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ORIGINAL RESEARCH article

Front. Genet.
Sec. Livestock Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1503148
This article is part of the Research Topic Insights in Livestock Genomics View all 3 articles

Identification of Key Genes Affecting Intramuscular Fat Deposition in Pigs Using Machine Learning Models

Provisionally accepted
Yumei Shi Yumei Shi 1Xini Wang Xini Wang 1Shaokang Chen Shaokang Chen 2Yanhui Zhao Yanhui Zhao 2Yan Wang Yan Wang 3Xihui Sheng Xihui Sheng 3Xiaolong Qi Xiaolong Qi 3Lei Zhou Lei Zhou 1Yu Feng Yu Feng 1Jian-Feng Liu Jian-Feng Liu 1Chuduan Wang Chuduan Wang 1Kai Xing Kai Xing 1*
  • 1 China Agricultural University, Beijing, China
  • 2 Beijing Animal Husbandry Station, Beijing, Beijing Municipality, China
  • 3 Beijing University of Agriculture, Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of intramuscular fat deposition. Machine learning (ML) is a new big data analysis 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 transcriptome data by ML method to identify differentially expressed genes (DEGs) affecting intramuscular fat deposition in pigs. In this study, a total of 74 transcriptome sequencing 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 6 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, PLA2G6, MPV17, NUDT2, and ND4L, 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 intramuscular fat content in pigs.

    Keywords: machine learning, pig, Transcriptome, Intramuscular fat, key genes

    Received: 28 Sep 2024; Accepted: 09 Dec 2024.

    Copyright: © 2024 Shi, Wang, Chen, Zhao, Wang, Sheng, Qi, Zhou, Feng, Liu, Wang and Xing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kai Xing, China Agricultural University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.