Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle
Open accessAbstract
The symbiotic rumen microbiota is essential for the digestion of plant fibers and contributes to the variation of production and health traits in ruminants. However, to date, the heritability of rumen microbial features and host genetic components associated with the rumen microbiota, as well as whether such genetic components are animal performance relevant, are largely unknown. In the present study, we assessed rumen microbiota from a cohort of 709 beef cattle and showed that multiple factors including breed, sex, and diet drove the variation of rumen microbiota among animals. The diversity indices, the relative abundance of ~ 34% of microbial taxa (59 out of 174), and the copy number of total bacteria had a heritability estimate ( h 2 ) ≥ 0.15, suggesting that they are heritable elements affected by host additive genetics. These moderately heritable rumen microbial features were also found to be associated with host feed efficiency traits and rumen metabolic measures (volatile fatty acids). Moreover, 19 single nucleotide polymorphisms (SNPs) located on 12 bovine chromosomes were found to be associated with 14 (12 of them had h 2 ≥ 0.15) rumen microbial taxa, and five of these SNPs were known quantitative trait loci for feed efficiency in cattle. These findings suggest that some rumen microbial features are heritable and could be influenced by host genetics, highlighting a potential to manipulate and obtain a desirable and efficient rumen microbiota using genetic selection and breeding. It could be a useful strategy to further improve feed efficiency and optimize rumen fermentation through targeting both cattle and their rumen microbiota.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Genetic manipulation of rumen microbiota is possible through different techniques as host genetics influences some heritable microbial traits.
“However, genetic manipulation of rumen microbiota is also possible through different techniques as host genetics influences some heritable microbial traits (81–83).”
From Phytogenic Additives Can Modulate Rumen Microbiome to Mediate Fermentation Kinetics and Methanogenesis Through Exploiting Diet–Microbe Interaction (2020) · cites this paper as [81]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 20,966 · see citing works
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,617 · see citing works
UCHIME improves sensitivity and speed of chimera detection
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr381 · Cited by 15,114 · see citing works
Diet rapidly and reproducibly alters the human gut microbiome
David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., et al. (2014) · Nature · doi:10.1038/nature12820 · Cited by 10,100 · see citing works
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050 · see citing works
McDonald, D. T., Price, M. N., Goodrich, J. K., Nawrocki, E. P., DeSantis, T. Z., Probst, A. J., Andersen, G. L., Knight, R. D., & Hugenholtz, P. (2011) · The ISME Journal · doi:10.1038/ismej.2011.139 · Cited by 5,010 · see citing works
Human Genetics Shape the Gut Microbiome
Goodrich, J. K., Waters, J. L., Poole, A. C., Sutter, J. L., Koren, O., Blekhman, R., Beaumont, M., Van Treuren, W., Knight, R., Bell, J. T., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.053 · Cited by 3,164 · see citing works
Inferring Correlation Networks from Genomic Survey Data
Friedman, J., & Alm, E. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002687 · Cited by 2,727 · see citing works
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094 · see citing works
Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Collaborators, G. R. C., Abecia, L., Angarita, E., Aravena, P., Nora Arenas, G., et al. (2015) · Scientific Reports · doi:10.1038/srep14567 · Cited by 1,682 · see citing works
Unravelling the effects of the environment and host genotype on the gut microbiome
Spor, A., Koren, O., & Ley, R. (2011) · Nature Reviews Microbiology · doi:10.1038/nrmicro2540 · Cited by 1,509 · see citing works
Benson, A. K., Kelly, S. A., Legge, R., Ma, F., Low, S. J., Kim, J., Zhang, M., Oh, P. L., Nehrenberg, D., Hua, K., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007028107 · Cited by 1,314 · see citing works
Genetic determinants of the gut microbiome in UK twins
Goodrich, J. K., Davenport, E. R., Beaumont, M., Jackson, M. A., Knight, R., Ober, C., Spector, T. D., Bell, J. T., Clark, A. G., & Ley, R. E. (2016) · Cell Host & Microbe · doi:10.1016/j.chom.2016.04.017 · Cited by 1,048 · see citing works
Exploring the bovine rumen bacterial community from birth to adulthood
Jami, E., Israel, A., Kotser, A., & Mizrahi, I. (2013) · The ISME Journal · doi:10.1038/ismej.2013.2 · Cited by 963 · see citing works
Factors That Alter Rumen Microbial Ecology
Russell, J. B., & Rychlik, J. L. (2001) · Science · doi:10.1126/science.1058830 · Cited by 954 · see citing works
Gender Bias in Autoimmunity Is Influenced by Microbiota
Yurkovetskiy, L., Burrows, M. P., Khan, A. A., Graham, L., Volchkov, P., Becker, L., Antonopoulos, D. A., Umesaki, Y., & Chervonsky, A. V. (2013) · Immunity · doi:10.1016/j.immuni.2013.08.013 · Cited by 912 · see citing works
Stevenson, D. M., & Weimer, P. J. (2007) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-006-0802-y · Cited by 833 · see citing works
Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.
