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. 2025 Jul 15;21(7):e1011776.
doi: 10.1371/journal.pgen.1011776. eCollection 2025 Jul.

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

Richard Howey  1   2 , Jonathan Adam  3 , Jerzy Adamski  4   5   6 , Natalie N Atabaki  7   8   9 , Søren Brunak  10   11 , Piotr Jaroslaw Chmura  10 , Federico De Masi  12 , Emmanouil T Dermitzakis  13 , Juan J Fernandez-Tajes  14 , Ian M Forgie  15 , Paul W Franks  9   16 , Giuseppe N Giordano  9 , Mark Haid  17 , Torben Hansen  7 , Tue H Hansen  18   19 , Peter P Harms  20 , Andrew T Hattersley  21   22 , Mun-Gwan Hong  23 , Ulrik Plesner Jacobsen  10 , Angus G Jones  21   22 , Robert W Koivula  8 , Tarja Kokkola  24 , Anubha Mahajan  14 , Andrea Mari  25 , Mark I McCarthy  14 , Timothy J McDonald  21   26 , Petra B Musholt  27 , Imre Pavo  28 , Ewan R Pearson  15 , Oluf Pedersen  7   29 , Hartmut Ruetten  30 , Femke Rutters  31 , Jochen M Schwenk  23 , Sapna Sharma  3 , Leen M 't Hart  31   32 , Henrik Vestergaard  7   33 , Mark Walker  34 ; IMI DIRECT Consortium; Ana Viñuela  35   36 , Heather J Cordell  2
Affiliations

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

Richard Howey et al. PLoS Genet. .

Abstract

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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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: HJC and AV serve on the editorial board of PLOS Genetics. MMcC and AM are currently employees of Genentech and holders of Roche stock.

Figures

Fig 1
Fig 1. Average BN constructed using imputed data of all variables with strength threshold 0.5.
Edges are labelled with the probability that they exist (strength), and, in brackets, the probability that they exist in the shown direction, given that they exist (direction). The thickness of the edges is proportional to the edge strength. The nodes are coloured as follows: red are metabolites; blue (with gene name) are proteins; purple (with gene name) are gene expression measurements; amber are clinical variables; green (prefixed with AS) are allele scores.
Fig 2
Fig 2. Markov Blanket of type 2 diabetes diagnosis taken from the average BN constructed using imputed data of all variables with strength threshold 0.5.
Edges are labelled with the probability that they exist (strength), and, in brackets, the probability that they exist in the shown direction, given that they exist (direction). The thickness of the edges is proportional to the edge strength. The nodes are coloured as follows: red are metabolites; blue (with gene name) are proteins; purple (with gene name) are gene expression measurements; amber are clinical variables; green (prefixed with AS) are allele scores.
Fig 3
Fig 3. Markov Blanket of BMI.
All edges and nodes show a Markov Blanket of BMI taken from the average BN constructed using imputed data of all variables with strength threshold 0.5. Edges and nodes that are not faded show a Markov Blanket of BMI from the average BN with a strength threshold of 0.85 applied instead of 0.5. The thickness of the edges is proportional to the edge strength. Non-faded edges are highlighted in black and labelled in red with the probability that they exist (strength), and, in brackets, the probability that they exist in the shown direction, given that they exist (direction). Nodes are coloured as follows: red are metabolites; blue are proteins; purple are gene expression measurements; amber are clinical variables; green are allele scores.
Fig 4
Fig 4. Sub-network taken from the average BN constructed using imputed data of all variables consisting of variables of interest with respect to T2D and BMI.
Edges are labelled with the probability that they exist (strength), and, in brackets, the probability that they exist in the shown direction, given that they exist (direction). The thickness of the edges is proportional to the edge strength. The nodes are coloured as follows: amber are clinical variables and green are allele scores.
Fig 5
Fig 5. Markov Blanket of centre.
All edges and nodes show a Markov Blanket of centre taken from the average BN constructed using imputed data of all variables with strength threshold 0.5. Edges and nodes that are not faded show a Markov Blanket of centre from the average BN with a strength threshold of 0.9 applied instead of 0.5. The thickness of the edges is proportional to the edge strength. Non-faded edges are highlighted in black and labelled in red with the probability that they exist (strength), and, in brackets, the probability that they exist in the shown direction, given that they exist (direction); their connected nodes are also labelled and highlighted. Nodes are coloured as follows: red are metabolites; blue (with gene name) are proteins; purple (with gene name) are gene expression measurements; amber are clinical variables; green (prefixed with AS) are allele scores.
See this image and copyright information in PMC

References

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