This review discusses the challenges and strategies for identifying and interpreting pleiotropy in complex traits, which refers to the phenomenon where a single genetic variant influences multiple traits. Genome-wide association studies (GWASs) have identified numerous genetic variants associated with multiple traits, particularly in autoimmune diseases, cancers, and neuropsychiatric disorders. These findings suggest that pleiotropy is widespread in human complex traits. However, detecting and interpreting pleiotropic effects remains challenging, requiring new methodologies and frameworks. The review outlines the evidence for pleiotropy in genetic mapping studies, analytical approaches to identify pleiotropic effects, sources of spurious cross-phenotype effects, and study design considerations. It also discusses the molecular and clinical implications of such findings and future research directions. The review highlights that many genetic loci appear to harbor variants associated with multiple, sometimes seemingly distinct traits, known as cross-phenotype (CP) associations. Examples include PTPN22 for immune-related disorders, TERT-CLPTM1L for cancers, and CACNA1C for bipolar disorder and schizophrenia. These associations suggest shared genetic pathways and the relevance of pleiotropy in human complex diseases. The distinction between CP associations and pleiotropy is important, as CP associations occur when a genetic locus is associated with multiple traits, while pleiotropy occurs when a genetic locus truly affects multiple traits. The review also discusses the sources of spurious pleiotropy, such as ascertainment bias, phenotypic misclassification, and shared controls. It outlines analytical strategies for detecting CP effects, including multivariate and univariate approaches, and discusses the clinical implications of CP associations. The review emphasizes the importance of distinguishing between biological and spurious pleiotropy, as well as the need for further research to understand the underlying mechanisms and implications of pleiotropy in complex traits and diseases.This review discusses the challenges and strategies for identifying and interpreting pleiotropy in complex traits, which refers to the phenomenon where a single genetic variant influences multiple traits. Genome-wide association studies (GWASs) have identified numerous genetic variants associated with multiple traits, particularly in autoimmune diseases, cancers, and neuropsychiatric disorders. These findings suggest that pleiotropy is widespread in human complex traits. However, detecting and interpreting pleiotropic effects remains challenging, requiring new methodologies and frameworks. The review outlines the evidence for pleiotropy in genetic mapping studies, analytical approaches to identify pleiotropic effects, sources of spurious cross-phenotype effects, and study design considerations. It also discusses the molecular and clinical implications of such findings and future research directions. The review highlights that many genetic loci appear to harbor variants associated with multiple, sometimes seemingly distinct traits, known as cross-phenotype (CP) associations. Examples include PTPN22 for immune-related disorders, TERT-CLPTM1L for cancers, and CACNA1C for bipolar disorder and schizophrenia. These associations suggest shared genetic pathways and the relevance of pleiotropy in human complex diseases. The distinction between CP associations and pleiotropy is important, as CP associations occur when a genetic locus is associated with multiple traits, while pleiotropy occurs when a genetic locus truly affects multiple traits. The review also discusses the sources of spurious pleiotropy, such as ascertainment bias, phenotypic misclassification, and shared controls. It outlines analytical strategies for detecting CP effects, including multivariate and univariate approaches, and discusses the clinical implications of CP associations. The review emphasizes the importance of distinguishing between biological and spurious pleiotropy, as well as the need for further research to understand the underlying mechanisms and implications of pleiotropy in complex traits and diseases.