# Prediction accuracy for feed intake and body weight gain using host genomic and rumen metagenomic data in beef cattle

## TL;DR

This study shows that combining beef cattle's genetic data with gut microbiome data improves predictions of feed intake and weight gain, helping optimize cattle management.

## Contribution

The novel contribution is evaluating the combined use of host genomics and rumen metagenomics for predicting beef cattle feed efficiency traits.

## Key findings

- Combined genomic and metagenomic models outperformed models using either data source alone.
- The rumen metagenome explains a significant proportion of variation in feed efficiency traits.
- Prediction accuracy varied depending on how metagenomic data was modeled.

## Abstract

Host genomic and rumen metagenome data can predict feed efficiency traits, supporting management decisions and increasing profitability. This study estimated the proportion of variation of average daily dry matter intake and average daily gain explained by the rumen metagenome in beef cattle, evaluated prediction accuracy using genomic data, metagenomic data, or their combination, and explored methods for modelling the rumen metagenome to improve phenotypic prediction accuracy. Data from 717 animals on four diets (two concentrate-based and two forage-based) were analyzed. Animal genotypes consisted of 749,922 imputed sequence variants, while metagenomic data comprised 16,583 open reading frames from ruminal microbiota. The metagenome was modelled using six (co)variance matrices, based on combinations of two creation methods and three modifications. Nineteen mixed linear models were used per trait: one with genomic effects only, six with metagenomic effects, six combining genomic and metagenomic effects, and six adding interaction effects. Two cross-validation schemes were applied to evaluate prediction accuracy: fourfold cross-validation balanced for diet type with 5 replicates and leave-one-diet-out cross-validation, where three diets served as training and the fourth as testing. Prediction accuracy was measured as the correlation between an animal’s summed random effects and its adjusted phenotype.

Although minimal, differences existed in parameter estimates and validation accuracy depending on how the metagenome effect was modelled. Median phenotype prediction accuracy ranged from −0.01 to 0.28. No specific set of model characteristics consistently lead to the highest accuracies. Models which combined genome and metagenome data outperformed those using either data source alone. Models where the rumen metagenome (co)variances matrix was scaled within each diet composition generally led to lower prediction accuracies in this study.

The rumen metagenome can explain a significant proportion of variation in beef cattle feed efficiency traits. Those traits can also be predicted using either host genome or rumen metagenome, though using both sources of information proved more accurate. Multiple methods of forming the metagenome (co)variance matrix can lead to similar prediction accuracies.

The online version contains supplementary material available at 10.1186/s12711-025-01007-8.