First, the power to discover an association in a genetic study depends on the effect size and frequency of the variant29. This dependence means that the most significant associations tend to be more common in the populations in which they are discovered than elsewhere13,30. For example, GWAS catalog variants are more common on average in European populations compared to East Asian and African populations (Figure 2B), an observation not representative of genomic variants at large. Understudied populations offer low-hanging fruit for genetic discovery because variants that are common in these groups but rare or absent in European populations could not be discovered even with very large European sample sizes. Some examples include SLC16A11 and HNF1A associations with type II diabetes in Latino populations, as well as APOL1 associations with end-stage kidney disease and associations with prostate cancer in African descent populations31–34. If we assume that causal genetic variants have an equal effect across all populations—an assumption with some empirical support that offers the best case scenario for transferability35–40—Eurocentric GWAS biases mean that variants associated with risk are disproportionately common and
