Mendelian randomization as an instrumental variable approach to causal inference

By Vanessa Didelez, Nuala Sheehan. Statistical Methods in Medical Research, 2007

947 citations recorded by the source corpus.

In epidemiological research, the causal effect of a modifiable phenotype or exposure on a disease is often of public health interest. Randomized controlled trials to investigate this effect are not always possible and inferences based on observational data can be confounded. However, if we know of a gene closely linked to the phenotype without direct effect on the disease, it can often be reasonably assumed that the gene is not itself associated with any confounding factors — a phenomenon called Mendelian randomization. These properties define an instrumental variable and allow estimation of the causal effect, despite the confounding, under certain model restrictions. In this paper, we present a formal framework for causal inference based on Mendelian randomization and suggest using directed acyclic graphs to check model assumptions by visual inspection. This framework allows us to address limitations of the Mendelian randomization technique that have often been overlooked in the medical literature.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/35287020453b0c542c7ed2f089468da8906aa2b1

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Citations in the corpus

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator (2016, background): “…is indeed uncontaminated (in the sense that a person’s genetic subgroup is independent of all factors, except the exposure and any causal consequence of the exposure), then differences in the outcome between genetic subgroups can be causally attributed to the exposure (Didelez and Sheehan, 2007).”
Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects (2015, positive): “These assumptions can be illustrated using a causal directed acyclic graph, displaying a causal effect of one variable on another byan arrowand the absence of a direct causal effect by the lack of an arrow (Figure 1) (5).”
Using multiple genetic variants as instrumental variables for modifiable risk factors (2012, background): “Non-linear models that naturally arise for discrete outcomes require different treatment.(11) Our illustrative Mendelian randomisation analysis confirmed a positive causal effect of adiposity (fat mass) on BMD, in line with previous research 31 and suggested that the size of this effect was larger t”
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, methodology): “As shown previously, these assumptions lead to the identification of the causal effect [6].”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “It is a special case of the instrumental variable (IV) methods [21], which date back to the 1920s [54] and have a long and rich history in econometrics and statistics.”
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors (2015, background): “mediate variable (here, BMI), and as the genetic variant is not independent of blood pressure conditional on BMI due to the presence of confounding factors between BMI and blood pressure [3].”
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example (2014, methodology): “To deal with the presence of unmeasured confounders of observed random variables the so-called instrumental variable technique [2] has found applications in genetics [3], epidemiology [4, 5] and economics [6].”
Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators (2013, background): “Thus, in theory, exposure data are not needed for a fully powered test of the hypothesis that an exposure is causally related to an outcome if the reduced-form estimator is used (2).”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “not associated with the outcome conditional on the risk factor and confounders [4, 5].”
Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference* (2014, background): “2014, 33 2297–2340 Didelez and Sheehan [36] and Lawlor et al. [33] provided good reviews of Mendelian randomization methods.”
A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “An association may be induced between the IV and the confounders, leading to possible bias in the IV estimate.(9) This affects not only the ratio method but all IV methods.”