Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits

Authors

Melanie K. Hess, University Invermay Agricultural Centre, University of Nebraska-Lincoln
Larissa Zetouni, University Invermay Agricultural Centre, Wageningen University & Research
Andrew S. Hess, University Invermay Agricultural Centre, University of Nevada
Juliana Budel, University Invermay Agricultural Centre, Universidade Federal do Pará
Ken G. Dodds, University Invermay Agricultural Centre
Hannah M. Henry, University Invermay Agricultural Centre
Rudiger Brauning, University Invermay Agricultural Centre
Alan F. McCulloch, University Invermay Agricultural Centre
Sharon M. Hickey, Ruakura Research Centre
Patricia L. Johnson, University Invermay Agricultural Centre
Sara Elmes, Deer Industry New Zealand
Janine Wing, Pāmu, Landcorp Farming Ltd
Brooke Bryson, Woodlands Research Farm
Kevin Knowler, University Invermay Agricultural Centre
Dianne Hyndman, University Invermay Agricultural Centre
Hayley Baird, University Invermay Agricultural Centre
Kathryn M. McRae, University Invermay Agricultural Centre
Arjan Jonker, Grasslands Research Centre
Peter H. Janssen, Grasslands Research Centre
John C. McEwan, University Invermay Agricultural Centre
Suzanne J. Rowe, University Invermay Agricultural Centre

Date of this Version

7-23-2023

Citation

Hess et al. Genetics Selection Evolution (2023) 55:53 https://doi.org/10.1186/s12711-023-00822-1

Comments

Open access.

Abstract

Background Rumen microbes break down complex dietary carbohydrates into energy sources for the host and are increasingly shown to be a key aspect of animal performance. Host genotypes can be combined with microbial DNA sequencing to predict performance traits or traits related to environmental impact, such as enteric methane emissions. Metagenome profiles were generated from 3139 rumen samples, collected from 1200 dual purpose ewes, using restriction enzyme-reduced representation sequencing (RE-RRS). Phenotypes were available for methane (CH4) and carbon dioxide (CO2) emissions, the ratio of CH4 to CH4 plus CO2 (CH4Ratio), feed efficiency (residual feed intake: RFI), liveweight at the time of methane collection (LW), liveweight at 8 months (LW8), fleece weight at 12 months (FW12) and parasite resistance measured by faecal egg count (FEC1). We estimated the proportion of phenotypic variance explained by host genetics and the rumen microbiome, as well as prediction accuracies for each of these traits.

Results Incorporating metagenome profiles increased the variance explained and prediction accuracy compared to fitting only genomics for all traits except for CO2 emissions when animals were on a grass diet. Combining the metagenome profile with host genotype from lambs explained more than 70% of the variation in methane emissions and residual feed intake. Predictions were generally more accurate when incorporating metagenome profiles compared to genetics alone, even when considering profiles collected at different ages (lamb vs adult), or on different feeds (grass vs lucerne pellet). A reference-free approach to metagenome profiling performed better than metagenome profiles that were restricted to capturing genera from a reference database. We hypothesise that our referencefree approach is likely to outperform other reference-based approaches such as 16S rRNA gene sequencing for use in prediction of individual animal performance.

Conclusions This paper shows the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits. A reference-free approach using a microbial relationship matrix from log10 proportions of each tag normalized within cohort (i.e., the group of animals sampled at the same time) is recommended for future predictions using RE-RRS metagenome profiles.