This refers to the phenomenon that the first (i.e., discovery) results for an association tend to be exaggerated away from the null, with replication studies tending to be more modest.
In MR, if the association between the genetic instrumental variable (IV) and exposure is based on results (or weights) of the discovery genome-wide association study (GWAS) (or a single study) rather than combined discovery and replication results or replication results only, then the association between the IV and exposure may be exaggerated. As this is the denominator of the Wald ratio (used to estimate the MR effect), the MR result would tend to be biased towards the null. This may be particularly likely in large biobank studies in which both the primary GWAS and one-sample MR analyses are undertaken in the same sample.
References
- Taylor AE, Davies NM, Ware JJ, VanderWeele T, Davey Smith G, Munafo MR. Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Economics and human biology 2014; 13: 99-106.
- 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
- Conditional F-statistic for multiple exposures
- Confounding
- Exclusion restriction assumption
- F-statistic
- Harmonization (in two-sample MR)
- Homogeneity Assumption
- Horizontal Pleiotropy
- Independence assumption
- INstrument Strength Independent of Direct Effect (InSIDE) assumption
- Intergenerational (or dynastic) effects
- Monotonicity assumption
- MR for testing critical or sensitive periods
- MR for testing developmental origins
- No effect modification assumption
- NO Measurement Error (NOME) assumption
- Non-linear MR
- Non-overlapping samples (in two-sample MR)
- Overfitting
- Pleiotropy
- Population stratification
- R-squared
- Regression dilution bias (attenuation by errors)
- Relevance assumption
- Reverse causality
- Same underlying population (in two-sample MR)
- Statistical power and efficiency
- Vertical pleiotropy
- Weak instrument bias