SummaryThe genomic control (GC) approach is extensively used to effectively control false positive signals due to population stratification in genome-wide association studies (GWAS). However, GC affects the statistical power of GWAS. The loss of power depends on the magnitude of the inflation factor (λ) that is used for GC. We simulated meta-analyses of different GWAS. Minor allele frequency (MAF) ranged from 0·001 to 0·5 and λ was sampled from two scenarios: (i) random scenario (empirically-derived distribution of real λ values) and (ii) selected scenario from simulation parameter modification. Adjustment for λ was considered under single correction (within study corrected standard errors) and double correction (additional λ corrected summary estimate). MAF was a pivotal determinant of observed power. In random λ scenario, double correction induced a symmetric power reduction in comparison to single correction. For MAF <5%, GC significantly reduced power for genetic risks ranging from 1·2 to 1·4 (n = 10–20). Rising MAF attenuated the correction effect of λ adjustment. Moderate λ approach yielded more conservative results for population stratification adjustment, especially for MAF <5%. Large λ approach yielded an approximate two fold decrease in power when compared to moderate λ approach and almost four fold when the original random λ scenario was considered. Meta-analysis power can be adequate to detect significant variants even for double GC correction when effect size exceeds >1·2 and MAF >5%. Our results provide a quick but detailed index for power considerations of future meta-analyses of GWAS that enables a more flexible design from early steps based on the number of studies accumulated in different groups and the λ values observed in the single studies.