This review discusses the role of pleiotropy in Mendelian randomization (MR) studies, a method used to estimate causal relationships between traits using genetic variants. Pleiotropy, the phenomenon where a single genetic variant influences multiple traits, can complicate MR analyses. The review highlights the challenges posed by both vertical and horizontal pleiotropy, where vertical pleiotropy involves a single SNP influencing one trait, which in turn influences another, while horizontal pleiotropy involves the same SNP influencing both traits through independent pathways. Recent developments in MR methods aim to improve the reliability of causal inference by addressing these issues. The review outlines various statistical techniques, such as genetic colocalization methods, weighted inverse variance meta-analysis (IVW), MR-Egger regression, and outlier removal strategies, which can be used to distinguish between vertical and horizontal pleiotropy. It also discusses the use of polygenic risk scores, multivariable MR, negative controls, and coherent frameworks to enhance the robustness of MR analyses. The review emphasizes the importance of methodological advancements and the integration of multiple statistical tools to improve the accuracy of causal inference in MR studies.This review discusses the role of pleiotropy in Mendelian randomization (MR) studies, a method used to estimate causal relationships between traits using genetic variants. Pleiotropy, the phenomenon where a single genetic variant influences multiple traits, can complicate MR analyses. The review highlights the challenges posed by both vertical and horizontal pleiotropy, where vertical pleiotropy involves a single SNP influencing one trait, which in turn influences another, while horizontal pleiotropy involves the same SNP influencing both traits through independent pathways. Recent developments in MR methods aim to improve the reliability of causal inference by addressing these issues. The review outlines various statistical techniques, such as genetic colocalization methods, weighted inverse variance meta-analysis (IVW), MR-Egger regression, and outlier removal strategies, which can be used to distinguish between vertical and horizontal pleiotropy. It also discusses the use of polygenic risk scores, multivariable MR, negative controls, and coherent frameworks to enhance the robustness of MR analyses. The review emphasizes the importance of methodological advancements and the integration of multiple statistical tools to improve the accuracy of causal inference in MR studies.