The study aimed to identify genetic variants that influence multiple phenotypes and to interpret these associations in the context of causal and non-causal models. By comparing large genome-wide association studies (GWAS) of 42 traits or diseases, the authors identified 341 loci associated with multiple traits. Several loci, such as *SLC39A8*, showed associations with a large number of phenotypes, including schizophrenia and Parkinson's disease. The authors also used these loci to identify traits that share multiple genetic causes, such as the association between variants increasing risk of schizophrenia and inflammatory bowel disease. Additionally, they developed a method to identify pairs of traits that show evidence of a causal relationship, such as increased BMI causally increasing triglyceride levels. The study highlights the importance of considering multiple traits when interpreting the molecular consequences of genetic variants and designing experimental studies.The study aimed to identify genetic variants that influence multiple phenotypes and to interpret these associations in the context of causal and non-causal models. By comparing large genome-wide association studies (GWAS) of 42 traits or diseases, the authors identified 341 loci associated with multiple traits. Several loci, such as *SLC39A8*, showed associations with a large number of phenotypes, including schizophrenia and Parkinson's disease. The authors also used these loci to identify traits that share multiple genetic causes, such as the association between variants increasing risk of schizophrenia and inflammatory bowel disease. Additionally, they developed a method to identify pairs of traits that show evidence of a causal relationship, such as increased BMI causally increasing triglyceride levels. The study highlights the importance of considering multiple traits when interpreting the molecular consequences of genetic variants and designing experimental studies.