Shabat, S. K. B., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller, M. E., White, B. A., Shterzer, N., & Mizrahi, I. (2016) · The ISME Journal · doi:10.1038/ismej.2016.62 · Cited by 823 · see citing works
Compact graphical representation of phylogenetic data and metadata with GraPhlAn
Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C., & Segata, N. (2015) · PeerJ · doi:10.7717/peerj.1029 · Cited by 806 · see citing works
Sex differences and hormonal effects on gut microbiota composition in mice
Org, E., Mehrabian, M., Parks, B. W., Shipkova, P., Liu, X., Drake, T. A., & Lusis, A. J. (2016) · Gut Microbes · doi:10.1080/19490976.2016.1203502 · Cited by 796 · see citing works
Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection
Hungate1000 project collaborators, Seshadri, R., Leahy, S. C., Attwood, G. T., Teh, K. H., Lambie, S. C., Cookson, A. L., Eloe-Fadrosh, E. A., Pavlopoulos, G. A., Hadjithomas, M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4110 · Cited by 624 · see citing works
Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency
Jami, E., White, B. A., & Mizrahi, I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085423 · Cited by 588 · see citing works
Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen
Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03317-6 · Cited by 559 · cited 3× · see citing works
“genomic characters of these heritable rumen taxa, such as the outcomes of the Hungate 1000 project [51] and the 913 microbial genomes assembled from rumen metagenomes [50], will provide more information to explain how host and rumen microorganisms coevolved at the genomic level and provide a better understanding of ho…”
BackgroundMethodRUMINANT NUTRITION SYMPOSIUM: Role of fermentation acid absorption in the regulation of ruminal pH12
Aschenbach, J. R., Penner, G. B., Stumpff, F., & Gäbel, G. (2010) · Journal of Animal Science · doi:10.2527/jas.2010-3301 · Cited by 500 · see citing works
Association of host genome with intestinal microbial composition in a large healthy cohort
GEM Project Research Consortium, Turpin, W., Espin-Garcia, O., Xu, W., Silverberg, M. S., Kevans, D., Smith, M. I., Guttman, D. S., Griffiths, A., Panaccione, R., et al. (2016) · Nature Genetics · doi:10.1038/ng.3693 · Cited by 460 · see citing works
Asnicar, F., Manara, S., Zolfo, M., Truong, D. T., Scholz, M., Armanini, F., Ferretti, P., Gorfer, V., Pedrotti, A., Tett, A., et al. (2017) · mSystems · doi:10.1128/msystems.00164-16 · Cited by 445 · see citing works
The rumen microbial metagenome associated with high methane production in cattle
Wallace, R. J., Rooke, J. A., McKain, N., Duthie, C.-A., Hyslop, J. J., Ross, D. W., Waterhouse, A., Watson, M., & Roehe, R. (2015) · BMC Genomics · doi:10.1186/s12864-015-2032-0 · Cited by 416 · see citing works
Rumen Microbiome from Steers Differing in Feed Efficiency
Myer, P. R., Smith, T. P. L., Wells, J. E., Kuehn, L. A., & Freetly, H. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129174 · Cited by 406 · see citing works
Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows
Difford, G. F., Plichta, D. R., Løvendahl, P., Lassen, J., Noel, S. J., Højberg, O., Wright, A. G., Zhu, Z., Kristensen, L., Nielsen, H. B., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007580 · Cited by 387 · see citing works
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., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005846 · Cited by 359 · see citing works
Ramayo‐Caldas, Y., Mach, N., Lepage, P., Levenez, F., Denis, C., Lemonnier, G., Leplat, J. J. J. J., Billon, Y., Berri, M., Dore, J. M., et al. (2016) · The ISME Journal · doi:10.1038/ismej.2016.77 · Cited by 359 · see citing works
Li, F., & Guan, L. L. (2017) · Applied and Environmental Microbiology · doi:10.1128/aem.00061-17 · Cited by 346 · see citing works
Zhou, M., Hernandez‐Sanabria, E., & Guan, L. L. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.02815-08 · Cited by 343 · see citing works
Li, F., Hitch, T. C. A., Chen, Y., Creevey, C. J., & Guan, L. L. (2019) · Microbiome · doi:10.1186/s40168-019-0618-5 · Cited by 326 · see citing works
Guan, L. L., Nkrumah, J. D., Basarab, J. A., & Moore, S. S. (2008) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2008.01343.x · Cited by 306 · see citing works
synbreed: a framework for the analysis of genomic prediction data using R
Wimmer, V., Albrecht, T., Auinger, H.-J., & Schön, C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts335 · Cited by 301 · see citing works
Jewell, K. A., McCormick, C. A., Odt, C. L., Weimer, P. J., & Suen, G. (2015) · Applied and Environmental Microbiology · doi:10.1128/aem.00720-15 · Cited by 281 · see citing works
Polymerase chain reaction primers miss half of rRNA microbial diversity
