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Howey, R.* ; Adam, J. ; Adamski, J. ; Atabaki, N.N.* ; Brunak, S.* ; Chmura, P.J.* ; De Masi, F.* ; Dermitzakis, E.T.* ; Fernandez-Tajes, J.J.* ; Forgie, I.M.* ; Franks, P.W.* ; Giordano, G.N.* ; Haid, M. ; Hansen, T.* ; Hansen, T.H.* ; Harms, P.P.* ; Hattersley, A.T.* ; Hong, M.G.* ; Jacobsen, U.P.* ; Jones, A.G.* ; Koivula, R.W.* ; Kokkola, T.* ; Mahajan, A.* ; Mari, A.* ; McCarthy, M.I.* ; McDonald, T.J.* ; Musholt, P.B.* ; Pavo, I.* ; Pearson, E.R.* ; Pedersen, O.* ; Ruetten, H.* ; Rutters, F.* ; Schwenk, J.M.* ; Sharma, S. ; 't Hart, L.M.* ; Vestergaard, H.* ; Walker, M.* ; Viñuela, A.* ; Cordell, H.J.*

Bayesian network imputation methods applied to multi-omics data identify putative causal relationships in a type 2 diabetes dataset containing incomplete data: An IMI DIRECT Study.

PLoS Genet. 21:e1011776 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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 pre-defined 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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Mendelian Randomization; Reveals; Design
ISSN (print) / ISBN 1553-7390
e-ISSN 1553-7404
Journal PLoS Genetics
Quellenangaben Volume: 21, Issue: 7, Pages: , Article Number: e1011776 Supplement: ,
Publisher Public Library of Science (PLoS)
Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Experimental Genetics (IEG)
CF Metabolomics & Proteomics (CF-MPC)
Grants Exeter NIHR CRF and Exeter NIHR BRC
European Union
Innovative Medicines Initiative Joint Undertaking
IMI DIRECT Consortium
Wellcome Trust