The article presents two methods, compressed mixed linear model (compressed MLM) and population parameters previously determined (P3D), to improve the efficiency and statistical power of genome-wide association studies (GWAS). Compressed MLM reduces computational burden by clustering individuals into groups based on kinship, thereby decreasing the effective sample size. P3D eliminates the need to recompute variance components for each marker, improving computational efficiency. Both methods were tested on datasets from humans, dogs, and maize, showing significant reductions in computing time while maintaining or improving statistical power. The joint use of these methods further enhances performance. The compressed MLM is a flexible extension of pedigree-based sire models, allowing for variable group sizes and optimizing accuracy. P3D, a two-step approach, avoids iteration for population parameter estimation, maintaining statistical power. Both methods were implemented in the TASSEL software. The study demonstrates that these methods significantly reduce computing time, making large-scale GWAS more feasible. The results show that compressed MLM and P3D are effective in controlling false positives and maintaining statistical power across different genetic architectures and species. The methods are particularly useful for large datasets, enabling faster analysis of GWAS with high marker numbers. The study highlights the importance of computational efficiency in GWAS and provides practical tools for improving the analysis of genetic data.The article presents two methods, compressed mixed linear model (compressed MLM) and population parameters previously determined (P3D), to improve the efficiency and statistical power of genome-wide association studies (GWAS). Compressed MLM reduces computational burden by clustering individuals into groups based on kinship, thereby decreasing the effective sample size. P3D eliminates the need to recompute variance components for each marker, improving computational efficiency. Both methods were tested on datasets from humans, dogs, and maize, showing significant reductions in computing time while maintaining or improving statistical power. The joint use of these methods further enhances performance. The compressed MLM is a flexible extension of pedigree-based sire models, allowing for variable group sizes and optimizing accuracy. P3D, a two-step approach, avoids iteration for population parameter estimation, maintaining statistical power. Both methods were implemented in the TASSEL software. The study demonstrates that these methods significantly reduce computing time, making large-scale GWAS more feasible. The results show that compressed MLM and P3D are effective in controlling false positives and maintaining statistical power across different genetic architectures and species. The methods are particularly useful for large datasets, enabling faster analysis of GWAS with high marker numbers. The study highlights the importance of computational efficiency in GWAS and provides practical tools for improving the analysis of genetic data.