It is important that the genetic IV-risk factor and genetic IV-outcome associations in two-sample MR are from the same underlying population or, at a minimum, there is evidence that the gene IV-risk factor association is similar in the population used for the second sample (genetic IV-outcome association).
Many GWAS that are used to identify genetic IVs for MR (and for the genetic IV-risk factor associations) are conducted in women and men combined. If these findings are then combined with genetic IV-outcome associations in women only (for example, if the outcome of interest were breast or ovarian cancer), the assumption is that the genetic IV-risk factor association does not differ between women and men. Ideally, we would want to have the two samples from the same underlying population. If this is not possible (e.g., because aggregate data being used and the populations differ in the two samples), some attempt should be made to find some data that provides evidence that the genetic IV-risk factor association is similar in the population used for the genetic IV-outcome association, as in the original GWAS.
- Lawlor DA. Two-sample Mendelian randomization: opportunities and challenges. . International Journal of Epidemiology 2016;doi:10.1093/ije/dyw127.
- Hartwig FP, Davies NM, Hemani G, Davey Smith G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol 2016;45:1717-1726.
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
- Statistical power/efficiency
- Vertical Pleiotropy
- Weak instrument bias
- Winner's curse