In many situations, MR will be less biased than conventional multivariable regression (or similar) approaches to exploring causal associations with observational data but will have less statistical power.

Large consortia used for MR studies and the increasing availability of publicly available summary statistics from GWAS that can be used in two-sample MR can provide adequate statistical power.

## References

- Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 2013;42:1497-501.
- Zheng J, Baird D, Borges MC et al. Recent Developments in Mendelian Randomization Studies. Curr Epidemiol Rep 2017;4:330-345.

## Other terms in 'Sources of bias and limitations in MR':

- Assortative mating
- Canalization
- Collider
- Collider bias
- Confounding
- Dynastic effects
- Exclusion restriction assumption
- Harmonization failure (in two-sample MR)
- Homogeneity Assumption
- Horizontal Pleiotropy
- Independence assumption
- InSIDE assumption (in two-sample MR using aggregate data)
- Monotonicity assumption
- MR for testing critical or sensitive periods
- MR for testing developmental origins
- No effect modification assumption (Additional IV assumption)
- Non-linear effects
- Non-overlapping samples (in two-sample MR)
- Overfitting
- Pleiotropy
- Population stratification
- Regression dilution bias (attenuation by errors)
- Relevance assumption
- Reverse causality
- Same underlying population (in two-sample MR)
- Vertical Pleiotropy
- Weak instrument bias
- Winner's curse