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A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD

Author

Listed:
  • Elin Shaddox

    (Rice University)

  • Francesco C. Stingo

    (University of Florence)

  • Christine B. Peterson

    (UT MD Anderson Cancer Center)

  • Sean Jacobson

    (National Jewish Health)

  • Charmion Cruickshank-Quinn

    (University of Colorado Denver)

  • Katerina Kechris

    (University of Colorado Denver)

  • Russell Bowler

    (National Jewish Health)

  • Marina Vannucci

    (Rice University)

Abstract

In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.

Suggested Citation

  • Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-016-9176-6
    DOI: 10.1007/s12561-016-9176-6
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    References listed on IDEAS

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    8. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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    Cited by:

    1. Yize Zhao & Zhe Sun & Jian Kang, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 279-286, June.
    2. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
    3. Christine B. Peterson & Nathan Osborne & Francesco C. Stingo & Pierrick Bourgeat & James D. Doecke & Marina Vannucci, 2020. "Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease," Biometrics, The International Biometric Society, vol. 76(4), pages 1120-1132, December.

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