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Rumen metagenome as a genomic selection target to reduce enteric methane emissions. | LitMetric

Rumen metagenome as a genomic selection target to reduce enteric methane emissions.

J Dairy Sci

Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia.

Published: August 2025


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Article Abstract

Ruminant digestion emits methane, a potent greenhouse gas contributing to global warming and reducing feed efficiency. Reducing enteric methane emissions (EME) through breeding decisions is theoretically possible, yet measuring these emissions on commercial farms is currently challenging and costly. It is common for EME to be measured using different technologies, which may show weak correlations between them, complicating the combination of reference populations, especially between countries. Here, using the same sequencing strategy, we identified a group of ruminant metagenomic features (a core) present in at least 90% of 410 dairy cows in Australia and 434 in Spain. With subsets of this core (the breeding core subsets) we estimated larger reductions on EME than using direct selection on EME. A combination of direct selection on EME and indirect selection on the breeding core subsets was estimated to produce even larger reductions. Combining the principal components of the core with some genera, Kyoto Encyclopedia of Genes and Genomes ontology and Clusters of Orthologous Groups could enhance EME reductions in breeding programs. We estimated an EME reduction of 0.41 phenotypic standard deviations per generation by selecting the top 30% of individuals with desirable ruminal microbiota profiles. An R Shiny application to estimate those reductions is provided. Additionally, the breeding core subsets could predict EME irrespective of each population's EME trait (sulfur hexafluoride in Australia and sniffers in Spain). These results suggest that rumen metagenome features could be used as selection criteria for genomic selection programs to reduce EME, as many of these features are heritable and correlated with EME. Features in the core could connect EME from different cattle populations, irrespective of the methane phenotype used in those populations. We propose that our methodology should be applied to much larger datasets to improve the accuracy of identifying a breeding core. Therefore, we propose a global effort to validate a common core of EME-associated ruminal features.

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Source
http://dx.doi.org/10.3168/jds.2024-25436DOI Listing

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