Hong, S. H., Bunge, J., Leslin, C. M., Jeon, S., & Epstein, S. S. (2009) · The ISME Journal · doi:10.1038/ismej.2009.89 · Cited by 278 · see citing works
Bile acids as metabolic regulators
Li, T., & Chiang, J. Y. L. (2015) · Current Opinion in Gastroenterology · doi:10.1097/mog.0000000000000156 · Cited by 270 · see citing works
Martinez-Pabon, F., Caicedo-Guerrero, J., Ibarra-Samboni, J. J., Ramírez-González, G., & Hernández Leo, D. (2022) · The Scientific World JOURNAL · doi:10.1155/8086 · Cited by 259 · see citing works
Genome-Wide Association Studies of the Human Gut Microbiota
Davenport, E., Cusanovich, D. A., Michelini, K., Barreiro, L. B., Ober, C., & Gilad, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140301 · Cited by 253 · see citing works
Nkrumah, J. D., Crews, D. H., Basarab, J. A., Price, M. A., Okine, E. K., Wang, Z., Li, C., & Moore, S. S. (2007) · Journal of Animal Science · doi:10.2527/jas.2006-657 · Cited by 235 · see citing works
Isolation of Succinivibrionaceae Implicated in Low Methane Emissions from Tammar Wallabies
Pope, P. B., Smith, W. J. M., Denman, S. E., Tringe, S. G., Barry, K., Hugenholtz, P., McSweeney, C. S., McHardy, A. C., & Morrison, M. (2011) · Science · doi:10.1126/science.1205760 · Cited by 225 · see citing works
Sasson, G., Kruger Ben-Shabat, S., Seroussi, E., Doron-Faigenboim, A., Shterzer, N., Yaacoby, S., Berg Miller, M. E., White, B. A., Halperin, E., & Mizrahi, I. (2017) · mBio · doi:10.1128/mbio.00703-17 · Cited by 219 · see citing works
Huws, S., Kim, E. J., Lee, M. R. F., Scott, M. B., Tweed, J. K. S., Pinloche, E., Wallace, R. J., & Scollan, N. D. (2011) · Environmental Microbiology · doi:10.1111/j.1462-2920.2011.02452.x · Cited by 205 · see citing works
Paz, H. A., Anderson, C. L., Muller, M. J., Kononoff, P. J., & Fernando, S. C. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.01206 · Cited by 198 · see citing works
Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and Epithelium
McCann, J. C., Luan, S., Cardoso, F. C., Derakhshani, H., Khafipour, E., & Loor, J. J. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00701 · Cited by 181 · see citing works
Ruminal Acidosis in Feedlot: From Aetiology to Prevention
Bermúdez, J. H., Benedito, J. L., Abuelo, Á., & Castillo, C. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/702572 · Cited by 174 · see citing works
Seedorf, H., Kittelmann, S., Henderson, G., & Janssen, P. H. (2014) · PeerJ · doi:10.7717/peerj.494 · Cited by 168 · see citing works
Leamy, L. J., Kelly, S. A., Nietfeldt, J. W., Legge, R. M., Ma, F., Hua, K., Sinha, R., Peterson, D. A., Walter, J., Benson, A. K., et al. (2014) · Genome biology · doi:10.1186/s13059-014-0552-6 · Cited by 159 · see citing works
Huber, J. A., Morrison, H. G., Huse, S. M., Neal, P. R., Sogin, M. L., & Welch, D. B. M. (2009) · Environmental Microbiology · doi:10.1111/j.1462-2920.2008.01857.x · Cited by 151 · see citing works
Basarab, J. A., Colazo, M. G., Ambrose, D. J., Novak, S., McCartney, D. H., & Baron, V. S. (2011) · Canadian Journal of Animal Science · doi:10.4141/cjas2011-010 · Cited by 146 · see citing works
Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake
Kong, R. S. G., Liang, G., Chen, Y., Stothard, P., & Guan, L. L. (2016) · BMC Genomics · doi:10.1186/s12864-016-2935-4 · Cited by 127 · see citing works
Genome‐wide association analysis for feed efficiency in Angus cattle
Rolf, M. M., Taylor, J. F., Schnabel, R. D., McKay, S., McClure, M. C., Northcutt, S. L., Kerley, M. S., & Weaber, R. L. (2011) · Animal Genetics · doi:10.1111/j.1365-2052.2011.02273.x · Cited by 120 · see citing works
Nicolazzi, E. L., Caprera, A., Nazzicari, N., Cozzi, P., Strozzi, F., Lawley, C., Pirani, A., Soans, C., Brew, F., Jorjani, H., et al. (2015) · BMC Genomics · doi:10.1186/s12864-015-1497-1 · Cited by 119 · see citing works
Hernandez-Sanabria, E., Guan, L. L., Goonewardene, L. A., Li, M., Mujibi, D. F., Stothard, P., Moore, S. S., & Leon-Quintero, M. C. (2010) · Applied and Environmental Microbiology · doi:10.1128/aem.01052-10 · Cited by 119 · see citing works
Firkins, J. L., & Yu, Z. (2015) · Journal of Animal Science · doi:10.2527/jas.2014-8754 · Cited by 110 · see citing works
Identification of genomic regions associated with feed efficiency in Nelore cattle
Oliveira, P. S., César, A. S. M., do Nascimento, M. L., Chaves, A. S., Tizioto, P. C., Túllio, R. R., Lanna, D. P., Rosa, A. N., Sonstegard, T. S., Mourão, G. B., et al. (2014) · BMC Genetics · doi:10.1186/s12863-014-0100-0 · Cited by 96 · see citing works
Reau, A. J. L., Meier‐Kolthoff, J. P., & Suen, G. (2016) · Microbial Genomics · doi:10.1099/mgen.0.000099 · Cited by 96 · see citing works
Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle1
