Extended Data Fig. 2. PGS performance varies across GD in simulations using CB and NG as training data (${\mathbf{h}}_{\mathbf{g}}^{\mathbf{2}}\mathbf{=}\mathbf{0.8}$ and ${\mathbf{p}}_{\mathbf{causal}}=\mathbf{0.1}\mathbf{\%}$).

(a) The coverage of the 90% credible intervals of genetic liability (CI-g_i) is approximately uniform across testing individuals at all GDs. The red dotted line represents the expected coverage of 90% CI-g_i. Each dot represents a randomly selected UKBB testing individual. For each dot, the x-axis is its GD from African training data, the y-axis is the empirical coverage of 90% CI-g_i calculated as the proportion of simulation replicates where the 90% credible intervals contain the individual’s true genetic liability, and the error bars represent mean ±1.96 standard error of the mean (s.e.m) of the empirical coverage calculated from 100 simulations. (b) The width of 90% CI-g_i increases with GD. For each dot, the y-axis is the average width of 90% CI-g_i across 100 simulation replicates, and the error bars represent ±1.96 s.e.m. (c) Individual PGS accuracy decreases with GD. For each dot, the y-axis is the average individual level PGS accuracy across 100 simulation replicates, and the error bars represent ±1.96 s.e.m. (d) Population-level metrics of PGS accuracy recapitulates the decay in PGS accuracy across genetic continuum. All UKBB testing individuals are divided into 100 equal-interval bins based on their GD. The x-axis is the average GD for the bin and the y-axis is the squared correlation between genetic liability and PGS estimates for the individuals within the bin. The dot and error bars represent the mean and ±1.96 s.e.m from 100 simulations.