Evidence map›Paper›PMID 39202463›Full record

ReviewGenes2024

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality.

Rugang Tian, Maryam Mahmoodi, Jing Tian, Sina Esmailizadeh Koshkoiyeh, Meng Zhao, Mahla Saminzadeh, Hui Li, Xiao Wang, Yuan Li, Ali Esmailizadeh

Abstract readReview
In one paragraph

Review in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
    Review
  10. Veterinary world · 2025
    Review
  11. Review
  12. Review
  13. Article
  14. Epigenetic landscape revealsFrontiers in nutrition · 2025
    Article
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

10 authors.

Rugang TianInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.
Maryam MahmoodiDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran.
Jing TianInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.
Sina Esmailizadeh KoshkoiyehDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran.
Meng ZhaoInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.
Mahla SaminzadehDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran.
Hui LiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.ORCID 0000-0003-2819-7637
Xiao WangInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.
Yuan LiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.ORCID 0000-0002-3239-0600
Ali EsmailizadehDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran.ORCID 0000-0003-0986-6639

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Indexed as

BreedingGenome-Wide Association StudyGenomicsRed MeatAnimalsCattleMeatMetabolomicsProteomicsQuantitative Trait Locibeef cattlefunctional genomicsgenomic selectionGWASmeat qualitymolecular breedingomics technologies

Identifiers

PMID39202463
PMCPMC11353656

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.