Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.

Journal: Frontiers in genetics
Published Date: Jan 6, 2025

Abstract

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, , and , were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.

Authors

  • Yumei Shi
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Xini Wang
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Shaokang Chen
    Beijing Animal Husbandry Station, Beijing, China.
  • Yanhui Zhao
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Xihui Sheng
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Xiaolong Qi
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Lei Zhou
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yu Feng
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Jianfeng Liu
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Chuduan Wang
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Kai Xing
    College of Animal Science and Technology, China Agricultural University, Beijing, China.

Keywords

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