Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

By J. Bowden, F. Del Greco M, Cosetta Minell, George Davey Smith, Nuala Sheehan, J. Thompson. International Journal of Epidemiology, 2016

1,508 citations recorded by the source corpus.

Background MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error’ (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2. The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.

Subfields: Experimental and Cognitive Psychology, Genetics, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/58c7721e9efaa50b385f51541c620b79c5a2d8fd

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Citations in the corpus

Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “We characterised instrument strengths in both the univariable and multivariable MR settings as follows: for the univariable estimates, we generated the mean F-statistic, using the approximation described by Bowden and colleagues [44].”
MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data (2017, methodology): “statistic quoted is a measure of weak instrument bias, and is described elsewhere.(20) The mr_plot() command is then applied to the same data, and the MR-Egger estimate is”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Simulation extrapolation (SIMEX) corrections can be applied to account for regression dilution bias (57).”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “proportion of variance in the risk factor explained by the genetic variants (typically measured by the R(2) statistic) [28], the precision of the MR-Egger estimate additionally depends on the variability between the genetic associations with the risk factor [29].”
The MR-Base platform supports systematic causal inference across the human phenome (2018, background): “Relaxing the IV2 assumption of ‘no horizontal pleiotropy’, MR-Egger ( Bowden et al., 2015 ; Bowden et al., 2016b ) adapts the IVW analysis by allowing a non-zero intercept, allowing the net-horizontal pleiotropic effect across all SNPs to be unbalanced, or directional.”
Bias due to participant overlap in two‐sample Mendelian randomization (2016, methodology): “Hence, for the MR-Egger method, an I2 heterogeneity statistic is a better indicator of bias than the F statistic (Bowden et al., 2016).”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, methodology): “This is equivalent to the NO Measurement Error (NOME) assumption.(14)”
A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization (2017, methodology): “The implications of NOME violation on the two approaches is discussed at length in separate work by Bowden et al [17], but are summarised below.”