  1. In Bayesian Network Regression models, networks are considered the
predictors of continuous responses. These models have been successfully used in
brain research to identify regions in the brain that are associated with
specific human traits, yet their potential to elucidate microbial drivers in
biological phenotypes for microbiome research remains unknown. In particular,
microbial networks are challenging due to their high-dimension and high
sparsity compared to brain networks. Furthermore, unlike in brain connectome
research, in microbiome research, it is usually expected that the presence of
microbes have an effect on the response (main effects), not just the
interactions.
  2. Here, we develop the first thorough investigation of whether Bayesian
Network Regression models are suitable for microbial datasets on a variety of
synthetic and real data under diverse biological scenarios. We test whether the
Bayesian Network Regression model that accounts only for interaction effects
(edges in the network) is able to identify key drivers (microbes) in phenotypic
variability.
  3. We show that this model is indeed able to identify influential nodes and
edges in the microbial networks that drive changes in the phenotype for most
biological settings, but we also identify scenarios where this method performs
poorly which allows us to provide practical advice for domain scientists aiming
to apply these tools to their datasets.
  4. BNR models provide a framework for microbiome researchers to identify
connections between microbes and measured phenotypes. We allow the use of this
statistical model by providing an easy-to-use implementation which is publicly
available Julia package at
https://github.com/solislemuslab/BayesianNetworkRegression.jl.