Sherman, E. L., Nkrumah, J. D., Li, C., Bartusiak, R., Murdoch, B. M., & Moore, S. S. (2008) · Journal of Animal Science · doi:10.2527/jas.2008-0876 · Cited by 92 · see citing works
Taxonomic Assessment of Rumen Microbiota Using Total RNA and Targeted Amplicon Sequencing Approaches
Li, F., Henderson, G., Sun, X., Cox, F., Janssen, P. H., & Guan, L. L. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00987 · Cited by 87 · see citing works
Hernandez‐Sanabria, E., Goonewardene, L. A., Wang, Z., Zhou, M., Moore, S. S., & Guan, L. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058461 · Cited by 78 · see citing works
Klieve, A. V., O’Leary, M. N., McMillen, L., & Ouwerkerk, D. (2007) · Journal of Applied Microbiology · doi:10.1111/j.1365-2672.2007.03492.x · Cited by 67 · see citing works
Li, C., Basarab, J. A., Snelling, W. M., Benkel, B., Murdoch, B. M., & Moore, S. S. (2002) · Journal of Animal Science · doi:10.2527/2002.8051187x · Cited by 55 · see citing works
Racca, A. W., Beck, A. E., McMillin, M., Korte, F. S., Bamshad, M. J., & Regnier, M. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv084 · Cited by 54 · see citing works
Kim, M., & Yu, Z. (2014) · The Journal of Microbiology · doi:10.1007/s12275-014-3443-3 · Cited by 44 · see citing works
Li, F., Neves, A. L. A., Ghoshal, B., & Guan, L. L. (2017) · Journal of Dairy Science · doi:10.3168/jds.2017-13356 · Cited by 36 · see citing works
Xiang, R., McNally, J., Rowe, S. J., Jonker, A., Pinares-Patiño, C. S., Oddy, V. H., Vercoe, P. E., McEwan, J. C., & Dalrymple, B. P. (2016) · Scientific Reports · doi:10.1038/srep39022 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Targeting circular RNAs as a therapeutic approach: current strategies and challenges
He, A. T., Liu, J., Li, F., & Yang, B. B. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00569-5 · Cited by 556 · see citing works
Xue, M., Sun, H., Wu, X., Liu, J., & Guan, L. L. (2020) · Microbiome · doi:10.1186/s40168-020-00819-8 · Cited by 516 · see citing works
The rumen microbiome: balancing food security and environmental impacts
Mizrahi, I., Wallace, R. J., & Moraïs, S. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00543-6 · Cited by 490 · see citing works
Disease-induced changes in plant microbiome assembly and functional adaptation
Gao, M., Xiong, C., Gao, C. D., Tsui, C. K. M., Wang, M., Zhou, X. M., Zhang, A., & Cai, L. (2021) · Microbiome · doi:10.1186/s40168-021-01138-2 · Cited by 486 · see citing works
The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger?
O’Hara, E., Neves, A. L. A., Song, Y., & Guan, L. L. (2020) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-021419-083952 · Cited by 251 · see citing works
Ruminal microbiota–host interaction and its effect on nutrient metabolism
Liu, K., Zhang, Y., Yu, Z., Xu, Q., Zheng, N., Zhao, S., Huang, G., & Wang, J. (2020) · Animal nutrition · doi:10.1016/j.aninu.2020.12.001 · Cited by 220 · see citing works
Bargaz, A., Elhaissoufi, W., Khourchi, S., Benmrid, B., Borden, K. A., & Rchiad, Z. (2021) · Microbiological Research · doi:10.1016/j.micres.2021.126842 · Cited by 218 · see citing works
Kim, H. S., Whon, T. W., Sung, H., Jeong, Y., Jung, E. S., Shin, N., Hyun, D., Kim, P. S., Lee, J., Lee, C. H., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20389-5 · Cited by 200 · see citing works
Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens
Wen, C., Yan, W., Mai, C., Duan, Z., Zheng, J., Sun, C., & Yang, N. (2021) · Microbiome · doi:10.1186/s40168-021-01040-x · Cited by 203 · see citing works
Hitch, T. C. A., Riedel, T., Oren, A., Overmann, J., Lawley, T. D., & Clavel, T. (2021) · ISME Communications · doi:10.1038/s43705-021-00017-z · Cited by 193 · see citing works
Xue, M., Xie, Y., Zhong, Y., Ma, X., Sun, H., & Liu, J. (2022) · Microbiome · doi:10.1186/s40168-022-01228-9 · Cited by 192 · see citing works
Hassan, F.-U., Arshad, M. A., Ebeid, H. M., Rehman, M. S.-U., Khan, M. S., Shahid, S., & Yang, C. (2020) · Frontiers in Veterinary Science · doi:10.3389/fvets.2020.575801 · Cited by 160 · see citing works
Gut Microbiota and Their Role in Health and Metabolic Disease of Dairy Cow
Xu, Q., Qiao, Q., Gao, Y., Hou, J., Hu, M., Du, Y., Zhao, K., & Li, X. O. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.701511 · Cited by 147 · see citing works
P2X7 receptor mediates NLRP3 inflammasome activation in depression and diabetes
Wang, D., Wang, H., Gao, H., Zhang, H., Zhang, H., Wang, Q., & Sun, Z. (2020) · Cell & Bioscience · doi:10.1186/s13578-020-00388-1 · Cited by 145 · see citing works
Strategies to improve the efficiency of beef cattle production
Terry, S. A., Basarab, J. A., Guan, L. L., & McAllister, T. A. (2021) · Canadian Journal of Animal Science · doi:10.1139/cjas-2020-0022 · Cited by 142 · see citing works
