227 citations · 364 across the 12 of their papers we have counts for
9 papers · 1 filter
Bias in multivariable Mendelian randomization studies due to measurement error on exposures
Jiazheng Zhu, Stephen Burgess, Andrew J. Grant
Multivariable Mendelian randomization estimates the causal effect of multiple exposures on an outcome, typically using summary statistics of genetic variant associations. However,…
Disentangling the effects of traits with shared clustered genetic predictors using multivariable Mendelian randomization
Fatima Batool, Ashish Patel, Dipender Gill +1
When genetic variants in a gene cluster are associated with a disease outcome, the causal pathway from the variants to the outcome can be difficult to disentangle. For example, the…
Selecting invalid instruments to improve Mendelian randomization with two-sample summary data
Ashish Patel, Francis J. DiTraglia, Verena Zuber +1
Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appro…
Pleiotropy robust methods for multivariable Mendelian randomization
Andrew J. Grant, Stephen Burgess
Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that…
Conditional inference in cis-Mendelian randomization using weak genetic factors
Ashish Patel, Dipender Gill, Paul J. Newcombe +1
Mendelian randomization is a widely-used method to estimate the unconfounded effect of an exposure on an outcome by using genetic variants as instrumental variables. Mendelian rand…
Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates
Stephen Burgess, Jeremy A Labrecque
Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions,…
Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy
Jessica M. B. Rees, Angela Wood, Stephen Burgess
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariabl…
Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables
Stephen Burgess, Verena Zuber, Elsa Valdes-Marquez +2
Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of gen…
Robust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization
Stephen Burgess, Jack Bowden, Frank Dudbridge +1
Mendelian randomization is the use of genetic variants to make causal inferences from observational data. The field is currently undergoing a revolution fuelled by increasing numbe…