The article discusses the use of multiple genetic variants as instrumental variables (IVs) in Mendelian randomization studies to estimate causal effects of modifiable risk factors on disease outcomes. It highlights the challenges of using genetic variants, which typically explain only a small proportion of the variability in risk factors, and the increasing availability of multiple genetic variants associated with risk factors and disease outcomes through genome-wide association studies (GWAS). The authors describe the assumptions of IVs and present an illustrative Mendelian randomization analysis using four adiposity-associated genetic variants to examine the causal effect of fat mass on bone density in children. They discuss the potential violations of IV assumptions, such as population stratification, linkage disequilibrium, and pleiotropy, and how multiple instruments can help address these issues. The article also includes simulation studies to evaluate the precision and bias of IV estimates using different sets of instruments and the impact of missing data. The results show that using multiple instruments can increase the precision of IV estimates, but weak instruments can introduce finite sample bias. The authors conclude that multiple instrument analyses are a promising approach for Mendelian randomization studies, provided that appropriate methods are used to handle missing data and to assess the validity of the instruments.The article discusses the use of multiple genetic variants as instrumental variables (IVs) in Mendelian randomization studies to estimate causal effects of modifiable risk factors on disease outcomes. It highlights the challenges of using genetic variants, which typically explain only a small proportion of the variability in risk factors, and the increasing availability of multiple genetic variants associated with risk factors and disease outcomes through genome-wide association studies (GWAS). The authors describe the assumptions of IVs and present an illustrative Mendelian randomization analysis using four adiposity-associated genetic variants to examine the causal effect of fat mass on bone density in children. They discuss the potential violations of IV assumptions, such as population stratification, linkage disequilibrium, and pleiotropy, and how multiple instruments can help address these issues. The article also includes simulation studies to evaluate the precision and bias of IV estimates using different sets of instruments and the impact of missing data. The results show that using multiple instruments can increase the precision of IV estimates, but weak instruments can introduce finite sample bias. The authors conclude that multiple instrument analyses are a promising approach for Mendelian randomization studies, provided that appropriate methods are used to handle missing data and to assess the validity of the instruments.