Table 1.

Overview of the compared data-combining approaches and data utilization

Combining approach	Individual-level data	GWAS summary statistics	Combining strategy	Validation	Test
Meta-GWAS	GWAS	–	$PRS={\sum }_{i=1}^M,Z_i\cdot x_i,$ $Z_i=\frac{\sqrt{n_{int}}\cdot z_{int}+\sqrt{n_{ext}}\cdot z_{ext}}{\sqrt{n_{int}+n_{ext}}}$	select PRS parameters	assess PRS prediction accuracy
SCT	penalized regression of C+T scores	grid C+T scores	$PRS=\sum _{j=1}^k{w}_j\cdot PR{S}_j$	not used
Meta-PRS	derive $PR{S}_{int}$	derive $PR{S}_{ext}$	$PRS=w_{int}\cdot PR{S}_{int}+w_{ext}\cdot PR{S}_{ext}$	select PRS parametersa

Abbreviations: M, number of SNPs; Z, SNP effect size; x, SNP effect allele count; n, effective sample size $n_{eff}=4/\left(1/n_{ca}+1/n_{co}\right)$; int, internal data; ext, external data; k, number of PRSs in grid; w, weights (either regression coefficients or square root of training sample size).

^aWhen the weights for meta-PRS were obtained with linear regression, the validation dataset was also used to train the regression parameters. When the weights were obtained from the training sample size, the validation set was not used.