Per-allele disease and complex trait effect sizes are predominantly African MAF-dependent in European populations

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 w=0.95 (95% CI: (0.93, 0.96)) and European MAF with weight (1-w) is a more powerful predictor than either European MAF (P<10-15, 3.65x greater increase in log-likelihood relative to a null model without MAF dependence) or African MAF (P<10-15). Next, we consider the widely used model, in which per-allele GWAS effect size variance is proportional to [(1 - )], where pE is the European MAF. We propose a different model in which per-allele effect size variance is proportional to [(1 - )], where pmix=w*pA+(1-w)*pE, and pA is the African MAF. We fit the mix 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 w and mix that best fit mean effect size variance estimates from S-LDSC across bivariate MAF bins. We demonstrate that our approach provides conservative estimates of w in simulations. We applied this approach to summary statistics for 50 diseases/complex traits in European populations (average N=483K) and estimated best-fit parameters of w=0.96 (95% CI: (0.76, 1.16)) and mix=-0.34 (95% CI: (-0.67, -0.02)), attaining a far better fit than the standard model using pE only (P<10-15, 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.