Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

By J. Bowden, George Davey Smith, Stephen Burgess. International Journal of Epidemiology, 2015

8,504 citations recorded by the source corpus.

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/89c748f6c44a4e5805aafda5bb7cce2f7a5971fa

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Citations in the corpus

The confidence interval method for selecting valid instrumental variables (2019, methodology): “(2018) provide a discussion of and comparison to these methods, also including alternative methods proposed by Bowden et al. (2016), Hartwig et al. (2017) and Burgess et al.”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, methodology): “Rather than setting the intercept term to be zero, the term is estimated as part of the analysis [9]:”
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator (2016, methodology): “We explore the statistical properties of the weighted median estimator in a realistic simulation study, and compare with an alternative summary data analysis method also robust to some violations of the IV assumptions, MR-Egger regression (Bowden et al., 2015).”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Here, it is assumed that on an average the random effects have zero mean and are uncorrelated with the SNP-exposure effect (55) (Appendix 1).”
Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, methodology): “On the methodological side, the commonly used metaanalysis estimators in this problem include Egger regression (Bowden et al., 2015) and weighted median (Bowden et al.”
Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic (2016, methodology): “This is referred to as balanced’ pleiotropy.(10) When InSIDE and NOME are perfectly satisfied, MR-Egger returns an unbiased estimate for the causal effect b.”
Mendelian randomization for cardiovascular diseases: principles and applications (2023, neutral): “29 Several other methods that are more robust to pleiotropy but typically less efficient, such as the weighted median, 30 MR-Egger, 31 and MR-Pleiotropy RESidual Sum and Outlier (PRESSO) 32 methods, are commonly used as sensitivity analyses.”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, methodology): “(9) Then, according to LRVB, we can obtain the accurate estimate Σ̂ of the covariance matrix of sufficient statistics in BWMR by Σ̂ = −(H−V−1)−1 = (I−VH)−1V. (10) In summary, from VEM algorithm, we obtain the estimate of β as the mean of latent variable β under variational posterior distribution aft”