Dynastic effects are the potential for MR causal estimates to be confounded by characteristics that are transmitted across generations. For example, consider an MR study of the effect of height on coronary heart disease. This might be biased by an intergenerational (dynastic) effect through maternal height. Height is heritable meaning that genetic variants associated with height and height itself will be correlated in mothers and her offspring. If maternal height influences fetal growth and development in utero, which influences future offspring heart disease risk, there could be a confounding path linking offspring genetic variants associated with height (the exposure) and coronary heart disease (the outcome) via maternal height (and related genetic variants and fetal growth and development.
It is difficult to detect the magnitude of any bias resulting from this. It is less likely to occur in MR studies of "own" exposures (as opposed to intrauterine exposures from maternal pregnancy exposures), that are not visible (e.g., metabolites or other 'omic traits) 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. MR using data within families (such as siblings or family trios) is an increasingly used method to account for possible intergenerational effects.
References
- 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; 45: 1927-1937.
- 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
- 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
- Winner's curse