Evidence map›Paper›PMID 39930002›Full record

ArticleCommunications biology2025

Multiple strategies association revealed functional candidate FASN gene for fatty acid composition in cattle.

Bo Zhu, Tianzhen Wang, Qunhao Niu, Zezhao Wang, El Hamidi Hay, Lei Xu, Yan Chen, Lupei Zhang, Xue Gao, Huijiang Gao and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Animals : an open access journal from MDPI · 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

14 authors.

Bo Zhu *State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Tianzhen Wang *State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Qunhao NiuState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Zezhao WangState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
El Hamidi HayUSDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT, USA.
Lei XuState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Yan ChenState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Lupei ZhangState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Xue GaoState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Huijiang GaoState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
Yang CaoKey laboratory of Beef Cattle Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Changchun, China.
Yumin ZhaoKey laboratory of Beef Cattle Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Changchun, China.
Lingyang XuState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China. xulingyang@caas.cn.ORCID http://orcid.org/0000-0002-8463-6668
Junya LiState Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China. lijunya@caas.cn.ORCID http://orcid.org/0000-0001-8051-050X

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31972554, 32272843 and 32272835
6 · The paper itself

Abstract

Fatty acid composition (FA) is an important indicator of meat quality in beef cattle. We investigated potential functional candidate genes for FA in beef cattle by integrating genomic and transcriptomic dataset through multiple strategies. In this study, we observed 65 SNPs overlapping with five candidate genes (CCDC57, FASN, HDAC11, ALG14, and ZMAT4) using two steps association based on the imputed sequencing variants. Using multiple traits GWAS, we further identified three significant SNPs located in the upstream of FASN and one SNP (chr19:50779529) was embedded in FASN. Of those, two SNPs were further identified as the cis-eQTL based on transcriptomic analysis of muscle tissues. Moreover, the knockdown of FASN yielded a significant reduction in intracellular triglyceride content in preadipocytes and impeded lipid droplet accumulation in adipocytes. RNA-seq analysis of preadipocytes with FASN interference revealed that the differentially expressed genes were enriched in cell differentiation and lipid metabolic pathway. Our study underscored the indispensable role of FASN in orchestrating adipocyte differentiation, and fatty acid metabolism. The integrative analysis with multiple strategies may contribute to the understanding of the genetic architecture of FA in farm animals.

Indexed as

Fatty AcidsFatty Acid Synthase, Type IAdipocytesAnimalsCattleGenome-Wide Association StudyLipid MetabolismPolymorphism, Single NucleotideQuantitative Trait LociFatty AcidsFatty Acid Synthase, Type I

Identifiers

PMID39930002
PMCPMC11811213

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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.