The study evaluates the prevalence and impact of horizontal pleiotropy in Mendelian Randomization (MR) analyses, which are used to infer causal relationships between exposures and complex traits or diseases. Horizontal pleiotropy occurs when a genetic variant affects multiple traits or diseases, violating the 'no horizontal pleiotropy' assumption in MR. The authors developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect and correct for horizontal pleiotropic outliers in multi-instrument summary-level MR testing. Through simulations, they demonstrated that MR-PRESSO is effective when horizontal pleiotropy occurs in <50% of instruments. Applying MR-PRESSO to 4,250 MR tests of complex traits and diseases derived from 82 summary-level genome-wide association studies (GWAS) datasets, they found that horizontal pleiotropy was detectable in over 48% of significant causal relationships, introduced distortions in causal estimates ranging from −131% to 201%, and induced false positive causal relationships in up to 10% of relationships. The study highlights the need for systematic evaluation and correction of horizontal pleiotropy in MR analyses to ensure accurate causal inference.The study evaluates the prevalence and impact of horizontal pleiotropy in Mendelian Randomization (MR) analyses, which are used to infer causal relationships between exposures and complex traits or diseases. Horizontal pleiotropy occurs when a genetic variant affects multiple traits or diseases, violating the 'no horizontal pleiotropy' assumption in MR. The authors developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect and correct for horizontal pleiotropic outliers in multi-instrument summary-level MR testing. Through simulations, they demonstrated that MR-PRESSO is effective when horizontal pleiotropy occurs in <50% of instruments. Applying MR-PRESSO to 4,250 MR tests of complex traits and diseases derived from 82 summary-level genome-wide association studies (GWAS) datasets, they found that horizontal pleiotropy was detectable in over 48% of significant causal relationships, introduced distortions in causal estimates ranging from −131% to 201%, and induced false positive causal relationships in up to 10% of relationships. The study highlights the need for systematic evaluation and correction of horizontal pleiotropy in MR analyses to ensure accurate causal inference.