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Detalle de: Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome

Title Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome
Abstract

The production of methane, a potent greenhouse gas, by ruminants during feed digestion is designated enteric methane emissions (EME) and is mainly produced by the rumen microbiome. Reliably recording EME in large populations is currently cost-prohibitive, hampering farming decisions aimed at reducing EME. Here, we perform comprehensive analyses on host genetics, KEGG orthology groups (KOs) from the rumen metagenome, and EME of more than 800 cows from Australia and Spain. We report that the rumen microbiome explains up to 34% of the EME variance, and when combined with the host genome, the variance explained is up to 59% with prediction accuracies of up to 0.40. The results support a recursive model, where both the host genome and rumen metagenome explain EME. The isometric log-ratio transformation of KOs may potentially better capture relationships between host genetics and the rumen microbiome than the centered log-ratio transformation, and BayesR yielded slightly higher microbe‑explained EME variance than best linear unbiased prediction. A forward simulation estimated to reach 90% of EME prediction accuracy with 6,000 animals with rumen microbiomes and host genomes, which could open opportunities for developing strategies to reduce EME. Our study contributes to the foundation for reducing EME, supporting global warming mitigation.

Apa Reference Sepulveda, B.J., González-Recio, O., Chamberlain, A.J., Xiang, R., Cocks, B.G., Wang, J., Prowse-Wilkins, C.P., Marett, L.C., Williams, S.R.O., Jacobs, J.L., García-Rodríguez, A., Jiménez-Montero, J.A. & Pryce, J.E. (2026). Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome. Communications Biology, 9(1). 10.1038/s42003-026-10048-8
Doi 10.1038/s42003-026-10048-8
Publication Date 13 Apr 2026
Publication Type Article
Researcher Oscar Gonzalez-Recio
Ontology Functional annotation, Computational models and simulations, Pathway analysis, Microbial communities, Metagenomics, Comparative Genomics, Climate change adaptation
Research Groups
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