Ahmad, A. A., Yang, C., Zhang, J., Kalwar, Q., Liang, Z., Li, C., Du, M., Yan, P., Long, R., Han, J., et al. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00625 · Cited by 140 · see citing works
Chen, W., & Wen, D. (2021) · Environmental Microbiome · doi:10.1186/s40793-021-00377-y · Cited by 134 · see citing works
Ban, Y., & Guan, L. L. (2021) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-021-00630-x · Cited by 134 · see citing works
Understanding the role of rumen epithelial host-microbe interactions in cattle feed efficiency
Na, S. W., & Guan, L. L. (2022) · Animal nutrition · doi:10.1016/j.aninu.2022.04.002 · Cited by 122 · see citing works
Hoque, M. N., Istiaq, A., Clement, R. A., Gibson, K. M., Saha, O., Islam, O. K., Abir, R. A., Sultana, M., Siddiki, A. Z., Crandall, K. A., et al. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00860 · Cited by 121 · see citing works
Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics
Cao, S., Zhang, W., Ding, W., Wang, M., Fan, S., Yang, B. K., McMinn, A., Wang, M., Xie, B.-B., Qin, Q., et al. (2020) · Microbiome · doi:10.1186/s40168-020-00826-9 · Cited by 120 · see citing works
Zhou, L., Wang, P., Huang, S., Li, Z., Gong, H., Huang, W., Zhao, Z., & Yu, Z. (2021) · Environmental Research · doi:10.1016/j.envres.2021.110934 · Cited by 115 · see citing works
Fan, P., Nelson, C., Driver, J., Elzo, M., Peñagaricano, F., & Jeong, K. (2021) · The ISME Journal · doi:10.1038/s41396-021-00925-x · Cited by 83 · see citing works
Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows
Ramayo‐Caldas, Y., Zingaretti, L. M., Popova, M., Estellé, J., Bernard, A., Pons, N., Bellot, P., Mach, N., Rau, A., Roume, H., et al. (2019) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12427 · Cited by 111 · see citing works
Bovine host genome acts on rumen microbiome function linked to methane emissions
Martínez-Álvaro, M., Auffret, M. D., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., & Roehe, R. (2022) · Communications Biology · doi:10.1038/s42003-022-03293-0 · Cited by 113 · see citing works
Interrogating the viral dark matter of the rumen ecosystem with a global virome database
Yan, M., Pratama, A. A., Somasundaram, S., Li, Z., Jiang, Y., Sullivan, M. B., & Yu, Z. (2023) · Nature Communications · doi:10.1038/s41467-023-41075-2 · Cited by 106 · see citing works
Zhang, Q., Difford, G. F., Sahana, G., Løvendahl, P., Lassen, J., Lund, M. S., Guldbrandtsen, B., & Janss, L. (2020) · The ISME Journal · doi:10.1038/s41396-020-0663-x · Cited by 102 · see citing works
Dynamic progression of the calf’s microbiome and its influence on host health
Amin, N., & Seifert, J. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.035 · Cited by 102 · see citing works
Huang, Y., Zheng, X., Weng, W., Yan, X., Chen, P., Liu, X., Peng, T., Zhong, Q., Xu, K., Wang, C., et al. (2021) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.125094 · Cited by 96 · see citing works
Huang, Y., Liu, X., Yang, C., Peng, Y., Yu, X., Gu, H., Zheng, X., Wang, C., Xiao, F., Shu, L., et al. (2021) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2021.108382 · Cited by 92 · see citing works
Influence of host genetics in shaping the rumen bacterial community in beef cattle
Abbas, W., Howard, J., Paz, H. A., Hales, K. E., Wells, J. E., Kuehn, L. A., Erickson, G. E., Spangler, M. L., & Fernando, S. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-72011-9 · Cited by 89 · see citing works
Intestinal Microbiota Regulate Certain Meat Quality Parameters in Chicken
Lei, J., Dong, Y., Hou, Q., He, Y., Lai, Y., Liao, C., Kawamura, Y., Li, J., & Zhang, B. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.747705 · Cited by 88 · see citing works
Multi-Omics Approaches in Immunological Research
Chu, X., Zhang, B., Koeken, V. A. C. M., Gupta, M. K., & Li, Y. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.668045 · Cited by 86 · see citing works
Bronzo, V., Lopreiato, V., Riva, F., Amadori, M., Curone, G., Addis, M. F., Cremonesi, P., Moroni, P., Trevisi, E., & Castiglioni, B. (2020) · Animals · doi:10.3390/ani10081397 · Cited by 83 · see citing works
The Signature Microbiota Drive Rumen Function Shifts in Goat Kids Introduced to Solid Diet Regimes
Lv, X., Chai, J., Diao, Q., Huang, W., Zhuang, Y., & Zhang, N. (2019) · Microorganisms · doi:10.3390/microorganisms7110516 · Cited by 74 · see citing works
Pitta, D., Indugu, N., Narayan, K. S., & Hennessy, M. L. (2022) · Journal of Dairy Science · doi:10.3168/jds.2021-21466 · Cited by 76 · see citing works
Zhao, C., Wang, L., Ke, S., Chen, X., Kenéz, Á., Xu, W., Wang, D., Zhang, F., Li, Y., Cui, Z., et al. (2022) · Animal nutrition · doi:10.1016/j.aninu.2022.07.014 · Cited by 72 · see citing works
