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High-dimensional Bayesian network inference from systems genetics data using genetic node ordering

Lingfei Wang, P. Audenaert (UGent) and Tom Michoel (UGent)
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Abstract
Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher overlap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.
Keywords
INTEGRATIVE GENOMICS APPROACH, COMPLEX TRAITS, EXPRESSION, RECONSTRUCTION, ARCHITECTURE, SELECTION, MAP, systems genetics, network inference, Bayesian network, expression, quantitative trait loci analysis, gene expression

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MLA
Wang, Lingfei, et al. “High-Dimensional Bayesian Network Inference from Systems Genetics Data Using Genetic Node Ordering.” FRONTIERS IN GENETICS, vol. 10, 2019, doi:10.3389/fgene.2019.01196.
APA
Wang, L., Audenaert, P., & Michoel, T. (2019). High-dimensional Bayesian network inference from systems genetics data using genetic node ordering. FRONTIERS IN GENETICS, 10. https://doi.org/10.3389/fgene.2019.01196
Chicago author-date
Wang, Lingfei, Pieter Audenaert, and Tom Michoel. 2019. “High-Dimensional Bayesian Network Inference from Systems Genetics Data Using Genetic Node Ordering.” FRONTIERS IN GENETICS 10. https://doi.org/10.3389/fgene.2019.01196.
Chicago author-date (all authors)
Wang, Lingfei, Pieter Audenaert, and Tom Michoel. 2019. “High-Dimensional Bayesian Network Inference from Systems Genetics Data Using Genetic Node Ordering.” FRONTIERS IN GENETICS 10. doi:10.3389/fgene.2019.01196.
Vancouver
1.
Wang L, Audenaert P, Michoel T. High-dimensional Bayesian network inference from systems genetics data using genetic node ordering. FRONTIERS IN GENETICS. 2019;10.
IEEE
[1]
L. Wang, P. Audenaert, and T. Michoel, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering,” FRONTIERS IN GENETICS, vol. 10, 2019.
@article{8667083,
  abstract     = {{Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher overlap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.}},
  articleno    = {{1196}},
  author       = {{Wang, Lingfei and Audenaert, P. and Michoel, Tom}},
  issn         = {{1664-8021}},
  journal      = {{FRONTIERS IN GENETICS}},
  keywords     = {{INTEGRATIVE GENOMICS APPROACH,COMPLEX TRAITS,EXPRESSION,RECONSTRUCTION,ARCHITECTURE,SELECTION,MAP,systems genetics,network inference,Bayesian network,expression,quantitative trait loci analysis,gene expression}},
  language     = {{eng}},
  pages        = {{13}},
  title        = {{High-dimensional Bayesian network inference from systems genetics data using genetic node ordering}},
  url          = {{http://dx.doi.org/10.3389/fgene.2019.01196}},
  volume       = {{10}},
  year         = {{2019}},
}

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