Large-scale genetic association studies aim to identify genetic factors underlying common diseases. However, population structure can lead to false positives or missed associations. This study examines the impact of population structure on association results using genome-wide SNPs in three groups: European Americans, African Americans, and Asians. Population structure significantly affects study outcomes, especially with larger sample sizes. Even modest levels of structure within groups can be problematic for detecting genetic effects. Genomic Control is a method to correct for population structure, but it may not work well if too few loci are used, leading to overcorrection and loss of power. The study shows that population structure can cause test statistics to be conservative or anticonservative, affecting the reliability of results. For example, in small samples, the chi-squared test is conservative, potentially missing real effects. In larger samples, the effects of structure become more severe. The study used Bayesian methods to model population structure and found that the level of structure between populations is comparable to that in mixed European populations. Differences in disease prevalence between subpopulations can also affect results. The study highlights the importance of accounting for population structure in association studies to avoid false positives and missed associations. Genomic Control is effective when sufficient loci are used, but may not be adequate for extreme population structures. The results emphasize the need for careful study design and correction methods to ensure accurate genetic association findings.Large-scale genetic association studies aim to identify genetic factors underlying common diseases. However, population structure can lead to false positives or missed associations. This study examines the impact of population structure on association results using genome-wide SNPs in three groups: European Americans, African Americans, and Asians. Population structure significantly affects study outcomes, especially with larger sample sizes. Even modest levels of structure within groups can be problematic for detecting genetic effects. Genomic Control is a method to correct for population structure, but it may not work well if too few loci are used, leading to overcorrection and loss of power. The study shows that population structure can cause test statistics to be conservative or anticonservative, affecting the reliability of results. For example, in small samples, the chi-squared test is conservative, potentially missing real effects. In larger samples, the effects of structure become more severe. The study used Bayesian methods to model population structure and found that the level of structure between populations is comparable to that in mixed European populations. Differences in disease prevalence between subpopulations can also affect results. The study highlights the importance of accounting for population structure in association studies to avoid false positives and missed associations. Genomic Control is effective when sufficient loci are used, but may not be adequate for extreme population structures. The results emphasize the need for careful study design and correction methods to ensure accurate genetic association findings.