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.

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.

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 provi...

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Main Authors: Richard Howey, Jonathan Adam, Jerzy Adamski, Natalie N Atabaki, Søren Brunak, Piotr Jaroslaw Chmura, Federico De Masi, Emmanouil T Dermitzakis, Juan J Fernandez-Tajes, Ian M Forgie, Paul W Franks, Giuseppe N Giordano, Mark Haid, Torben Hansen, Tue H Hansen, Peter P Harms, Andrew T Hattersley, Mun-Gwan Hong, Ulrik Plesner Jacobsen, Angus G Jones, Robert W Koivula, Tarja Kokkola, Anubha Mahajan, Andrea Mari, Mark I McCarthy, Timothy J McDonald, Petra B Musholt, Imre Pavo, Ewan R Pearson, Oluf Pedersen, Hartmut Ruetten, Femke Rutters, Jochen M Schwenk, Sapna Sharma, Leen M 't Hart, Henrik Vestergaard, Mark Walker, IMI DIRECT Consortium, Ana Viñuela, Heather J Cordell
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-07-01
Series:PLoS Genetics
Online Access:https://doi.org/10.1371/journal.pgen.1011776
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https://doi.org/10.1371/journal.pgen.1011776

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