Mendelian randomization-Egger (MR-Egger) is a statistical method used in Mendelian randomization to assess causal relationships between risk factors and outcomes using summarized genetic data. It consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. Unlike conventional methods, MR-Egger assumes the InSIDE (INstrument Strength Independent of Direct Effect) assumption, which states that the pleiotropic effects of genetic variants are uncorrelated with their associations with the risk factor. This allows MR-Egger to detect violations of the instrumental variable assumptions and provide a consistent estimate of the causal effect. MR-Egger is a valuable sensitivity analysis for detecting violations of the instrumental variable assumptions, but it may produce biased estimates and inflated Type 1 error rates in practice due to violations of the InSIDE assumption or the influence of outlying variants. The method is particularly useful when the instrumental variable assumptions are not satisfied, but it is not a panacea for all violations of these assumptions. The interpretation of MR-Egger results requires careful consideration of the intercept term, which indicates the average pleiotropic effect of genetic variants. A non-zero intercept suggests that the IVW estimate is biased, and the MR-Egger estimate may differ from the conventional method. MR-Egger is sensitive to the orientation of genetic variants, and the choice of orientation can affect the results. The method is also influenced by the presence of outlying variants, which can significantly alter the estimates. The precision of the MR-Egger estimate depends on the variability between genetic associations with the risk factor, and the method is less precise when the associations are similar. The method is also affected by the correlation between pleiotropic effects and the associations with the risk factor, which can lead to violations of the InSIDE assumption. The MR-Egger method is compared with conventional Mendelian randomization methods, and the results can differ significantly. In some cases, the MR-Egger method may detect directional pleiotropy, while the conventional method may not. The interpretation of results from MR-Egger requires careful consideration of the intercept term and the causal estimate. The method is also sensitive to the assumptions of linearity and homogeneity of the causal effect, which are not necessary for estimating a causal effect but are important for ensuring the consistency of the estimates. In conclusion, MR-Egger is a valuable tool for sensitivity analysis in Mendelian randomization, but it is not a substitute for conventional methods. The method is particularly useful when the instrumental variable assumptions are not satisfied, but it requires careful interpretation and application. The method is also sensitive to the assumptions of linearity and homogeneity of the causal effect, and violations of these assumptions can lead to inappropriate inferences. Overall, MR-Egger is a useful tool for assessing the plausibility of findings from Mendelian randomization studies, but it should be used in conjunction with otherMendelian randomization-Egger (MR-Egger) is a statistical method used in Mendelian randomization to assess causal relationships between risk factors and outcomes using summarized genetic data. It consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. Unlike conventional methods, MR-Egger assumes the InSIDE (INstrument Strength Independent of Direct Effect) assumption, which states that the pleiotropic effects of genetic variants are uncorrelated with their associations with the risk factor. This allows MR-Egger to detect violations of the instrumental variable assumptions and provide a consistent estimate of the causal effect. MR-Egger is a valuable sensitivity analysis for detecting violations of the instrumental variable assumptions, but it may produce biased estimates and inflated Type 1 error rates in practice due to violations of the InSIDE assumption or the influence of outlying variants. The method is particularly useful when the instrumental variable assumptions are not satisfied, but it is not a panacea for all violations of these assumptions. The interpretation of MR-Egger results requires careful consideration of the intercept term, which indicates the average pleiotropic effect of genetic variants. A non-zero intercept suggests that the IVW estimate is biased, and the MR-Egger estimate may differ from the conventional method. MR-Egger is sensitive to the orientation of genetic variants, and the choice of orientation can affect the results. The method is also influenced by the presence of outlying variants, which can significantly alter the estimates. The precision of the MR-Egger estimate depends on the variability between genetic associations with the risk factor, and the method is less precise when the associations are similar. The method is also affected by the correlation between pleiotropic effects and the associations with the risk factor, which can lead to violations of the InSIDE assumption. The MR-Egger method is compared with conventional Mendelian randomization methods, and the results can differ significantly. In some cases, the MR-Egger method may detect directional pleiotropy, while the conventional method may not. The interpretation of results from MR-Egger requires careful consideration of the intercept term and the causal estimate. The method is also sensitive to the assumptions of linearity and homogeneity of the causal effect, which are not necessary for estimating a causal effect but are important for ensuring the consistency of the estimates. In conclusion, MR-Egger is a valuable tool for sensitivity analysis in Mendelian randomization, but it is not a substitute for conventional methods. The method is particularly useful when the instrumental variable assumptions are not satisfied, but it requires careful interpretation and application. The method is also sensitive to the assumptions of linearity and homogeneity of the causal effect, and violations of these assumptions can lead to inappropriate inferences. Overall, MR-Egger is a useful tool for assessing the plausibility of findings from Mendelian randomization studies, but it should be used in conjunction with other