Dynastic effects are the potential for MR causal estimates to be confounded by characteristics that are transmitted across generations. For example, an MR study of the effect of height on coronary heart disease might be biased by a dynastic effect through a confounding path linking genetic instruments for height to coronary heart disease via the correlation between own and mother’s height genes and the possibility that maternal height influences fetal growth and development in utero, which influences future offspring (own) heart disease.
It is difficult to detect the magnitude of any bias resulting from this. It is less likely to occur in MR studies of ‘own’ risk factors (as opposed to intrauterine exposures from maternal pregnancy risk factors), that are not ‘visible’ and, hence, not likely copied from parents by offspring. Triangulation of MR findings with those from other methods that have different and unrelated potential causes of bias can help improve confidence about whether MR results are markedly biased by these effects.
- Nuesch E, Dale C, Palmer TM et al. Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis. Int J Epidemiol 2015.
- 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
- Collider bias
- 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)
- 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