Detecting interaction networks in the human microbiome with conditional Granger causality

• Published in: PLoS computational biology 2019-05, Vol.15 (5), p.e1007037-e1007037
• Main Authors: Mainali, Kumar, Bewick, Sharon, Vecchio-Pagan, Briana, Karig, David, Fagan, William F
• Format: Article
• Language: English
• Subjects: Applied physics; Bioengineering; Bioinformatics; Biology; Biology and Life Sciences; Causality; Causation; Computational Biology; Computer and Information Sciences; Correlation analysis; Ecological effects; Ecology; Ecology and Environmental Sciences; Economic models; Funding; Gastrointestinal Microbiome; Gene expression; Genera; Hand - microbiology; Humans; Identification and classification; Interspecific; Laboratories; Medicine and Health Sciences; Metabolism; Metabolites; Microbial Interactions; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Microorganisms; Models, Biological; Networks; Physiological aspects; Proxy; Sequences; Species Specificity; Statistical models; Studies; Supervision; Taxa; Time series; Tongue - microbiology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007037

Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction; we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.