Cited by 374Ruminant Nutrition and Digestive PhysiologyGenetic and phenotypic traits in livestockAnaerobic Digestion and Biogas Production

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Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

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Roehe, Rainer, Dewhurst, Richard J., Duthie, Carol-Anne, Rooke, John A., McKain, Nest, Ross, Dave W., Hyslop, Jimmy J., Waterhouse, Anthony, Freeman, Tom C., Watson, Mick, Wallace, R. John·2016·PLoS Genetics

DOI 10.1371/journal.pgen.1005846PMC PMC4758630

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Abstract

Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome.

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Additional file 3 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
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Additional file 6 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
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Additional file 2 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
Hess, M. K., Hodgkinson, H. E., Hess, A. S., Zetouni, L., Budel, J. C. D. C., Henry, H., Donaldson, A., Bilton, T. P., van Stijn, T. C., Kirk, M. R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24159505
Additional file 4 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
Hess, M. K., Hodgkinson, H. E., Hess, A. S., Zetouni, L., Budel, J. C. D. C., Henry, H., Donaldson, A., Bilton, T. P., van Stijn, T. C., Kirk, M. R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24159511
Additional file 1 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
Hess, M. K., Hodgkinson, H. E., Hess, A. S., Zetouni, L., Budel, J. C. D. C., Henry, H., Donaldson, A., Bilton, T. P., van Stijn, T. C., Kirk, M. R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24159502
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Additional file 14 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
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Additional file 1 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291905
Additional file 8 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694066
Additional file 16 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291899
Additional file 3 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291911
Additional file 4 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291914
Additional file 12 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291887
Additional file 6 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694060
Additional file 6 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291920
Additional file 17 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291902
Additional file 7 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291923
Additional file 1 of Opportunities and limits of combining microbiome and genome data for complex trait prediction
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Additional file 2 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
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Additional file 10 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
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Additional file 11 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291881
Additional file 1 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422374
Additional file 1 of Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves
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Additional file 5 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694057
Additional file 4 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694054
Additional file 5 of Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Xue, M.-Y., Sun, H.-Z., Wu, X.-H., Liu, J., & Guan, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.12291917
Additional file 7 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694063
Additional file 1 of Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions
Martínez-Álvaro, M., Mattock, J., Auffret, M. D., Weng, Z., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., & Roehe, R. (2022) · Figshare · doi:10.6084/m9.figshare.21285704
Additional file 5 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host
Hess, M. K., Hodgkinson, H. E., Hess, A. S., Zetouni, L., Budel, J. C. D. C., Henry, H., Donaldson, A., Bilton, T. P., van Stijn, T. C., Kirk, M. R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24159514
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Additional file 3 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694051
Additional file 2 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422422
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Authors: Roehe, R., Dewhurst, R. J., Duthie, C.-A., Rooke, J. A., McKain, N., Ross, D. W., Hyslop, J. J., Waterhouse, A., Freeman, T. C., Watson, M., & Wallace, R. J.
Affiliations: Scotland's Rural College (GB)
University of Aberdeen (GB)
Roslin Institute (GB)
Corresponding author: Roehe, Rainer
ORCID: Roehe, Rainer
Dewhurst, Richard J.
Duthie, Carol-Anne
Waterhouse, Anthony
Freeman, Tom C.
Watson, Mick
Wallace, R. John
Venue: PLoS Genetics
Publisher: Public Library of Science (PLoS)
Published: 2016-02-18
Volume: 12
Issue: 2
Pages: e1005846
ISSN: 1553-7390 (ISSN-L)
1553-7404
DOI: 10.1371/journal.pgen.1005846
PMC: PMC4758630
OpenAlex: W2275915069
Cited by: 359
References: 60
Citations by year: 2026: 29 · 2025: 27 · 2024: 33 · 2023: 33 · 2022: 30 · 2021: 39 · 2020: 51 · 2019: 33 · 2018: 42 · 2017: 32 · 2016: 10
FWCI: 81.88
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS Genetics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders: Scottish Government
Department for Environment, Food and Rural Affairs, UK Government
Directorate for Biological Sciences
Medical Research Council (MR/K001744/1)
Biotechnology and Biological Sciences Research Council (BBS/E/D/05191132, BB/J004235/1, BBS/E/D/20211553, BBS/E/D/20211552, BBS/E/D/20211550, BBS/E/D/20211551, BB/N016742/1, BB/ J004243/1, BB/N01720X/1, BB/I001107/1, BBS/E/D/20310000)
Natural Environment Research Council (NBAF010003)
Topics: Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Ruminant Nutrition and Digestive Physiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Anaerobic Digestion and Biogas Production
Keywords: host genetic variation, methanogenic archaea, feed efficiency, rumen metagenomics, microbial gene abundance, diet effects, mcrA, host-microbiome interactions, genetic selection, livestock greenhouse gas emissions
MeSH terms: Animals, Animal Feed, Animal Nutritional Physiological Phenomena, Archaea, Cattle, Female, Male, Methane, Rumen, Genetic Variation, Metagenomics, Microbiota

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Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

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