International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.3, 134-143 http://ecoevopublisher.com/index.php/ijmec 138 and a presence/absence matrix. The entire process requires the comprehensive application of assembly and alignment tools and manual verification to ensure sequence accuracy (Crysnanto et al., 2021; Li et al., 2023b). This process can effectively capture the rich structural variations of the genus goats, providing comprehensive genomic resources for population analysis. 4.3 Data mining and functional annotation tools This study adopted a clear process to conduct functional labeling and biological interpretation of the goat pan-genome. First, use tools such as MAKER to predict the gene model in the new sequence; Then, the annotation was completed by searching databases such as NCBI NR and UniProt with BLAST+ (Li et al., 2023b). For newly discovered variant regions, GWAS and population genetic statistics (such as F_ST) were used to detect their associations with population structure and trait selection (Sasazaki et al., 2021). The outline of gene functions has become clear. Core genes undertake the underlying affairs of cells, such as metabolism. In contrast, variant genes are more often involved in shaping adaption-related traits, such as disease resistance (Gao et al., 2019; Li et al., 2020). To further reveal its mechanism, this study integrates multiple layers of biological data. Transcriptomics is used to track expression fluctuations and also to assess how DNA variations switch genes. Epigenomics describes the alterations in chromatin state and chromosome configuration (Denoyelle et al., 2021). This combinatorial strategy not only reveals historical selection signals, but also captures events such as gene loss and the clearance of harmful mutations during the domestication stage (Li et al., 2023b). With the iteration of computing tools, pan-genomic datasets are expected to more efficiently locate key DNA differences and further explain the genetic basis of phenotypic diversity and environmental adaptation in goats. 5 Analysis of the Functional Characteristics of the Core and Alternative Genomes 5.1 Conserved functions of core genes The core genes in the goat pan-genome are ubiquitous in all individuals and mainly undertake the basic functions of maintaining life activities, thus being highly conserved in evolution. These genes are mostly involved in metabolism, cell structure, reproduction and development. For instance, ribosomal proteins, metabolic enzymes and cytoskeletal proteins remain almost unchanged in all goats, demonstrating their indispensability. The key genes in the signaling pathway are also highly consistent among different varieties, indicating that they are strongly functionally constrained and prevent the accumulation of harmful mutations. GO functional enrichment analysis revealed that core genes were concentrated in basic categories such as "metabolic processes", "cell cycle", and "nucleic acid synthesis" (Gao et al., 2019). Similar to other domestic animals, core genes account for the majority of the total number of genes (more than 90% in chickens (Li et al., 2020), while a few variable genes are associated with specific functions. This indicates that core genes ensure species survival by stably performing key life processes, and at the same time provides an important reference for cross-species comparative genomics. 5.2 Environmental adaptability associations of variable genomes Goat populations display genetic differences shaped by local adaptation and by human breeding. High-elevation habitats are harsh-thin air, cold, and intense sun. In mountain goats, HSP genes buffer proteins and bolster immunity (Figure 2). They also carry distinctive variants in oxygen-sensing genes such as EPAS1 and HIF1A, boosting red-blood-cell production and oxygen transport (Lu et al., 2025). In hot and arid regions, the loci of heat shock genes such as HSP70 often undergo changes. In cold climates, the situation is different. The variations of UCP1 and FGF5 regulate heat production and also promote wool growth. The artificially selected imprints are clear: Dairy goats are enriched with milk-related genes, while Angora goats accumulate more fiber gene loci (Li et al., 2023b). The differences in immune structures are also obvious. The TLR lineages of different groups are not consistent, reflecting the disease ecology and pathogen pressure in various regions (Li et al., 2020). The combination of the above models enables goats to resist cold and heat, tolerate hypoxia and enhance their disease resistance. 5.3 Selection signals of variable regions and population specificity Pan-genome analysis revealed selective footprints and population-specific translocations. Some gene fragments
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