Predicting feed efficiency of Angus steers using the gastrointestinal microbiome

Area of Science:

• Animal Science
• Microbiology
• Genomics

Background:

• Gastrointestinal microbial composition influences feed efficiency variations in ruminants.
• Next-generation sequencing has advanced microbiome research in livestock.
• Understanding the gut microbiome is key to improving beef cattle production.

Purpose of the Study:

• To analyze beef cattle microbiome data using multivariate and univariate approaches.
• To develop a statistical method for classifying cattle based on microbiota and residual feed intake (RFI).
• To compare the effectiveness of different analytical methods for microbiome data.

Main Methods:

• Collected fecal and ruminal samples from 63 Angus steers at multiple time points.
• Utilized Canonical Discriminant Analysis (CDA) and Stepwise Discriminant Analysis.
• Classified steers into positive and negative RFI groups based on fecal microbiota profiles.

Main Results:

• CDA successfully distinguished between ruminal and fecal samples, identifying key bacterial families (e.g., Prevotellaceae in rumen, Peptostreptococcaceae in feces).
• A multivariate approach using 19 bacterial families correctly assigned all animals to their respective RFI groups.
• Rhizobiaceae was associated with negative RFI, while Comamonadacea was linked to positive RFI.

Conclusions:

• Multivariate analysis enhances microbiome data interpretation in beef cattle.
• Fecal microbiome profiling is a viable method for predicting feed efficiency.
• This approach can aid in selecting animals with improved feed conversion.