This article discusses the use of multiple genetic variants as instrumental variables (IVs) in Mendelian randomization (MR) analyses to estimate causal effects of modifiable risk factors on disease outcomes. MR uses genetic variants to mimic randomization in observational studies, helping to address confounding. However, genetic variants typically explain only a small proportion of the variability in risk factors, requiring large sample sizes. Recent advances in genome-wide association studies (GWAS) have identified numerous genetic variants associated with risk factors and disease outcomes, making the use of multiple IVs increasingly feasible. The study uses four adiposity-associated genetic variants (FTO, MC4R, TMEM18, GNPDA2) as IVs to examine the causal effect of fat mass on bone mineral density (BMD) in children from the Avon Longitudinal Study of Parents and Children (ALSPAC). The results show that IV estimates using multiple instruments provide more precise estimates than single instruments. However, weak instruments can increase finite sample bias, and missing data on multiple genetic variants can reduce sample size. The article also discusses statistical issues related to using multiple instruments, including over-identification tests, finite sample bias, and instrument strength. It highlights that using multiple instruments can improve the power of MR analyses and allow for testing of IV assumptions. However, the use of allele scores as IVs may have lower power than multiple instruments. The study also presents simulation results showing that multiple instruments and weighted allele scores can provide more accurate estimates than single instruments. The study concludes that using multiple genetic variants as IVs in MR analyses can increase statistical power and provide opportunities to test IV assumptions. However, further research is needed into multiple imputation methods to address missing data issues in IV estimation. The use of multiple genetic variants as IVs is a promising approach for MR studies, but careful consideration of the assumptions and limitations is required.This article discusses the use of multiple genetic variants as instrumental variables (IVs) in Mendelian randomization (MR) analyses to estimate causal effects of modifiable risk factors on disease outcomes. MR uses genetic variants to mimic randomization in observational studies, helping to address confounding. However, genetic variants typically explain only a small proportion of the variability in risk factors, requiring large sample sizes. Recent advances in genome-wide association studies (GWAS) have identified numerous genetic variants associated with risk factors and disease outcomes, making the use of multiple IVs increasingly feasible. The study uses four adiposity-associated genetic variants (FTO, MC4R, TMEM18, GNPDA2) as IVs to examine the causal effect of fat mass on bone mineral density (BMD) in children from the Avon Longitudinal Study of Parents and Children (ALSPAC). The results show that IV estimates using multiple instruments provide more precise estimates than single instruments. However, weak instruments can increase finite sample bias, and missing data on multiple genetic variants can reduce sample size. The article also discusses statistical issues related to using multiple instruments, including over-identification tests, finite sample bias, and instrument strength. It highlights that using multiple instruments can improve the power of MR analyses and allow for testing of IV assumptions. However, the use of allele scores as IVs may have lower power than multiple instruments. The study also presents simulation results showing that multiple instruments and weighted allele scores can provide more accurate estimates than single instruments. The study concludes that using multiple genetic variants as IVs in MR analyses can increase statistical power and provide opportunities to test IV assumptions. However, further research is needed into multiple imputation methods to address missing data issues in IV estimation. The use of multiple genetic variants as IVs is a promising approach for MR studies, but careful consideration of the assumptions and limitations is required.