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

TY - JOUR

T1 - Bayesian network imputation methods applied to multi-omics data identify putative causal relationships in a type 2 diabetes dataset containing incomplete data

T2 - An IMI DIRECT Study

AU - Howey, Richard

AU - Adam, Jonathan

AU - Adamski, Jerzy

AU - Atabaki, Natalie N.

AU - Brunak, Søren

AU - Chmura, Piotr Jaroslaw

AU - De Masi, Federico

AU - Dermitzakis, Emmanouil T.

AU - Fernandez-Tajes, Juan J.

AU - Forgie, Ian M.

AU - Franks, Paul W.

AU - Giordano, Giuseppe N.

AU - Haid, Mark

AU - Hansen, Torben

AU - Hansen, Tue H.

AU - Harms, Peter P.

AU - Hattersley, Andrew T.

AU - Hong, Mun Gwan

AU - Jacobsen, Ulrik Plesner

AU - Jones, Angus G.

AU - Koivula, Robert W.

AU - Kokkola, Tarja

AU - Mahajan, Anubha

AU - Mari, Andrea

AU - McCarthy, Mark I.

AU - McDonald, Timothy J.

AU - Musholt, Petra B.

AU - Pavo, Imre

AU - Pearson, Ewan R.

AU - Pedersen, Oluf

AU - Ruetten, Hartmut

AU - Rutters, Femke

AU - Schwenk, Jochen M.

AU - Sharma, Sapna

AU - T Hart, Leen M.

AU - Vestergaard, Henrik

AU - Walker, Mark

AU - The IMI DIRECT consortium

AU - Viñuela, Ana

AU - Cordell, Heather J.

N1 - Publisher Copyright: © 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.

PY - 2025/7/15

Y1 - 2025/7/15

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105011320794

U2 - 10.1371/journal.pgen.1011776

DO - 10.1371/journal.pgen.1011776

M3 - Article

C2 - 40663565

AN - SCOPUS:105011320794

SN - 1553-7390

VL - 21

JO - PLoS Genetics

JF - PLoS Genetics

IS - 7

M1 - e1011776

ER -