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
Rattachement africain : gb, us, de, si, sg, dk, se, ch, nl, fi, it, fr. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- 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
- Date Crossref
- 15/07/2025
- Éditeur
- Public Library of Science (PLoS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Newcastle University Research Software Engineering pays non établi dans la noticeUniversité ou école supérieure
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Center for Environmental Health Metabolomics and Proteomics Core pays non établi dans la noticeStructure de recherche
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Helmholtz Munich pays non établi dans la noticeStructure de recherche
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University of Ljubljana pays non établi dans la noticeUniversité ou école supérieure
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National University of Singapore pays non établi dans la noticeUniversité ou école supérieure
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University of Copenhagen pays non établi dans la noticeUniversité ou école supérieure
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Lund University Department of Clinical Science pays non établi dans la noticeUniversité ou école supérieure
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Novo Nordisk Foundation pays non établi dans la noticeStructure de recherche
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University of Oxford Endocrinology and Metabolism pays non établi dans la noticeUniversité ou école supérieure
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Oxford Centre for Diabetes pays non établi dans la noticeStructure de recherche
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Technical University of Denmark Department of Health Technology pays non établi dans la noticeUniversité ou école supérieure
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University of Geneva Department of Genetic Medicine and Development pays non établi dans la noticeUniversité ou école supérieure
Research Software Engineering — Newcastle University, Metabolomics and Proteomics Core — Center for Environmental Health et Helmholtz Munich, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.