The process by which potentially disruptive influences on normal development from genetic (and environmental) exposures are damped or buffered by compensatory developmental processes. This results from animals (including humans) being relatively ‘plastic’ during developmental periods. For example, hypothetically fetuses with genetic variants that result in on average higher glucose levels might develop other compensatory systems in such a way that higher glucose levels do not adversely affect them.
It is not directly possible to determine. If canalization has occurred, then genetic IVs may still relate to the risk factor by the same magnitude (as in the absence of any canalization) but the effect on a potential outcome of that risk factor could be dampened. Biological evidence for canalization having a marked impact on genetic associations is lacking. This will not impact MR results when assessing intrauterine effects (i.e., via maternal genetic IVs for maternal pregnancy risk factors) on offspring outcomes, as the mother’s genotype will not have been influenced by canalization in the offspring (though this has additional potential problems – see MR for testing developmental origins).
- Lawlor DA, Harbord RM, Sterne JAC, Timpson NJ, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistic in Medicine 2008;27:1133-1163.
- Davey Smith G, Ebrahim S. "Mendelian randomisation": can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology 2003;32:1-22.
Other terms in 'Sources of bias and limitations in MR':
- Assortative mating
- Collider bias
- Dynastic effects
- 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