Evidence map›Paper›PMID 41975031›Full record

ArticleCommunications biology2026

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

Boris J Sepulveda, Oscar González-Recio, Amanda J Chamberlain, Ruidong Xiang, Benjamin G Cocks, Jianghui Wang, Claire P Prowse-Wilkins, Leah C Marett, S Richard O Williams, Joe L Jacobs and 3 more

Abstract read
In one paragraph

Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Boris J SepulvedaAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia. boris.sepulveda@agriculture.vic.gov.au.ORCID http://orcid.org/0000-0002-6946-7376
Oscar González-RecioDepartamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (CSIC), Madrid, Spain.ORCID http://orcid.org/0000-0002-9106-4063
Amanda J ChamberlainAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.ORCID http://orcid.org/0000-0002-9395-1299
Ruidong XiangAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.ORCID http://orcid.org/0000-0002-1584-7605
Benjamin G CocksSchool of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.
Jianghui WangAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
Claire P Prowse-WilkinsAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.ORCID http://orcid.org/0000-0002-7506-862X
Leah C MarettAgriculture Victoria Research, Ellinbank, Bundoora, VIC, Australia.
S Richard O WilliamsAgriculture Victoria Research, Ellinbank, Bundoora, VIC, Australia.ORCID http://orcid.org/0000-0003-1321-6487
Joe L JacobsSchool of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.ORCID http://orcid.org/0000-0003-2104-4320
Aser García-RodríguezDepartment of Animal Production, NEIKER-BRTA, Granja Modelo de Arkaute, Vitoria-Gasteiz, Spain.
Jose A Jiménez-MonteroSpanish Holstein Association (CONAFE), CTRA, Madrid, Spain.
Jennie E PryceAgriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.

Funding

Dairy Australia DairyBio
6 · The paper itself

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.

Indexed as

Gastrointestinal MicrobiomeMethaneMicrobiotaRumenAnimalsAustraliaCattleMetagenomeMethane

Identifiers

PMID41975031
PMCPMC13265817

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.