Genome−wide diversity of Chinese Sinan and Xiangxi beef cattle populations based on whole−genome resequencing

This study conducted analysis of genetic diversity, population structure, and selection signatures in Sinan cattle (SNC) and Xiangxi cattle (XXC) using whole-genome resequencing data. SNC and XXC cattle are indigenous beef populations in southwestern China, with ongoing conservation and breeding programs managed by their respective original breeding farms. Whole-genome sequencing of 28 SNC and 23 XXC individuals achieved an average depth of 12.3× (SD = 1.4) and 12.8× (SD = 1.6), respectively. Using genome-wide SNP data (mean MAF 0.1034–0.1080, PIC 0.1257–0.1312), we found that XXC exhibited significantly higher observed heterozygosity than SNC (Ho = 0.1275 vs. 0.1195, t-test p < 0.01) and faster LD decay, indicating greater genetic diversity. ADMIXTURE cross-validation showed K = 1 as optimal (highest likelihood, lowest CV error), indicating no significant stratification; K = 7 revealed subtle differentiation (ΔK = 42.3), suggesting incipient divergence. Selection signature analysis employed pairwise Fst and θπ ratio (top 5% as threshold). We identified 1,315 significant genomic regions, comprising 1,993 candidate genes in SNC and 1,407 in XXC. Only 218 genes (12.9%) were shared between the two populations, indicating divergent selection pressures. KEGG enrichment analysis revealed that PI3K-Akt ( P = 0.003) and MAPK ( P = 0.008) pathways were significantly enriched in SNC, while tuberculosis ( P = 0.015) and AMPK ( P = 0.021) pathways were significantly enriched in XXC. GO enrichment analysis, after Benjamini‑Hochberg correction (FDR < 0.05), showed that SNC genes were significantly enriched in actin cytoskeleton (GO:0015629, P = 0.009) and mitochondrial membrane (GO:0031966, P = 0.013), whereas XXC genes were significantly enriched in synaptic structure (GO:0098794, P = 0.024) and dendritic development (GO:0097447, P = 0.031). These functional divergences likely reflect population‑specific adaptations. This work establishes a genomic foundation for future association studies and marker-assisted selection in these two important indigenous populations.