Second, LD, the correlation structure of the genome, varies across populations due to demographic history (Figure 2A,C–E). These LD differences in turn drive differences in effect size estimates (i.e. predictors) from GWAS across populations in proportion to LD between tagging and causal SNP pairs, even when causal effects are the same35,37–40 (Supplementary Note). Differences in effect size estimates due to LD differences may typically be small for most regions of the genome (Figure 2C–E), but PRS sum across these effects, also aggregating these population differences. While it would be ideal to use causal effects rather than correlated effect size estimates to calculate PRS, it may not be feasible to fine-map most variants to a single locus to solve issues of low generalizability, even with very large GWAS. This is because complex traits are highly polygenic, meaning most of our prediction power comes from small effects that do not meet genome-wide significance and/or cannot be fine-mapped, even in many of the best-powered GWAS to date42.
