The study presents a method for detecting pleiotropy in Mendelian randomization (MR) studies using summary data and a continuous outcome. MR uses genetic variants as instrumental variables to estimate causal effects of modifiable phenotypes on an outcome, assuming no pleiotropy. The study proposes an alternative approach when only summary genetic data are available or data on gene-phenotype and gene-outcome come from different subjects. The presence of pleiotropy is investigated using the between-instrument heterogeneity Q test and the I² index in a meta-analysis of MR Wald estimates derived from each instrument. For continuous outcomes, the method is evaluated through simulations and illustrated using published data. The Q test tends to be conservative in small samples but becomes more powerful with higher pleiotropy and larger sample sizes. In MR studies with large sample sizes based on summary data, the between-instrument Q test is a useful tool to explore heterogeneity due to pleiotropy or other causes. The study also compares the Q test with the Sargan test in scenarios where data come from the same subjects. The results show that the Q test is conservative but effective in detecting pleiotropy when sample sizes are large. The study concludes that the between-instrument heterogeneity test is a good tool for detecting heterogeneity in MR estimates, although it does not identify the source of heterogeneity. The study also highlights the importance of using multiple instruments to assess pleiotropy and other causes of heterogeneity in MR studies. The method is applied to an illustrative example of birth weight and fasting glucose levels in adults, where the Q test and I² index were used to detect heterogeneity. The results suggest that the third IV assumption may be violated for some instruments, and the exclusion of the variant with the largest effect reduced heterogeneity. The study emphasizes the need for biological knowledge to investigate the causes of pleiotropy. The study also discusses the limitations of the Q test, including its conservative nature in small samples and the potential for weak correlation between MR estimates. The study concludes that the between-instrument heterogeneity test is a useful tool for detecting heterogeneity in MR studies, but further research is needed to identify the sources of heterogeneity.The study presents a method for detecting pleiotropy in Mendelian randomization (MR) studies using summary data and a continuous outcome. MR uses genetic variants as instrumental variables to estimate causal effects of modifiable phenotypes on an outcome, assuming no pleiotropy. The study proposes an alternative approach when only summary genetic data are available or data on gene-phenotype and gene-outcome come from different subjects. The presence of pleiotropy is investigated using the between-instrument heterogeneity Q test and the I² index in a meta-analysis of MR Wald estimates derived from each instrument. For continuous outcomes, the method is evaluated through simulations and illustrated using published data. The Q test tends to be conservative in small samples but becomes more powerful with higher pleiotropy and larger sample sizes. In MR studies with large sample sizes based on summary data, the between-instrument Q test is a useful tool to explore heterogeneity due to pleiotropy or other causes. The study also compares the Q test with the Sargan test in scenarios where data come from the same subjects. The results show that the Q test is conservative but effective in detecting pleiotropy when sample sizes are large. The study concludes that the between-instrument heterogeneity test is a good tool for detecting heterogeneity in MR estimates, although it does not identify the source of heterogeneity. The study also highlights the importance of using multiple instruments to assess pleiotropy and other causes of heterogeneity in MR studies. The method is applied to an illustrative example of birth weight and fasting glucose levels in adults, where the Q test and I² index were used to detect heterogeneity. The results suggest that the third IV assumption may be violated for some instruments, and the exclusion of the variant with the largest effect reduced heterogeneity. The study emphasizes the need for biological knowledge to investigate the causes of pleiotropy. The study also discusses the limitations of the Q test, including its conservative nature in small samples and the potential for weak correlation between MR estimates. The study concludes that the between-instrument heterogeneity test is a useful tool for detecting heterogeneity in MR studies, but further research is needed to identify the sources of heterogeneity.