Regarding polygenic scoring practices and as stated above, it is typical for researchers to separate samples into more ancestrally homogeneous groups prior to polygenic scoring analyses; Fig. 3 demonstrates why this is one sensible analytical choice. However, this is not always done. Some researchers are unaware of the extent to which ancestry can impact variant frequencies, and others may choose not to split samples because there is no clear choice regarding how multiple admixed and/or similar populations should be split. In all instances, proper use of principal components (PCs) or other methods of correcting for ancestry is critical. Further, Fig. 3 implies that there is no single recommendation for the number of PCs needed, given that PCs correlate with 1000Genomes populations (see Supplementary Fig. 3). Underscoring this point, Supplementary Fig. 4 shows the magnitude of correlations between 1000Genomes participants’ polygenic scores (for height, BMI, and schizophrenia) and the first 20 PCs (N = 2577; see Supplementary Fig. 5 for representative scatterplots and Methods for additional details). Two key conclusions can be drawn from these figures. It is important that multiple,
