The standard practice in GWAS is not only to test directly genotyped markers but also to test unobserved markers where the genotypes are estimated using imputation. In addition to many quality control (QC) steps requiring homogenous samples, ancestry can influence imputation and some methods recommend within group imputation (MaCH, BEAGLE)11,12 while other methods, such as IMPUTE2, recommend using diverse reference panels.13 There are also methods specifically intended to be used in admixed populations such as MaCHadmix.14 Therefore the choice to perform imputation within or across groups will depend on the imputation method’s recommended practices. Regardless of method, imputation quality can vary across groups due to a variety of factors including reference panel diversity and single nucleotide polymorphism (SNP) array density, content, and design. Meta-analysis of data from diverse populations offers the opportunity to increase the power to detect loci through increased sample size and improve the resolution of fine-mapping of causal variants.15 It is expected that there will be some differences in causal loci between diverse populations and how well GWAS findings translate from one population to another depends on heterogeneity in allelic effects between distantly related populations.
