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Confirmation of the superior performance of the causal Graphical Analysis Using Genetics (cGAUGE) pipeline in comparison to various competing alternatives

Lookup NU author(s): Professor Heather Cordell, Dr Richard Howey



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 Howey R and Cordell HJ. Various methods exist that utilise information from genetic predictors to help identify potential causal relationships between measured biological or clinical traits. Here we conduct computer simulations to investigate the performance of a recently proposed causal Graphical Analysis Using Genetics (cGAUGE) pipeline, used as a precursor to Mendelian randomization analysis, in comparison to our previously proposed Bayesian Network approach for addressing this problem. We use the same simulation (and analysis) code as was used by the developers of cGAUGE, adding in a comparison with the Bayesian Network approach. Overall, we find the optimal method (in terms of giving high power and low false discovery rate) is the cGAUGE pipeline followed by subsequent analysis using the MR-PRESSO Mendelian randomization approach.

Publication metadata

Author(s): Cordell HJ, Howey R

Publication type: Article

Publication status: Published

Journal: Wellcome Open Research

Year: 2022

Volume: 7

Online publication date: 05/07/2022

Acceptance date: 05/07/2022

Date deposited: 20/09/2022

ISSN (electronic): 2398-502X

Publisher: F1000 Research Ltd


DOI: 10.12688/wellcomeopenres.17991.1


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Funder referenceFunder name
Wellcome Trust