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

Elin Shaddox, Francesco C. Stingo (), Christine B. Peterson, Sean Jacobson, Charmion Cruickshank-Quinn, Katerina Kechris, Russell Bowler and Marina Vannucci
Additional contact information
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

Statistics in Biosciences, 2018, vol. 10, issue 1, No 5, 59-85

Abstract: 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.

Keywords: Gaussian graphical model; Bayesian inference; Markov random field prior; Spike-and-slab prior; Gene network; Chronic obstructive pulmonary disease (COPD) (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s12561-016-9176-6

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