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.

Keywords: Chinese merino sheep; Hetian sheep; coat fleece type; machine learning; multi-omics.

FIGURE 1

Geographical distribution of Hetian and Chinese Merino sheep, sampling locations and coat characteristics. (A) Geographic distribution and sampling locations. (B) Chinese merino sheep. (C) Single-coated fleece from Chinese Merino sheep. (D) Hetian sheep. (E) Double-coated fleece from Hetian sheep.

FIGURE 2

(A) Fst analysis of Manhattan (Fixation Indices: 0–0.05: genetic differentiation between populations is small and negligible. 0.05–0.15: Moderate genetic differentiation between populations. 0.15–0.25: Genetic differentiation between populations. >0.25: There is significant genetic differentiation between populations). (B) Fst and θπ ratio strategy selection signature analysis results are displayed (Note: The abscissa is the Log2π ratio, the ordinate is the F _st score, which corresponds to the frequency distribution map above and the frequency distribution map on the right, respectively, and the dot plot in the middle represents the corresponding F _st and Log2π ratio in different windows. The blue and redareas are the top 5% areas selected by F _st and Log2Pi ratio, blue represents Hetian sheep, and red represents Chinese merino sheep). (C) HT vs. CM mRNA gene level differential analysis results. (D) Venn diagram for Fst and θπ ratio and differential expression analysis (red: CM F _st and θπ ratio; blue: HT F _st and θπ ratio; grey: DEGs).

FIGURE 3

(A) The double-coated fleece trait (HT); (B) The single-coated fleece trait (CM); (C) The first interaction network of the double-coated fleece trait (HT); (D) The first interaction network of the single-coated fleece trait (CM).

FIGURE 4

(A) Protein-protein interaction networks in the double-coated fleece trait. (B) Protein-protein interaction networks in the single-coated fleece trait. (C) Feature genes for double-coated fleece. (D) Feature genes for single-coated fleece. The abscissa coordinates show the importance of the features, sorted by the importance of the variables in descending order.

FIGURE 5

Expression levels of key genes associated with HT and CM coat types and wool growth traits. (A) IRF2BP2 gene. (B) EGFR gene. (C) FEN1 gene. (D) TP53 gene. (E) STMN1 gene. (F) ALOX12 gene. (G) BHLHE40 gene. (H) LY96 gene. (I) AT5ME gene.