Evidence map›Paper›PMID 40302672›Full record

ArticleAnimal bioscience2025

Meta-analysis of muscle transcriptome data identifies key genes influencing intramuscular fat content in pigs.

Yan Wang, Jiahao Wang, Yanhui Zhao, Xihui Sheng, Xiaolong Qi, Lei Zhou, Jianfeng Liu, Chuduan Wang, Jianliang Wu, Yongchun Cao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animal bioscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

11 authors.

Yan WangState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Jiahao WangState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Yanhui ZhaoCollege of Animal Science and Technology, Beijing University of Agricultural, Beijing, China.
Xihui ShengCollege of Animal Science and Technology, Beijing University of Agricultural, Beijing, China.
Xiaolong QiCollege of Animal Science and Technology, Beijing University of Agricultural, Beijing, China.
Lei ZhouState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Jianfeng LiuState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Chuduan WangState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Jianliang WuBeijing Zhongyu Pig Breeding Co., Ltd., Beijing, China.
Yongchun CaoState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Kai XingState Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.

Funding

Biological Breeding-National Science and Technology Major Project 2023ZD04046China Agriculture Research System 35
6 · The paper itself

Abstract

objectiveThe content of intramuscular fat (IMF) is closely linked to meat quality, and the mechanism of IMF deposition is complex. Despite numerous transcriptomic studies on IMF, variations in sample sizes and data analysis methods have produced inconsistent gene expression patterns and results. To identify the pivotal genes influencing pig IMF content, we performed a meta-analysis on 10 pig muscle transcriptome datasets with a total of eighty samples, forty with high and forty with low IMF samples.

methodsDESeq2 has been used to analyze the high and low IMF groups for 10 datasets each, resulting in the differentially expressed genes (DEGs) for each dataset. To identify key genes affecting IMF content, we performed a meta-analysis of the differential expression results from the 10 datasets using MetaVolcanoR. Subsequently, we conducted protein-protein interaction network analysis, Gene Ontology and Kyoto encyclopedia of genes and genomes functional enrichment analysis, and quantitative trait locus (QTL) analysis on the DEGs.

resultsThe meta-analysis identified 129 DEGs, comprising 71 upregulated and 58 downregulated DEGs in the high IMF group. The DEGs exhibited enrichment in processes associated with adipocyte differentiation and fat anabolism. QTL analysis demonstrated that five DEGs, including FASN and SCD, corresponded to six QTLs associated with IMF.

conclusionThe findings suggest that meta-analysis effectively integrates data from multiple datasets, resulting in more reliable outcomes. This approach enabled the identification of the core gene cluster comprising FASN, SCD, and PLIN1, LEP, and G0S2, which influence IMF content in pigs.

Indexed as

Intramuscular FatMeta-analysisPigRNA-Seq

Identifiers

PMID40302672
PMCPMC12229906

What Socratic holds

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LicenceCC BY
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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 Socratic graph.