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Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep

文献类型： 外文期刊

作者： Zhang, Yanwei 1 ; Li, Wenrong 2 ; Xu, Xinming 2 ; Xie, Mengwan 1 ; Tang, Liping 1 ; Zheng, Peiyu 1 ; Song, Nannan 1 ; Yu, Lijuan 1 ; Di, Jiang 1 ;

作者机构： 1.Xinjiang Acad Anim Sci, Inst Anim Sci, Key Lab Evaluat & Utilizat Livestock & Poultry Res, Minist Agr & Rural Areas, Urumqi, Xinjiang, Peoples R China

2.Xinjiang Acad Anim Sci, Key Lab Anim Biotechnol Xinjiang, Inst Biotechnol, Urumqi 830026, Xinjiang, Peoples R China

关键词： Hetian sheep; Chinese merino sheep; coat fleece type; multi-omics; machine learning

期刊名称：FRONTIERS IN GENETICS （ 影响因子：2.8； 五年影响因子：3.3 ）

ISSN：

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Introduction The double-coated fleece is crucial for the adaptability and economic value of Hetian sheep, yet its underlying molecular mechanisms remain largely unexplored.Methods We integrated genome and transcriptome data from double-coated Hetian sheep and single-coated Chinese Merino sheep. Candidate genes associated with coat fleece type and environmental adaptation were identified using combined selective sweep and differential expression analyses. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction (PPI) network construction, and machine learning-based screening.Results Selective sweep and differential expression analyses identified 101 and 106 candidate genes in Hetian sheep and Chinese Merino sheep, respectively. Enrichment analyses revealed these genes were primarily involved in pathways related to wool growth and energy metabolism. PPI network analysis and machine learning identified IRF2BP2 and EGFR as key functional genes associated with coat fleece type.Discussion This study enhances understanding of the genetic mechanisms governing double-coated fleece formation in Hetian sheep. The identification of key genes (IRF2BP2, EGFR) and the methodological approach provide valuable insights for developing machine learning-driven multi-omics selection models in sheep breeding.

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