Li, Y., & Hu, C. (2021) · npj Biofilms and Microbiomes · doi:10.1038/s41522-021-00188-6 · Cited by 69 · see citing works
Dietary strategies with anti-aging potential: Dietary patterns and supplements
Duan, H., Pan, J., Guo, M., Li, J., Yu, L., & Fan, L. (2022) · Food Research International · doi:10.1016/j.foodres.2022.111501 · Cited by 69 · see citing works
Wang, W., Zhang, Y., Zhang, X., Li, C., Yuan, L., Zhang, D., Zhao, Y., Li, X., Cheng, J., Lin, C., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01642-7 · Cited by 64 · see citing works
Aguilar-Marín, S. B., Betancur-Murillo, C. L., Isaza, G. A., Mesa, H., & Jovel, J. (2020) · BMC Microbiology · doi:10.1186/s12866-020-02037-6 · Cited by 63 · see citing works
Ding, Y., Pan, B., Zhao, G., Sun, C., Han, X., & Li, M. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.148346 · Cited by 63 · see citing works
Inter‐breed diversity and temporal dynamics of the faecal microbiota in healthy horses
Massacci, F. R., Clark, A. P., Ruet, A., Lansade, L., Costa, M., & Mach, N. (2019) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12441 · Cited by 62 · see citing works
Sun, H., Pan, B., He, H., Zhao, G., Hou, Y., & Zhu, P. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107643 · Cited by 62 · see citing works
In Pursuit of Understanding the Rumen Microbiome
Sanjorjo, R. A., Tseten, T., Kang, M., Kwon, M., & Kim, S.-W. (2023) · Fermentation · doi:10.3390/fermentation9020114 · Cited by 62 · see citing works
Saborío‐Montero, A., Gutiérrez‐Rivas, M., García-Rodríguez, A., Atxaerandio, R., Goiri, I., de Maturana, E. L., Jiménez-Montero, J. A., Alenda, R., & González-Recio, Ó. (2019) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12444 · Cited by 61 · see citing works
Aliakbari, A., Zemb, O., Billon, Y., Barilly, C., Ahn, I., Riquet, J., & Gilbert, H. (2021) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12539 · Cited by 57 · see citing works
Yan, Z., Chen, Q., Li, C., Nguyen, B. T., Zhu, Y., He, J., & Hu, H. (2021) · ISME Communications · doi:10.1038/s43705-021-00012-4 · Cited by 57 · see citing works
Concepts and Consequences of a Core Gut Microbiota for Animal Growth and Development
Perlman, D., Martínez-Álvaro, M., Moraïs, S., Altshuler, I., Hagen, L. H., Jami, E., Roehe, R., Pope, P. B., & Mizrahi, I. (2021) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-013020-020412 · Cited by 56 · see citing works
Qiu, X., Qin, X., Chen, L., Chen, Z., Hao, R., Zhang, S., Yang, S., Wang, L., Cui, Y., Li, Y., et al. (2022) · Microorganisms · doi:10.3390/microorganisms10020323 · Cited by 56 · see citing works
Smith, P. E., Kelly, A. K., Kenny, D. A., & Waters, S. M. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.855565 · Cited by 56 · see citing works
Animal board invited review: The effect of diet on rumen microbial composition in dairy cows
Palmonari, A., Federiconi, A., & Formigoni, A. (2024) · animal · doi:10.1016/j.animal.2024.101319 · Cited by 56 · see citing works
Research progress and applications of genome‐wide association study in farm animals
Tan, X., He, Z., Fahey, A. G., Zhao, G., Liu, R., & Wen, J. (2023) · Animal Research and One Health · doi:10.1002/aro2.14 · Cited by 55 · see citing works
Brito, L. F., Oliveira, H. R., Houlahan, K., Fonseca, P. A. S., Lam, S., Butty, A. M., Seymour, D. J., Vargas, G., Chud, T. C. S., Silva, F. F., et al. (2020) · Canadian Journal of Animal Science · doi:10.1139/cjas-2019-0193 · Cited by 53 · see citing works
Daghio, M., Ciucci, F., Buccioni, A., Cappucci, A., Casarosa, L., Serra, A., Conte, G., Viti, C., McAmmond, B. M., Hamme, J. D. V., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.652031 · Cited by 53 · see citing works
Amat, S., Holman, D. B., Schmidt, K. N., Menezes, A. C. B., Baumgaertner, F., Winders, T., Kirsch, J. D., Liu, T., Schwinghamer, T. D., Sedivec, K. K. K., et al. (2021) · Microorganisms · doi:10.3390/microorganisms9102011 · Cited by 51 · see citing works
Host Genetic Determinants of the Microbiome Across Animals: From Caenorhabditis elegans to Cattle
Ryu, E. P., & Davenport, E. (2022) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-020420-032054 · Cited by 47 · see citing works
Plastic film mulching reduces microbial interactions in black soil of northeastern China
Luo, S., Wang, S., Zhang, H., Zhang, J., & Tian, C. (2021) · Applied Soil Ecology · doi:10.1016/j.apsoil.2021.104187 · Cited by 48 · see citing works
Opportunities and limits of combining microbiome and genome data for complex trait prediction
Miguel Pérez-enciso, A., Zingaretti, L. M., Ramayo‐Caldas, Y., & Campos, G. D. L. (2021) · Genetics Selection Evolution · doi:10.1186/s12711-021-00658-7 · Cited by 49 · see citing works
Methane and Carbon Dioxide Emission of Beef Heifers in Relation with Growth and Feed Efficiency
