Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle

Ross, Elizabeth M., Moate, Peter J., Marett, Leah C., Cocks, Ben G. and Hayes, Ben J. (2013) Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle. PLoS One, 8 9: . doi:10.1371/journal.pone.0073056


Author Ross, Elizabeth M.
Moate, Peter J.
Marett, Leah C.
Cocks, Ben G.
Hayes, Ben J.
Title Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-09-04
Sub-type Article (original research)
DOI 10.1371/journal.pone.0073056
Open Access Status DOI
Volume 8
Issue 9
Total pages 8
Place of publication San Francisco, United States
Publisher Public Library of Science
Language eng
Abstract Mammals have a large cohort of endo- and ecto- symbiotic microorganisms (the microbiome) that potentially influence host phenotypes. There have been numerous exploratory studies of these symbiotic organisms in humans and other animals, often with the aim of relating the microbiome to a complex phenotype such as body mass index (BMI) or disease state. Here, we describe an efficient methodology for predicting complex traits from quantitative microbiome profiles. The method was demonstrated by predicting inflammatory bowel disease (IBD) status and BMI from human microbiome data, and enteric greenhouse gas production from dairy cattle rumen microbiome profiles. The method uses unassembled massively parallel sequencing (MPS) data to form metagenomic relationship matrices (analogous to genomic relationship matrices used in genomic predictions) to predict IBD, BMI and methane production phenotypes with useful accuracies (r = 0.423, 0.422 and 0.466 respectively). Our results show that microbiome profiles derived from MPS can be used to predict complex phenotypes of the host. Although the number of biological replicates used here limits the accuracy that can be achieved, preliminary results suggest this approach may surpass current prediction accuracies that are based on the host genome. This is especially likely for traits that are largely influenced by the gut microbiota, for example digestive tract disorders or metabolic functions such as enteric methane production in cattle.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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