Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption

By F. Hartwig, George Davey Smith, J. Bowden. bioRxiv, 2017

2,987 citations recorded by the source corpus.

Background Mendelian randomisation (MR) is being increasingly used as a strategy to improve causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Methods Here, a new method –the mode estimator – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The mode estimate is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluated the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. Results The mode estimates presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. Conclusions The mode estimator relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0a1519154b22525e0d8a51c666258f786f9d9a92

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Citations in the corpus

Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians (2018, background): “These results suggest that the inverse variance weighted estimate is driven by genetic pleiotropy 19 46-48 very large samples, results using weak instruments are biased towards the outcome-risk factor association in the single sample setting and towards the null in the two sample setting.”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “assumption (ZEMPA) and calculate the weighted mode of the Wald ratio estimates (65).”
Multivariable Mendelian Randomization and Mediation. (2020, background): “There are a number of sensitivity analyses that can be used to try to identify pleiotropic effects in the MR analysis (Chen et al. 2008; Davey Smith and Hemani 2014; Bowden et al. 2015; Bowden et al. 2016; Bowden et al. 2017; Hartwig et al. 2017; Spiller et al. 2018).”
The MR-Base platform supports systematic causal inference across the human phenome (2018, background): “When multiple independent instruments are available it is possible to perform sensitivity analyses that attempt to distinguish between horizontal and vertical pleiotropy and return causal estimates adjusted for the former ( Bowden et al., 2016a ; Bowden et al., 2015 ; Hartwig et al., 2017b ).”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “In sensitivity analyses, we conducted univariable MR analyses robust to some forms of potential unbalanced horizontal pleiotropy [51] (horizontal pleiotropy being the process by which genetic variants used to instrument an exposure also associate with other traits that influence the outcome, a form ”
The confidence interval method for selecting valid instrumental variables (2019, neutral): “(2008) and von Hinke et al. (2016). It is therefore important to have methods that can determine whether instruments are invalid under as weak a set of assumptions as possible.”