ArticleMicrobial genomics2026
Harnessing gastrointestinal microbial co-association networks to predict feed efficiency and methane emissions across beef and dairy cattle.
Article in Microbial genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Harnessing gastrointestinal microbial co-association networks to predict feed efficiency and methane emissions across beef and dairy cattle.Microbial genomics · 2026Article
- Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds.The ISME journal · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enteric methane emissions from cattle pose a significant environmental concern and represent a substantial energy loss for the animal, necessitating the development of effective mitigation strategies. The gastrointestinal microbiota plays a crucial role in determining both feed efficiency and methane production. Still, the specific microbial signatures that predict these traits across different production systems remain poorly understood. This study aimed to identify common predictive microbial biomarkers for feed efficiency and methane emissions using co-association network analysis across contrasting cattle production systems. Rumen liquid and faecal microbiota from 55 Charolais heifers (beef) and 56 Holstein cows (dairy) were analysed using 16S rRNA gene amplicon sequencing. Phenotypic data included feed efficiency, methane yield and acetate/propionate ratio. Co-association networks were constructed using Partial Correlation and Information Theory to identify amplicon sequence variants (ASVs) directly connected to phenotypes. Multiple regression analysis determined the minimal ASV sets required to achieve optimal predictive accuracy. Rumen microbiomes consistently showed superior predictive performance compared to faecal communities across all traits. Network-selected ASVs explained substantial phenotypic variance across traits and production systems (
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Registered trials
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