Synonyms: IV2 assumption, second MR assumption
Known as the second IV assumption (and sometimes also referred to as the “exchangeability” assumption) this states that there are no confounders of the association between the IVs and the outcome.
As genetic variants are determined at conception it is not possible for them to be affected by confounders of risk factor-outcome associations. When referring to the second IV assumption, factors that could influence the genetic variants and outcome include population stratification, dynastic effects and assortative mating. Generally, genetic variants can influence confounders of the risk factor-outcome association and would be analagous to horizontal pleiotropy. If they do and these confounders are not measured / not controlled for in the MR analysis, then a path from the genetic IV via these confounders could bias the causal effect estimate. As noted under "confounding" in both one-sample and two-sample MR, associations of the genetic IV with potential confounders should be undertaken and methods such as multivariable MR used to control for these where possible.
References
- Davey Smith G, Lawlor DA, Harbord R, Timpson N, Day I, Ebrahim S. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PloS Medicine 2008;4:e352-doi:10.1371/journal.pmed.0040352.
- Sheehan NA, Didelez V. Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail. Human Genetics 2019.
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
- 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)
- Statistical power/efficiency
- Vertical Pleiotropy
- Weak instrument bias
- Winner's curse