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Lookup NU author(s): Dr Richard Howey, Professor Heather CordellORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 Howey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Here we report the results from exploratory analysis using a Bayesian network approach of data originally derived from a large North European study of type 2 diabetes (T2D) conducted by the IMI DIRECT consortium. 3029 individuals (795 with T2D and 2234 without) within 7 different study centres provided data comprising genotypes, proteins, metabolites, gene expression measurements and many different clinical variables. The main aim of the current study was to demonstrate the utility of our previously developed method to fit Bayesian networks by performing exploratory analysis of this dataset to identify possible causal relationships between these variables. The data was analysed using the BayesNetty software package, which can handle mixed discrete/continuous data with missing values. The original dataset consisted of over 16,000 variables, which were filtered down to 260 variables for analysis. Even with this reduction, no individual had complete data for all variables, making it impossible to analyse using standard Bayesian network methodology. However, using the recently proposed novel imputation method implemented in BayesNetty we computed a large average Bayesian network from which we could infer possible associations and causal relationships between variables of interest. Our results confirmed many previous findings in connection with T2D, including possible mediating proteins and genes, some of which have not been widely reported. We also confirmed potential causal relationships with liver fat that were identified in an earlier study that used the IMI DIRECT dataset but was limited to a smaller subset of individuals and variables (namely individuals with complete data at predefined variables of interest). In addition to providing valuable confirmation, our analyses thus demonstrate a proof-of-principle of the utility of the method implemented within BayesNetty. The full final average Bayesian network generated from our analysis is freely available and can be easily interrogated further to address specific focussed scientific questions of interest.
Author(s): Howey R, Adam J, Adamski J, Atabaki NN, Brunak S, Chmura PJ, De Masi F, Dermitzakis ET, Fernandez-Tajes JJ, Forgie IM, Franks PW, Giordano GN, Haid M, Hansen T, Hansen TH, Harms PP, Hattersley AT, Hong M-G, Jacobsen UP, Jones AG, Koivula RW, Kokkola T, Mahajan A, Mari A, McCarthy MI, McDonald TJ, Musholt PB, Pavo I, Pearson ER, Pedersen O, Ruetten H, Rutters F, Schwenk JM, Sharma S, T Hart LM, Vestergaard H, Walker M, Vinuela A, Cordell HJ
Publication type: Article
Publication status: Published
Journal: PLOS Genetics
Year: 2025
Volume: 21
Issue: 7
Online publication date: 15/07/2025
Acceptance date: 18/06/2025
Date deposited: 04/08/2025
ISSN (print): 1553-7390
ISSN (electronic): 1553-7404
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pgen.1011776
DOI: 10.1371/journal.pgen.1011776
Data Access Statement: The molecular and clinical raw data as well as the processed data are available under restricted access due to the informed consent given by study participants, the various national ethical approvals for the present study, and the European General Data Protection Regulation (GDPR); individual-level clinical and molecular data cannot be transferred from the centralized IMI DIRECT repository. Requests for access will be informed about how data can be accessed via the IMI DIRECT secure analysis platform following submission of an appropriate application. The IMI DIRECT data access policy is available at https://directdiabetes.org
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