As genetic variants are fixed at conception, in general, they can be seen to reflect a lifecourse predisposition to a particular trait. This makes MR less prone to regression dilution bias, but such genetic variants might be unsuited to testing the effects of exposures that are believed to act during critical (a time period in which the exposure is hypothesised to solely have its effect) or sensitive (a period during which the exposure is hypothesised to have the greatest effect) periods.

Some extensions of MR, such as multivariable MR, are available that can help identify critical periods. These methods generally require variation in the association between the genetic variants and the exposure across the lifecourse. As genome-wide association studies (GWASs) of trajectories (e.g., change in weight or height across infancy childhood and into early adulthood) are undertaken, they may also provide genetic instrumental variables (IVs) that could be used in MR to compare change in an exposure between two ages with change in the same exposure between two later ages and compare these to see if there are differences in the effect of that exposure at different ages on an outcome of interest. However, this method is uncommon and likely to need very large sample sizes.

## References

- Power GM, Sanderson E, Pagoni P, et al. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review. European Journal of Epidemiology 2023; https://doi.org/10.1007/s10654-023-01032-1
- Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol 2017; 45: 1866-1886.
- Lawlor DA, Richmond R, Warrington N et al. Using Mendelian randomization to determine causal effects of pregnancy (intrauterine) exposures on offspring outcomes: Sources of bias and methods for assessing them. . Wellcome Open Research 2017; 2: 11.

## 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 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
- Winner's curse