ArticleThe ISME journal2020
Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.
Article in The ISME journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning.Journal of animal science · 2024Pooled it
- Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome.Communications biology · 2026Article
- A guide to network analysis, multi-omics integration, and applications in livestock microbiome research.World journal of microbiology & biotechnology · 2025Review
- Mechanisms and Applications of Gastrointestinal Microbiota-Metabolite Interactions in Ruminants: A Review.Microorganisms · 2025Review
- The influence of vaginal microbiota on ewe fertility: a metagenomic and functional genomic approach.Microbiome · 2025Article
- The crosstalk between host and rumen microbiome in cattle: insights from multi-omics approaches and genome-wide association studies.World journal of microbiology & biotechnology · 2025Review
- Improving genomic prediction accuracy for methane emission and feed efficiency in sheep: integrating rumen microbial PCA with host genomic variation using neural network GBLUP (NN-GBLUP).Genetics, selection, evolution : GSE · 2025Article
- Sequence-based GWAS reveals genes and variants associated with predicted methane emissions in French dairy cows.Genetics, selection, evolution : GSE · 2025Article
- Rumen metagenome profiles are heritable and rank the New Zealand national sheep flock for enteric methane emissions.Genetics, selection, evolution : GSE · 2025Article
- Host genome drives the microbiota enrichment of beneficial microbes in shrimp: exploring the hologenome perspective.Animal microbiome · 2025Article
- Network analyses unraveled the complex interactions in the rumen microbiota associated with methane emission in dairy cattle.Animal microbiome · 2025Article
- Ruminant nutrition symposium: novel microbial solutions to optimize production efficiency in beef and dairy systems.Journal of animal science · 2025Review
- A metagenomic catalogue of the ruminant gut archaeome.Nature communications · 2024Article
- Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions.Animal biotechnology · 2024Review
- Revealing host genome-microbiome networks underlying feed efficiency in dairy cows.Scientific reports · 2024Article
- Article
- Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle.Scientific reports · 2024Article
- Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle.Genetics, selection, evolution : GSE · 2024Article
- Multi-omics analyses reveal rumen microbes and secondary metabolites that are unique to livestock species.mSystems · 2024Article
- Alterations of rumen and fecal microbiome in growing beef and dairy steers fed rumen-protected Capsicum oleoresin.Journal of animal science · 2024Article
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reducing methane emissions from livestock production is of great importance for the sustainable management of the Earth's environment. Rumen microbiota play an important role in producing biogenic methane. However, knowledge of how host genetics influences variation in ruminal microbiota and their joint effects on methane emission is limited. We analyzed data from 750 dairy cows, using a Bayesian model to simultaneously assess the impact of host genetics and microbiota on host methane emission. We estimated that host genetics and microbiota explained 24% and 7%, respectively, of variation in host methane levels. In this Bayesian model, one bacterial genus explained up to 1.6% of the total microbiota variance. Further analysis was performed by a mixed linear model to estimate variance explained by host genomics in abundances of microbial genera and operational taxonomic units (OTU). Highest estimates were observed for a bacterial OTU with 33%, for an archaeal OTU with 26%, and for a microbial genus with 41% heritability. However, after multiple testing correction for the number of genera and OTUs modeled, none of the effects remained significant. We also used a mixed linear model to test effects of individual host genetic markers on microbial genera and OTUs. In this analysis, genetic markers inside host genes ABS4 and DNAJC10 were found associated with microbiota composition. We show that a Bayesian model can be utilized to model complex structure and relationship between microbiota simultaneously and their interaction with host genetics on methane emission. The host genome explains a significant fraction of between-individual variation in microbial abundance. Individual microbial taxonomic groups each only explain a small amount of variation in methane emissions. The identification of genes and genetic markers suggests that it is possible to design strategies for breeding cows with desired microbiota composition associated with phenotypes.
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