When one or more genetic IVs have different effect allele between exposure and outcome datasets of summary association results. This is of greater concern for palindromic variants with high minor allele frequency, especially when strand issues are a possibility (Figure 1.1).
To overcome errors due to harmonization, it is advised to harmonize using automated scripts that have been thoroughly tested (for example, those used in MR-Base) and check the correlation between effect allele frequencies before and after harmonizing. It is also useful to provide pre- and post-harmonization datasets to allow assessment of the quality of the harmonization and perform sensitivity analysis to evaluate the influence of variants difficult to harmonize (e.g., palindromic variants with high minor allele frequency).
References
- Hemani G, Zheng J, Elsworth B et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018;7.
- 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
- Canalization
- Collider
- Collider bias
- Confounding
- Dynastic effects
- Exclusion restriction assumption
- 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)
- Overfitting
- Pleiotropy
- 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