Fig. 5

Plot showing the inverse relationship of accuracy and coverage across different \(r^{2}\) and \(R^{2}\) thresholds for PRED-LD (combined and separate panels) and the compared tools, respectively. The results are obtained from the GWAS datasets as described in the text. We show the mean and the standard error of the mean for the predictions across the different datasets. PRED-LD allows both for a definition of a strict LD threshold or a lower one, resulting either in a more accurate imputation or a broader coverage, respectively. RAISS achieves high accuracy, but with the least coverage across all choices of thresholds. FAPI exhibits high \(R^{2}\), but with more limited coverage compared to SSIMP, DIST and PRED-LD. Notably, SSIMP offers the highest accuracy and coverage ratio for \(R^{2}\) > 0.5 and \(R^{2}\) > 0.6 at the expense of execution time (see also Table 4). DIST delivers the best coverage overall, at the expense of the lowest \(R^{2}\) among the tools evaluated