Identifying microbial drivers in biological phenotypes with a Bayesian Network Regression model

TL;DR

This paper evaluates the suitability of Bayesian Network Regression models for identifying microbial drivers in biological phenotypes, demonstrating their effectiveness in various scenarios and providing a practical implementation for microbiome research.

Contribution

It is the first comprehensive study assessing Bayesian Network Regression models for microbial data, highlighting their strengths and limitations in microbiome-phenotype association analysis.

Findings

BNR models can identify influential microbes and interactions affecting phenotypes.

The model performs well in most biological scenarios but has limitations in some cases.

A user-friendly Julia package is provided for microbiome researchers.

Abstract

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.