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Understanding genetic architectures of disease is fundamental to partitioning heritability, polygenic risk prediction, and statistical fine-mapping. Genetic architectures of disease in European populations have been shown to depend on European minor allele frequency (MAF): SNPs with lower MAF have larger per-allele effects, due to the action of negative selection. However, we hypothesized that African MAF (defined using African-ancestry segments in African Americans), which is not distorted by the out-of-Africa bottleneck, might better predict per-allele effect sizes of common genetic variation in European populations; we note that common variants explaining most disease heritability are typically much older than the split between African and non-African populations. To demonstrate this, we first analyze the proportion of non-synonymous SNPs, which are strongly impacted by negative selection. The proportion of non-synonymous SNPs is much better predicted by African MAF than European MAF; a mixture of African MAF with weight (95% CI: (0.93, 0.96)) and European MAF with weight ( ) is a more powerful predictor than either European MAF (<10, 3.65x greater increase in log-likelihood relative to a null model without MAF dependence) or African MAF (<10). Next, we consider the widely used model, in which per-allele GWAS effect size variance is proportional to , where is the European MAF. We propose a different model in which per-allele effect size variance is proportional to , where , and is the African MAF. We fit the model by extending the baseline-LD model used in S-LDSC to include a grid of bivariate African and European MAF bins and identifying values of and that best fit mean effect size variance estimates from S-LDSC across bivariate MAF bins. We demonstrate that our approach provides conservative estimates of in simulations. We applied this approach to summary statistics for 50 diseases/complex traits in European populations (average =483K) and estimated best-fit parameters of (95% CI: (0.76, 1.16)) and (95% CI: (-0.67, -0.02)), attaining a far better fit than the standard model using only (10, 4.53x greater decrease in mean-squared error relative to a null model without MAF dependence). We conclude that per-allele disease and complex trait effect sizes are predominantly African MAF-dependent in European populations.