Evidence map›Paper›PMID 39472728›Full record

ArticleScientific reports2024

Revealing host genome-microbiome networks underlying feed efficiency in dairy cows.

Guillermo Martinez-Boggio, Hugo F Monteiro, Fabio S Lima, Caio C Figueiredo, Rafael S Bisinotto, José E P Santos, Bruna Mion, Flavio S Schenkel, Eduardo S Ribeiro, Kent A Weigel and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Quantitative genetics of microbiome-mediated traits.Evolution; international journal of organic evolution · 2025
    Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Guillermo Martinez-BoggioDepartment of Animal and Dairy Sciences, University of Wisconsin, 1675 Observatory Dr, Madison, WI, 53706, USA. guillermo.martinezboggio@wisc.edu.
Hugo F MonteiroDepartment of Population Health and Reproduction, University of California, Davis, 95616, USA.
Fabio S LimaDepartment of Population Health and Reproduction, University of California, Davis, 95616, USA.
Caio C FigueiredoDepartment of Veterinary Clinical Sciences, Washington State University, Pullman, 99163, USA.
Rafael S BisinottoDepartment of Large Animal Clinical Sciences, University of Florida, Gainesville, 32610, USA.
José E P SantosDepartment of Animal Sciences, University of Florida, Gainesville, 32611, USA.
Bruna MionDepartment of Animal Biosciences, University of Guelph, Guelph, N1G-2W1, Canada.
Flavio S SchenkelDepartment of Animal Biosciences, University of Guelph, Guelph, N1G-2W1, Canada.
Eduardo S RibeiroDepartment of Animal Biosciences, University of Guelph, Guelph, N1G-2W1, Canada.
Kent A WeigelDepartment of Animal and Dairy Sciences, University of Wisconsin, 1675 Observatory Dr, Madison, WI, 53706, USA.
Guilherme J M RosaDepartment of Animal and Dairy Sciences, University of Wisconsin, 1675 Observatory Dr, Madison, WI, 53706, USA.
Francisco PeñagaricanoDepartment of Animal and Dairy Sciences, University of Wisconsin, 1675 Observatory Dr, Madison, WI, 53706, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ruminants have the ability to digest human-inedible plant materials, due to the symbiotic relationship with the rumen microbiota. Rumen microbes supply short chain fatty acids, amino acids, and vitamins to dairy cows that are used for maintenance, growth, and lactation functions. The main goal of this study was to investigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, and residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1-6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Indexed as

RumenAnimal FeedAnimalsCattleDairyingFemaleGastrointestinal MicrobiomeGene Regulatory NetworksGenomeGenotypeLactationMicrobiotaPhenotypePolymorphism, Single NucleotideRNA, Ribosomal, 16SRNA, Ribosomal, 16SBayesian networkDry matter intakeResidual feed intakeRumen microbiome

Identifiers

PMID39472728
PMCPMC11522680

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.