Mendelian randomization: prospects, potentials, and limitations.

By George Davey Smith, Shah Ebrahim. International Journal of Epidemiology, 2004

1,194 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9541d26c5afd06a01cfcfa3e1473634645f70c2a

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Citations in the corpus

Interpreting findings from Mendelian randomization using the MR-Egger method (2017, background): “Although in some cases, the necessary assumption that genetic variants are valid instrumental variables is credible, in other cases the instrumental variable assumptions are not plausible [3].”
Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique (2016, background): “Moreover, genetic variants and interventions may affect the exposure through different mechanisms.(24,25) The final step would be to undertake quality control checks.”
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, background): “Additionally, if the minor allele frequencies of variants differ between ethnic groups (or other distinct populations or subpopulations), population stratification may bias results [26].”
Orienting the causal relationship between imprecisely measured traits using GWAS summary data (2017, background): “Any non-differential measurement error that might be present (either because the SNP is poorly typed or because the SNP is not in complete linkage disequilibrium with the causal variant) will reduce power in MR but will not incur bias [3, 13, 32].”
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology (2008, positive): “Finally, genetic variants that are related to a modifiable exposure will generally be related to it throughout life from birth to adulthood and therefore their use in causal inference can also avoid attenuation by errors (regression dilution bias) [27].”
On Instrumental Variables Estimation of Causal Odds Ratios (2011, background): “The growing success of IV methods in biostatistics and epidemiology can, however, be mainly attributed to applications in genetic epidemiology (Smith and Ebrahim, 2004).”
Meta-analysis methods for genome-wide association studies and beyond (2013, background): “Mendelian randomization entails the use of genetic variants as proxies (‘instrumental variables’) for modifiable or environmental exposures under investigation 89 .”
Using multiple genetic variants as instrumental variables for modifiable risk factors (2012, background): “Population stratification, linkage disequilibrium and pleiotropy have been identified as factors that could bias Mendelian randomisation analyses.(2,5,11,40) We briefly describe them, and the use of multiple instruments to address issues they raise.”
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors (2015, background): “A variant satisfying these assumptions divides a study population into subgroups which are analogous to treatment arms in a randomized controlled trial, in that they differ systematically with respect to the risk factor of interest, but not with respect to confounding factors [5].”
Methodological challenges in Mendelian randomization (2014, background): “investigators should consider and comment on (1) whether the exposure used in the analysis completely captures the phenotype that may mediate the association between the variant and the outcome, (2) whether the exposure is time-varying, (3) whether there may be gene–environment interaction, (4) whet”
Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization (2014, background): “As both examples highlight, the biological understanding of many genetic markers and their potential pleiotropic effects are typically incomplete at the time of the study (Solovieff et al. 2013).”