Multi-omics analysis

Meta-data Analysis of Ruminant Microbial Communities for Methane Prediction

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Abstract

Rumen microbial communities (RMC) are a vital component of the digestion and fermentation process in the rumen, breaking down complex plant material into energy. This process creates methane as a byproduct. The RMC has been linked to variation in methane emissions across individuals through abundance of genera and functional pathways. The RMC is strongly influenced by diet and controlled by host genomics but also shares a core RMC across ruminant species. We therefore hypothesise that methane emissions can be predicted across species through variations in the RMC. In this study, we examined differences in the RMC in sheep (n=241), cattle (n=39) and deer (n=42) using genera and functional pathway counts for high and low methane individuals. These differences were analysed through three different sequencing methods; reduced representational sequencing of microbial DNA, metagenomic DNA, and meta-transcriptomic RNA. Alpha diversity revealed significant differences across ruminant species and high and low methane individuals. Methane prediction models were trained on sheep from high and low methane breeding lines and tested on cattle grouped into high and low methane based on breeding values. Results suggest there was some predictive ability to separate high and low methane emitting cattle when trained on sheep data (classification rate of 0.590 using genera counts), where genera previously associated with methane emissions were providing the most information in separating high and low methane animals. These results suggest low methane emitting animals may be determined across species, which would facilitate identifying low methane emitting animals in species (such as deer) that are difficult to measure for methane.

Keywords: 2026

How to Cite:

Perry, B., Bilton, T., Henry, H., McNaughton, L., Clarke, J., McRae, K., Thompson, B., McEwan, J., Black, M. & Rowe, S., (2026) “Meta-data Analysis of Ruminant Microbial Communities for Methane Prediction”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2285177. doi: https://doi.org/10.31274/wcgalp.23636

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