The article reviews recent advancements in methods for correcting population stratification in genome-wide association studies (GWAS). Population stratification, which can confound GWAS results, is addressed by methods that infer genetic ancestry, such as Structured Association and Principal Components Analysis (PCA). However, these methods are limited when family structure or cryptic relatedness is present. The authors discuss the limitations of these methods and introduce new approaches, including Family-Based Association Tests and Mixed Models, which can account for both population and family structure. They evaluate the performance of these methods through simulations and provide guidelines for their application. The article also highlights the importance of considering low-frequency and rare variants in future GWAS studies and concludes with recommendations for practical use of these methods.The article reviews recent advancements in methods for correcting population stratification in genome-wide association studies (GWAS). Population stratification, which can confound GWAS results, is addressed by methods that infer genetic ancestry, such as Structured Association and Principal Components Analysis (PCA). However, these methods are limited when family structure or cryptic relatedness is present. The authors discuss the limitations of these methods and introduce new approaches, including Family-Based Association Tests and Mixed Models, which can account for both population and family structure. They evaluate the performance of these methods through simulations and provide guidelines for their application. The article also highlights the importance of considering low-frequency and rare variants in future GWAS studies and concludes with recommendations for practical use of these methods.