The paper discusses the challenges and methods for conducting Mendelian randomization analyses with multiple genetic variants, particularly when these variants may have pleiotropic effects. The authors propose a method called MR-Egger regression, which is an adaptation of Egger regression, to detect and correct for bias caused by directional pleiotropy. Pleiotropy can lead to biased causal estimates and increased type I error rates in Mendelian randomization studies. The MR-Egger regression test assesses the presence of directional pleiotropy by examining the intercept term in a weighted linear regression model, where the intercept represents the average pleiotropic effect across genetic variants. Under the assumption that the association of each genetic variant with the exposure is independent of its direct effect on the outcome, the slope coefficient from MR-Egger regression provides a consistent estimate of the causal effect. The paper illustrates the use of MR-Egger regression through two published Mendelian randomization studies and demonstrates its performance using simulated data. The results show that MR-Egger regression can provide more robust estimates of causal effects and detect violations of the standard instrumental variable assumptions. The method is valuable for assessing the robustness of findings in Mendelian randomization investigations.The paper discusses the challenges and methods for conducting Mendelian randomization analyses with multiple genetic variants, particularly when these variants may have pleiotropic effects. The authors propose a method called MR-Egger regression, which is an adaptation of Egger regression, to detect and correct for bias caused by directional pleiotropy. Pleiotropy can lead to biased causal estimates and increased type I error rates in Mendelian randomization studies. The MR-Egger regression test assesses the presence of directional pleiotropy by examining the intercept term in a weighted linear regression model, where the intercept represents the average pleiotropic effect across genetic variants. Under the assumption that the association of each genetic variant with the exposure is independent of its direct effect on the outcome, the slope coefficient from MR-Egger regression provides a consistent estimate of the causal effect. The paper illustrates the use of MR-Egger regression through two published Mendelian randomization studies and demonstrates its performance using simulated data. The results show that MR-Egger regression can provide more robust estimates of causal effects and detect violations of the standard instrumental variable assumptions. The method is valuable for assessing the robustness of findings in Mendelian randomization investigations.