Table 2:

Summary of the relative performance of constituent PRS methods.

LD	BOLT-LMM	SBayesR	PRS-CS	Figure(s)/Table(s)
Individual-level data (UKB, N=337K)	✔✔	✔	✔	Figures 3,4,6
In-sample LD (UKB, N=337K)	---	✔✔	✔	Figures 3,4,6
Very large unmatched LD (UKB, N=337K)	---	✔	✔✔	Extended Data Figure 1
Small unmatched LD (1000G, N=489)	---	✘	✔✔*	Tables S4–S6

For each of three constituent PRS methods (BOLT-LMM, SBayesR, PRS-CS) we report its relative performance in prediction in UK Biobank non-British Europeans under various settings; we also provide links to the corresponding Figure(s)/Table(s). ✔✔: the method is significantly more accurate than the second best method in the same row, and combining this method with PolyFun-pred increases prediction accuracy; ✔✔*: the method is significantly more accurate than the second best method in the same row, and combining this method with PolyFun-pred does not increase prediction accuracy; ✔: the method is significantly less accurate than the best method in the same row, but is significantly more accurate than P+T; ✘: the method is not significantly more accurate than P+T; ---: the method is not applicable, because it requires individual-level data. For individual-level data, the difference between BOLT-LMM and the second-best method was significant in simulations but non-significant in real trait analyses. For In-sample LD, the difference between SBayesR and PRS-CS was significant in simulations but non-significant in real traits analyses. For Very large unmatched LD (a likely scenario when analyzing summary statistics from a meta-analysis of many cohorts), we performed real trait analyses only, as simulations would have required another very large individual-level data set in addition to UK Biobank (see Supplementary Note). For small unmatched LD, we performed both simulations and real trait analyses but report results of real trait analyses, which we believe to be most reflective of real-life settings (in simulations, SBayesR was significantly more accurate than PRS-CS; see Supplementary Note). Results for non-European target populations from UK Biobank were similar, though some of the differences were not statistically significant due to smaller prediction accuracies and sample sizes. We have facilitated the use of very large LD reference panels for European training data by publicly releasing summary LD information for N=337K British-ancestry UK Biobank samples across 18 million SNPs (see Data availability).