A key way in which MR may violate the exclusion restriction assumption. Also known as “genuine” or “true” pleiotropy. This is when a genetic variant affects other traits which influence the outcome independently of the hypothesised exposure.
This can result in biased MR estimates because of violation of the exclusion restriction assumption. For example, if some or all of the genetic IVs that robustly associate with the risk factor of interest also, independently of the association with the risk factor, associate with other risk factors for the outcome, then the MR estimate will be the combined effect of all of these (independent) risk factors – not the effect of the risk factor of interest alone. Methods such as MR-Egger, weighted median- and mode-based MR methods have been developed to explore and account for the impact of horizontal pleiotropy in MR studies (see Chapter 4). This can also be informative about the trait’s aetiology (see MR-TRYX).
- Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014;23:R89-R98.
- 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
- Collider bias
- Dynastic effects
- Exclusion restriction assumption
- Harmonization failure (in two-sample MR)
- Homogeneity Assumption
- 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)
- Population stratification
- Regression dilution bias (attenuation by errors)
- Relevance assumption
- Reverse causality
- Same underlying population (in two-sample MR)
- Statistical power/efficiency
- Vertical Pleiotropy
- Weak instrument bias
- Winner's curse