	Depends on the single-population PRS method	Fixed-effect meta-analysis and a single-population PRS method	62
	ShaPRS	Population-specific GWAS and LD	Up to all 1000 Genomes common variants	Optional	Depends on the single-population PRS method	Meta-analysis accounting for heterogeneity between GWAS and a single-population PRS method	64
	MultiPRS	Population-specific GWAS and LD	Up to all 1000 Genomes common variants, depending on the C + T parameters	Yes	C + T parameters and linear combination weights for population-specific PRS	C + T	67
Joint modelling of two populations	XP-BLUP	GWAS in the auxiliary population; individual-level data for the target population	Genotyped SNPs in the target data set	Yes	P-value threshold for the auxiliary GWAS (optional)	Local FDR for variant selection; restricted maximum likelihood (ReML) for model fitting; best linear unbiased prediction	71
	XPASS(+)	GWAS and LD from the auxiliary and target populations	All available variants	No	None	Best linear unbiased prediction and conjugate gradient for solving linear systems	72
	BridgePRS	GWAS and LD from the auxiliary and target populations; individual-level data for the auxiliary population	All available variants	Yes	Ridge shrinkage parameters and linear combination weights for PRS generated under different prior parameters and loci selection criteria	Best linear unbiased prediction	73
	TL-Multi	GWAS and LD from the auxiliary and target populations	HapMap3	Optional	Regularization parameters in LASSO	Coordinate descent for model fitting	74
	SDPRX	GWAS and LD from the auxiliary and target populations	HapMap3	Yes	Linear combination weights for population-specific PRS	Markov chain Monte Carlo	76
Joint modelling of two or more populations	CT-SLEB	Population-specific GWAS and LD	Up to all 1000 Genomes common variants, depending on the C + T parameters	Yes	C + T parameters and parameters in the super learning model (such as LASSO, ridge regression and neural networks)	Two-dimensional C + T; empirical Bayes (for SNP effect estimation); super learning (for combining PRSs generated under different C + T parameters)	41
	TL-PRS/MTL-PRS	Population-specific GWAS and LD	HapMap3	Yes	Learning rate and number of iterations in the gradient descent algorithm	Gradient descent for model fitting	77
	PROSPER	Population-specific GWAS and LD	HapMap3 + MEGA chip array	Yes	Regularization parameters in LASSO, ridge regression and LD matrix estimation; tuning parameters in the ensemble regression	Coordinate descent and super learning (for combining PRSs generated under different tuning parameters)	78
	ME-Bayes SL	Population-specific GWAS and LD	HapMap3 + MEGA chip array	Yes	Parameters in the super learning model (such as linear regression, ridge regression and elastic net)	LDpred2 (for estimating causal SNP proportions and heritability); ME-Bayes (for SNP effect estimation); super learning (for combining PRSs generated under different ME-Bayes parameters)	79
	PRS-CSx(-auto)	Population-specific GWAS and LD	HapMap3	Optional	The global shrinkage parameter and linear combination weights for population-specific PRS; none for the auto algorithm	Markov chain Monte Carlo	68
Incorporating information beyond GWAS	XPXP	Population-specific GWAS (for both the target trait and its genetically correlated traits) and LD	All available variants	No	None	Best linear unbiased prediction and conjugate gradient for solving linear systems	81
	X-Wing	Population-specific GWAS and LD	HapMap3	No	None	Scan statistics (for local genetic correlation estimation); Markov chain Monte Carlo (for PRS model fitting); summary statistics based repeated learning (for combining population-specific PRSs)	82
	PolyPred-S+/PolyPred-P+	Population-specific GWAS and LD; functional annotations	All available variants for PolyFun-pred; HapMap3 for SBayesR and PRS-CS	Yes	Linear combination weights for population-specific PRS	PolyFun + SuSiE (for fine-mapping informed PRS) and Markov chain Monte Carlo (for SBayesR and PRS-CS)	69

C + T, clumping and P-value thresholding; FDR, false discovery rate; GWAS, genome-wide association studies; LD, linkage disequilibrium; PRS, polygenic risk score.