A behavioral model for mapping the genetic architecture of gut-microbiota networks. (1st January 2021)

• Record Type: Journal Article
• Authors: Jiang, Libo; Liu, Xinjuan; He, Xiaoqing; Jin, Yi; Cao, Yige; Zhan, Xiang; Griffin, Christopher H.; Gragnoli, Claudia; Wu, Rongling
• Is Part Of: Gut microbes. Volume 13:Isuse 1(2021)
• Publication Date: 2021-01-01
• Subjects: The gut microbiota -- competition -- cooperation -- rule of thumb -- network science -- qtl; Gastrointestinal system -- Microbiology -- Periodicals; Microbiology -- Periodicals; Intestine, Small -- Periodicals; 616.3
• DOI: 10.1080/19490976.2020.1820847
• Languages: English
• ISSNs: 1949-0984

Abstract

ABSTRACT: The gut microbiota may play an important role in affecting human health. To explore the genetic mechanisms underlying microbiota-host relationships, many genome-wide association studies have begun to identify host genes that shape the microbial composition of the gut. It is becoming increasingly clear that the gut microbiota impacts host processes not only through the action of individual microbes but also their interaction networks. However, a systematic characterization of microbial interactions that occur in densely packed aggregates of the gut bacteria has proven to be extremely difficult. We develop a computational rule of thumb for addressing this issue by integrating ecological behavioral theory and genetic mapping theory. We introduce behavioral ecology theory to derive mathematical descriptors of how each microbe interacts with every other microbe through a web of cooperation and competition. We estimate the emergent properties of gut-microbiota networks reconstructed from these descriptors and map host-driven mutualism, antagonism, aggression, and altruism QTLs. We further integrate path analysis and mapping theory to detect and visualize how host genetic variants affect human diseases by perturbing the internal workings of the gut microbiota. As the proof of concept, we apply our model to analyze a published dataset of the gut microbiota, showing its usefulness and potential to gain new insight into how microbes are organized in human guts. The new model provides an analytical tool for revealing the "endophenotype" role of microbial networks in linking genotype to end-point phenotypes.