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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Published on: August 7, 2017

Detecting interaction networks in the human microbiome with conditional Granger causality.

Kumar Mainali¹, Sharon Bewick¹, Briana Vecchio-Pagan²

¹Department of Biology, University of Maryland, College Park, Maryland, United States of America.

Plos Computational Biology

|May 21, 2019

Summary

This summary is machine-generated.

Correlation analysis in human microbiome studies can be misleading. This research introduces causal models to reveal true microbial interactions, finding that correlation poorly predicts causation and highlighting distinct inter- and intraspecific interaction patterns.

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Area of Science:

Microbiome research
Computational biology
Systems ecology

Background:

Human microbiome studies often rely on correlation analysis to infer microbial interactions.
Correlation does not equate to causation, potentially misrepresenting ecological relationships due to factors like environmental filtering.
Existing methods may not accurately capture the causal dynamics within complex microbial communities.

Purpose of the Study:

To apply causal inference models to human microbiome time-series data.
To differentiate between correlation and causation in microbial interactions.
To understand the temporal dynamics and body-site specificity of microbial relationships.

Main Methods:

Analysis of a high-resolution, long-duration human microbiome time series.
Application of causal modeling techniques, specifically Granger causality.
Comparison of correlation networks with causation networks to assess interaction validity.

Main Results:

Correlation is an unreliable proxy for biological interactions in the human microbiome, showing a weak negative relationship with causality.
Strong interspecific interactions are predominantly positive, while strong intraspecific interactions are mainly negative.
Intraspecific interactions occur on a short timescale (1-3 days), and their presence is more conserved across body sites than interspecific interactions.

Conclusions:

Causal modeling provides a more accurate approach than correlation for deciphering microbial interactions in the human microbiome.
Understanding the distinct characteristics of inter- and intraspecific interactions is crucial for microbiome research.
Granger causality and related methods offer valuable tools for investigating the drivers of microbiome composition and structure.