Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

By Stephen Burgess, Adam S. Butterworth, S. Thompson. Genetic Epidemiology, 2013

5,226 citations recorded by the source corpus.

Genome‐wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual‐level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse‐variance weighted average of variant‐specific associations and a likelihood‐based approach for summarized data give similar estimates and precision to the two‐stage least squares method for individual‐level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P‐value in a linear regression of the risk factor for each variant is less than 1×10−5 , then weak instrument bias will be small. We use these methods to estimate the causal association of low‐density lipoprotein cholesterol (LDL‐C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL‐C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual‐level data, although the necessary assumptions cannot be so fully assessed.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/34d560783eec6ff508e7f357169a2c62d1ff4d9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Mendelian randomization for cardiovascular diseases: principles and applications (2023, neutral): “29 Several other methods that are more robust to pleiotropy but typically less efficient, such as the weighted median, 30 MR-Egger, 31 and MR-Pleiotropy RESidual Sum and Outlier (PRESSO) 32 methods, are commonly used as sensitivity analyses.”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, methodology): “A sampling strategy is used in PRESSO to detect outliers due to horizontal pleiotropy and then inverse variance weighted (IVW) regression (Burgess et al., 2013) is applied to estimate causal effects.”
Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique (2016, methodology): “Recent developments in two-sample Mendelian randomization are based on methods originally developed for meta-analysis.(10,11) Whereas the first formal extended elucidation of Mendelian randomization contained what are in essence two-sample Mendelian randomization estimates (e.”
Mapping the human genetic architecture of COVID-19 (2021, positive): “Fixed-effects IVW analysis was used as the primary analysis 37 , with weighted median estimator (WME) 38 , weighted mode based estimator (WMBE) 39 , MR Egger regression 40 and MR-PRESSO 41 outlier corrected estimates used as additional sensitivity analyses.”
Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects (2015, positive): “Finally, our estimated causal odds ratio for a 30% decrease in LDL-C was surprisingly large in comparison with not only estimates of the effect of statin usage, which also reduces LDL-C levels by approximately 30% (22), but also a Mendelian randomization analysis that included variants solely associ”
Bias due to participant overlap in two‐sample Mendelian randomization (2016, methodology): “As covariance is a linear operator in both its arguments, we may therefore expect the bias of the 2SLS estimator to be approximately linear in the proportion of overlap between the samples.”
Meta‐analysis and Mendelian randomization: A review (2019, background): “The Galbraith radial plot has additionally been repurposed for detecting outlying variants in MR.28”
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator (2016, background): “The availability of such summary data has facilitated powerful Mendelian randomization investigations to be conducted in a two-sample framework by providing genetic associations precisely estimated in large sample sizes (Burgess et al., 2013).”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “This is referred to as the IVW estimate and the general framework as two-sample summary data MR.(2,3) For further details, see Box 1.”
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018, methodology): “Figure 2 (left) shows the power of Cochran’s Q to detect heterogeneity at the 5% significance level as a function of first-order, second-order, iterative and exact weights when data are simulated under a multiplicative random-effects model with heterogeneity due to pleiotropy of increasing magnitude”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, methodology): “, the Instrument Strength Independent of Direct Effects – InSIDE – assumption).(5,8) More recently, other summary data MR methods that allow relaxing (but not eliminating) the instrumental variable assumptions regarding horizontal pleiotropy have been proposed.”
Multivariable Mendelian Randomization and Mediation. (2020, methodology): “MR methods, the assumptions required and their implications are discussed in detail earlier in this volume and elsewhere (Lawlor et al. 2008; Burgess et al. 2013; Davey Smith and Hemani 2014).”