Renand, G., Vinet, A., Decruyenaere, V., Maupetit, D., & Dozias, D. (2019) · Animals · doi:10.3390/ani9121136 · Cited by 46 · see citing works
Predictive ability of host genetics and rumen microbiome for subclinical ketosis
Gebreyesus, G., Difford, G. F., Buitenhuis, B., Lassen, J., Noel, S. J., Højberg, O., Plichta, D. R., Zhu, Z., Poulsen, N. A., Sundekilde, U. K., et al. (2020) · Journal of Dairy Science · doi:10.3168/jds.2019-17824 · Cited by 46 · see citing works
Ji, L., Nasir, F., Tian, L., Chang, J., Sun, Y., Zhang, J., Li, X., & Tian, C. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.676880 · Cited by 46 · see citing works
Hu, C., Ding, L., Jiang, C., Ma, C., Liu, B., Li, D., & Degen, A. A. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.755255 · Cited by 46 · see citing works
Bai, X., & Plastow, G. S. (2022) · CABI Agriculture and Bioscience · doi:10.1186/s43170-022-00073-y · Cited by 46 · see citing works
Waters, S. M., Roskam, E., Smith, P. E., Kenny, D. A., Popova, M., Eugène, M., & Morgavi, D. P. (2025) · Journal of Dairy Science · doi:10.3168/jds.2024-25778 · Cited by 45 · see citing works
Huang, S., Ji, S., Suen, G., Wang, F., & Li, S. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.630605 · Cited by 45 · see citing works
Meta-analysis of the effects of essential oil as an alternative to monensin in diets for beef cattle
Torres, R. D. N. S., Paschoaloto, J. R., Ezequiel, J. M. B., da Silva, D. A. V., & Almeida, M. T. C. (2021) · The Veterinary Journal · doi:10.1016/j.tvjl.2021.105659 · Cited by 45 · see citing works
A review of the resistome within the digestive tract of livestock
Ma, T., McAllister, T., & Guan, L. (2021) · Journal of Animal Science and Biotechnology · doi:10.1186/s40104-021-00643-6 · Cited by 36 · see citing works
Kaplan-Shabtai, V., Indugu, N., Hennessy, M. L., Vecchiarelli, B., Bender, J. S., Stefanovski, D., Lage, C. F. D. A., Räisänen, S. E., Audino Melgar, M., Nedelkov, K., et al. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.611951 · Cited by 43 · see citing works
Yan, M., & Yu, Z. (2024) · Microbiome · doi:10.1186/s40168-024-01791-3 · Cited by 43 · see citing works
Zamorano-López, N., Borrás, L., Giménez, J. B., Seco, A., & Aguado, D. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121787 · Cited by 43 · see citing works
Islam, M., Kim, S., Ramos, S., Mamuad, L. L., Son, A.-R., Yu, Z., Lee, S.-S., Cho, Y.-I., & Lee, S.-S. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.601061 · Cited by 43 · see citing works
Behera, B., Dehury, B., Rout, A. K., Patra, B., Mantri, N., Chakraborty, H. J., Sarkar, D., Kaushik, N., Bansal, V., Singh, I., et al. (2021) · Gene Reports · doi:10.1016/j.genrep.2021.101372 · Cited by 34 · see citing works
Age-dependent variations in rumen bacterial community of Mongolian cattle from weaning to adulthood
Ahmad, A. A., Zhang, J., Liang, Z., Du, M., Yang, Y., Zheng, J., Yan, P., Long, R., Tong, B., Han, J., et al. (2022) · BMC Microbiology · doi:10.1186/s12866-022-02627-6 · Cited by 42 · see citing works
Martínez-Álvaro, M., Mattock, J., Auffret, M. D., Weng, Z., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., & Roehe, R. (2022) · Microbiome · doi:10.1186/s40168-022-01352-6 · Cited by 41 · see citing works
Genetic and microbiome analysis of feed efficiency in laying hens
Zhou, Q., Lan, F., Gu, S., Li, G., Wu, G., Yan, Y., Li, X., Jin, J., Wen, C., Sun, C., et al. (2022) · Poultry Science · doi:10.1016/j.psj.2022.102393 · Cited by 42 · see citing works
Kaur, H., Kaur, G., Gupta, T., Mittal, D., & Ali, S. A. (2023) · Biology · doi:10.3390/biology12091200 · Cited by 42 · see citing works
Wang, B., & Luo, H. (2021) · BMC Microbiology · doi:10.1186/s12866-021-02311-1 · Cited by 42 · see citing works
Ge, T., Yang, C., Li, B., Huang, X., Zhao, L., Zhang, X., Tian, L., & Zhang, E. (2023) · BMC Veterinary Research · doi:10.1186/s12917-023-03592-6 · Cited by 46 · see citing works
Rumen metaproteomics: Closer to linking rumen microbial function to animal productivity traits
Andersen, T. O., Kunath, B. J., Hagen, L. H., Arntzen, M. Ø., & Pope, P. B. (2020) · Methods · doi:10.1016/j.ymeth.2020.07.011 · Cited by 40 · see citing works
Si, J., Feng, L., Gao, J., Huang, Y., Zhang, G., Mo, J., Zhu, S., Qi, W., Liang, J. Z., & Lan, G. (2020) · AMB Express · doi:10.1186/s13568-020-01050-2 · Cited by 40 · see citing works
Enteric methane research and mitigation strategies for pastoral-based beef cattle production systems
Smith, P. E., Kelly, A. K., Kenny, D. A., & Waters, S. M. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.958340 · Cited by 41 · see citing works
Jia, P., Dong, L., Tu, Y., & Diao, Q. (2023) · Microbiome · doi:10.1186/s40168-023-01654-3 · Cited by 40 · see citing works
