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

Abstract

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.