Although admixture mapping can detect genes/variants that may not have been identified by current GWAS, it has several limitations. First, as shown in the ADH1B gene, multiple ancestry-specific disease-causing variants could be located in the same ancestry block, which limits the ability of admixture mapping to detect them. Second, ancestry blocks are determined by relatively common variants. If disease-causing variants have low MAF, e.g., rs72900220 identified by Kranzler et al (2019) (Kranzler et al., 2019), then larger sample sizes and much smaller blocks are needed to detect them in admixture mapping. Third, we performed a power analysis using QuantoV1.2.4 (Gauderman, 2002), assuming a MAF of 30% and the same sample size as in this study. We estimate 80% power to detect an odds ratio > 1.3 and change of score > 0.4 for binary and continuous traits, respectively. To detect variants with smaller effect, a larger sample size would be needed. Fourth, due to the design of admixture mapping and the complex LD patterns in admixed populations, no gene- or set-based tests could be performed. While large-scale GWAS will still
