Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

By J. Bowden, George Davey Smith, Philip C. Haycock, Stephen Burgess. Genetic Epidemiology, 2016

7,631 citations recorded by the source corpus.

Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

Subfields: Epidemiology, Genetics

View Paper: https://www.semanticscholar.org/paper/eef74afa93dfded0a616460ce87913fa93853662

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Citations in the corpus

Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, methodology): “Though difficult to test empirically, this assumption can be relaxed in various ways when multiple instruments are available for a putative exposure [24, 25] and a number of sensitivity tests are now available to improve reliability [26].”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, methodology): “Some methods are available to assess pleiotropy including for the use of MVMR [43-45].”
Triangulation in aetiological epidemiology (2016, positive): “dealing with these, in particular for dealing with violation of the exclusion restriction criteria due to genuine pleiotropy.(36,37) Two of these methods in particular (MREgger(36) and the weighted median estimator(37)) are likely to become increasingly used in MR sensitivity analyses, as twosample ”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “The repertoire of robust univariable MR approaches [51] that seek to act as sensitivity analyses for potential unbalanced horizontal pleiotropy each make a different series of assumptions [53].”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, methodology): “Indeed, a similar pattern has been previously shown for the simple and weighted median.(10) This suggests that comparing both methods would be a useful sensitivity analysis in practice, although care must be taken since the simple MBE may in some cases (as in our real data example with urate levels)”
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data (2017, methodology): “…of genetic variants with heterogeneous causal estimates; 14 to use an unweighted rather than a weighted analysis in the median-based method; 9 to make inferences based on a t-distribution rather than a normal distribution; and to report a confidence interval at a different alpha level (for…”
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).”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, methodology): “Addressing the issue of bias due to pleiotropy has attracted lots of attention in the summary-data MR literature [7, 8, 26, 34, 51, 52], but no solid statistical underpinning has yet been given.”