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

Area of Science:

• Animal Science
• Microbiome Research
• Genetics and Genomics

Background:

• Enteric methane emissions (EME) from ruminants are a significant source of greenhouse gases.
• Current methods for measuring EME in large cattle populations are cost-prohibitive, hindering mitigation efforts.
• The rumen microbiome and host genetics are known to influence EME, but their combined predictive power is not fully understood.

Purpose of the Study:

• To comprehensively analyze the contributions of host genetics and rumen metagenome to enteric methane emissions (EME) in cattle.
• To develop predictive models for EME using host and microbiome data.
• To explore potential strategies for reducing EME and mitigating global warming.

Main Methods:

• Analysis of host genetics, KEGG orthology groups (KOs) from rumen metagenomes, and EME data from over 800 cows.
• Application of isometric log-ratio (ILR) and centered log-ratio (CLR) transformations for microbiome data.
• Comparison of prediction models including BayesR and best linear unbiased prediction (BLUP).

Main Results:

• The rumen microbiome alone explained up to 34% of EME variance.
• Combining host genome and rumen microbiome data explained up to 59% of EME variance with prediction accuracies up to 0.40.
• The ILR transformation of KOs showed potential for better capturing host-microbiome relationships than CLR; BayesR outperformed BLUP.

Conclusions:

• Both host genetics and the rumen microbiome are crucial determinants of enteric methane emissions (EME).
• Predictive models integrating host and microbiome data offer a promising avenue for large-scale EME assessment.
• These findings provide a foundation for developing targeted strategies to reduce EME in livestock, contributing to climate change mitigation.