Methodological challenges in Mendelian randomization

By Tyler J. VanderWeele, E. T. Tchetgen Tchetgen, Marilyn C. Cornelis, Peter Kraft. Epidemiology, 2014

512 citations recorded by the source corpus.

We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene–environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507–508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype–outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/449df2653c17bf6ac1482d90335cc5910cf24f5a

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Citations in the corpus

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression (2015, background): “We are particularly interested in the coverage properties of the estimators with a null causal effect, and the power of estimators with a positive causal effect, as salient findings from Mendelian randomization investigations are not only the magnitude of the causal effect—or indeed whether such can”
A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “In many cases, testing for an association between an IV and the outcome, to assess the causal role of the exposure and the direction of the causal effect without estimating a causal parameter, is sufficient.(32) Such a test can be performed by a variety of methods, but most commonly using linear reg”
Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. (2018, unknown): “While Mendelian randomization studies have limitations (Nitsch et al., 2006; VanderWeele et al., 2014), this evidence is consistent with the notion that pentameric CRP does not cause inflammatory tissue damage.”
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, background): “Although these assumptions are restrictive, we note that even if these parametric assumptions are not satisfied, a Mendelian randomization investigation can still be interpreted as a test of the causal null hypothesis, even if the magnitude of the causal effect estimate does not have an interpretati”
The MR-Base platform supports systematic causal inference across the human phenome (2018, background): “…about the strengths and limitations of MR ( Davey Smith and Hemani, 2014 ; Haycock et al., 2016 ; Swerdlow et al., 2016 ; Holmes et al., 2017 ; VanderWeele et al., 2014 ; Lawlor et al., 2008 ; Lawlor et al., 2016 ; Zheng et al., 2017a ), and we summarise how these relate to MR-Base in…”
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, background): “The causes and consequences of pleiotropy in MR are described in detail elsewhere.(1,9,10,13,14,20) In addition to assumptions IV1–IV3, there are additional assumptions and considerations that apply to all instrumental variable estimation, including MR and MVMR.”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, background): “Where the homogeneity assumption cannot be satisfied, the causal estimates from MR analyses will provide a valid test of the causal null hypothesis, but not the local average treatment effect [45, 54].”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “Several reviews of such methods exist in the literature [8, 32, 35].”