Wadood, A. A., Bordbar, F., & Zhang, X. (2025) · Frontiers in Animal Science · doi:10.3389/fanim.2025.1551244 · Cited by 43 · see citing works
Zhang, C., Liu, H., Jiang, X., Zhang, Z., Hou, X., Wang, Y., Wang, D., Li, Z., Cao, Y., Wu, S., et al. (2024) · Microbiome · doi:10.1186/s40168-024-01937-3 · Cited by 38 · see citing works
Zhang, Y., Li, F., Chen, Y., Wu, H., Meng, Q., & Guan, L. L. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00367 · Cited by 38 · see citing works
Animal development in the microbial world: Re-thinking the conceptual framework
Bosch, T. C. G., & McFall‐Ngai, M. (2021) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2020.11.007 · Cited by 34 · see citing works
Dai, Q., Ma, J., Cao, G., Hu, R., Zhu, Y., Li, G., Zou, H., Wang, Z., Peng, Q., Bai, X., et al. (2021) · AMB Express · doi:10.1186/s13568-021-01259-9 · Cited by 38 · see citing works
Dong, H., Fan, S., Sun, H., Chen, C.-L., Wang, A., Jiang, L., & Ma, D. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.730506 · Cited by 38 · see citing works
Gut microbiota, host genetics and phenotypes in aquatic animals: A review
Gao, Q., Liu, P., Li, Y., Song, D., Long, W., Wang, Z., Yi, S., & Jiang, L. (2023) · Aquaculture Reports · doi:10.1016/j.aqrep.2023.101648 · Cited by 39 · see citing works
Kumar, A., Manisha, D., Sujitha, K., Peter, D. M., Kirubagaran, R., & Dharani, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-88525-9 · Cited by 38 · see citing works
Wang, J., Xie, J., Li, L., Luo, Z., Zhang, R., Wang, L., & Jiang, Y. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.664120 · Cited by 37 · see citing works
Perspectives for sustainable agriculture from the microbiome in plant rhizosphere
Nwachukwu, B. C., & Babalola, O. O. (2021) · Plant Biotechnology Reports · doi:10.1007/s11816-021-00676-3 · Cited by 36 · see citing works
Kong, F., Wang, S., Dai, D., Cao, Z., Wang, Y., Li, S., & Wang, W. (2022) · Antioxidants · doi:10.3390/antiox11091715 · Cited by 35 · see citing works
Microbial Risks Caused by Livestock Excrement: Current Research Status and Prospects
Abdugheni, R., Li, L., Yang, Z.-N., Huang, Y., Fang, B., Shurigin, V., Mohamad, O. A. A., Liu, Y., & Li, W. (2023) · Microorganisms · doi:10.3390/microorganisms11081897 · Cited by 35 · see citing works
Zhao, Y., Lin, S., Liu, Y., Li, G., Wang, J., & Butterbach‐Bahl, K. (2020) · Soil Use and Management · doi:10.1111/sum.12579 · Cited by 36 · see citing works
Prabhu, V. R., Wasimuddin, Kamalakkannan, R., Arjun, M. S., & Nagarajan, M. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00133 · Cited by 37 · see citing works
Badhan, A., Wang, Y., Terry, S. A., Gruninger, R. J., Guan, L. L., & McAllister, T. A. (2025) · Journal of Dairy Science · doi:10.3168/jds.2024-26063 · Cited by 45 · see citing works
Johne's Disease in Dairy Cattle: An Immunogenetic Perspective
Mallikarjunappa, S., Brito, L. F., Pant, S. D., Schenkel, F. S., Meade, K. G., & Karrow, N. A. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.718987 · Cited by 34 · see citing works
Young, J., Skarlupka, J. H., Cox, M. S., Resende, R. T., Fischer, A., Kalscheur, K. F., McClure, J. C., Cole, J. B., Suen, G., & Bickhart, D. M. (2020) · Applied and Environmental Microbiology · doi:10.1128/aem.00861-20 · Cited by 33 · see citing works
- Authors
- Li, F., Li, C., Chen, Y., Liu, J., Zhang, C., Irving, B., Fitzsimmons, C., Plastow, G., & Guan, L. L.
- Affiliations
- University of Alberta (CA)
- Agriculture and Agri-Food Canada (CA)
- Corresponding author
- Guan, Le Luo
- Venue
- Microbiome
- Published
- 2019-06-13
- Volume
- 7
- Issue
- 1
- Pages
- 92-92
- ISSN
- 2049-2618 (ISSN-L)
- PMC
- PMC6567441
- OpenAlex
- W2953135971
- Cited by
- 530
- References
- 71
- Citations by year
- 2026: 44 · 2025: 77 · 2024: 94 · 2023: 65 · 2022: 62 · 2021: 120 · 2020: 58 · 2019: 10
- FWCI
- 93.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Microbiome (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Alberta Livestock and Meat Agency (2013R029R)
- Natural Sciences and Engineering Research Council of Canada (Discovery)
- Alberta Innovates - Technology Futures (Graduate student scholarship)
- Alberta Agriculture and Forestry (2018F095R)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Ruminant Nutrition and Digestive Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- rumen microbiota, host genetics, heritability, feed efficiency, beef cattle, single nucleotide polymorphisms, quantitative trait loci, volatile fatty acids, rumen fermentation, genetic selection
- MeSH terms
- Host Microbial Interactions, Animals, Animal Feed, Animal Nutritional Physiological Phenomena, Archaea, Bacteria, Breeding, Cattle, Female, Fermentation, Genotype, Male, Phylogeny, Rumen, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review