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.
Data availability
The rumen metagenome sequence reads and associated metadata of the Australian dataset are publicly available at the National Center for Biotechnology’s Sequence Read Archive, Bioproject accession PRJNA1162230. The data on enteric methane emissions, associated metadata, host genotypes, and other supplementary material from the Australian dairy cattle population were generated using animals from the Ellinbank Research Farm. However, these data were produced under formal agreements involving the State Government and dairy industry co‑investment. As a result, the data are subject to third‑party governance and cannot be made publicly available. Dairy Australia and Agriculture Victoria act as custodians of the data on behalf of the contributing parties. Reasonable requests for access for non‑commercial research purposes may be considered via Prof. Jennie E. Pryce, AgriBio, 5 Ring Road, Bundoora VIC 3083, Australia (jennie.pryce@agriculture.vic.gov.au), subject to approval by the custodians and execution of an appropriate Data Use Agreement. The rumen metagenome, methane measurements, and metadata for the Spanish population are available at the ENA under bioproject accession PRJNA789746 (https://www.ebi.ac.uk/ena/browser/view/PRJEB44278), the GigaScience database (https://gigadb.org/dataset/100950), and in López-García et al.29. The genotypes of the Spanish dairy cattle should be addressed to Dr. Óscar González-Recio, CSIC, Dpt Mejora Genética Animal, Crta. de La Coruña km 7.5, 28040 Madrid, Spain; E-mail: (gonzalez.oscar@inia.csic.es). Supplementary Data 4, 5, and 6 have the numerical values used to generate the graphs presented in Figs. 2, 3, and 4, respectively.
Code availability
The code used to conduct the analyses and to generate the numerical source data required to reproduce the graphs and charts presented in the main figures is available at https://doi.org/10.5281/zenodo.1901889173. Supplementary Code 1 has the code to generate descriptive figures, estimate variance components and prediction accuracy of enteric methane emissions with BLUP models, and estimation of reference population size. Due to commercial restrictions, the input data for Supplementary Code 1 has the animal and sample IDs masked, and the genotypes, EME, metadata and fixed effects simulated; therefore, the results are not the same as those reported in the manuscript. Supplementary Code 2 is the code to identify KOs that could be from Bos taurus. Supplementary Code 3 is an example of the parameter file used as input in BayesR315.
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Acknowledgements
The authors are grateful for the financial support of DairyBio (Melbourne, Australia), funded by Dairy Australia (Melbourne, Australia), the Gardiner Foundation (Melbourne, Australia), and Agriculture Victoria (Melbourne, Australia). Likewise, the authors are grateful for the financial support of the METALGEN project (RTA2015-00022-C03) from the national plan for research, development, and innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). Part of the analyses were performed in the CESGA High-Performance Computing Centre (Galicia, Spain). The authors also thank the staff at Ellinbank SmartFarm (Ellinbank, Australia), regional Holstein associations, and farmers of Spain for their technical expertise and assistance. The authors thank Sunduimijid Bolormaa, of Agriculture Victoria Research, for inputting the genotypes of the Australian population. The authors extend their gratitude to Josie B. Garner, William J. Wales, and Peter J. Moate from Agriculture Victoria Research (Ellinbank) for their valuable contributions in developing the methane datasets used in this study.
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J.E.P., O.G-R., R.X., and B.J.S. conceived the study and designed the analyses. J.E.P., A.J.C., O.G-R., B.G.C., and R.X. supervised the analyses. J.L.J., L.C.M., S.R.O.W., A.G-R., and J.A.J-M. collected the phenotypes and monitored the environment. A.J.C., J.W., and C.P.P-W. processed the rumen samples in the laboratory. B.J.S. performed the analyses and wrote the first draft. All authors contributed to the formal data analysis, result interpretation, and discussions; approved the final manuscript for publication; and agreed with the order of presentation of the authors.
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Sepulveda, B.J., González-Recio, O., Chamberlain, A.J. et al. Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome. Commun Biol (2026). https://doi.org/10.1038/s42003-026-10048-8
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DOI: https://doi.org/10.1038/s42003-026-10048-8
