International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.3, 134-143 http://ecoevopublisher.com/index.php/ijmec 140 Pan-genomic data can more accurately measure the genetic distance and diversity between populations, thereby providing a basis for more targeted conservation and hybridization programs. The "Vargoat" project provides intuitive evidence: the genetic diversity of global goats is extremely high, and some local breeds carry unique variations that urgently need priority protection (Denoyelle et al., 2021). Furthermore, many newly identified DNA fragments are adjacent to QTLS with traits such as milk yield and meat yield (Yang et al., 2024; Azam et al., 2025), suggesting that structural variations can serve as practical markers in breeding. 6.3 Comparative insights with the pan-genome of other species Cross-species comparisons reveal both commonalities and differences. After domestication, many domestic animals have experienced genomic "contraction" : wild populations retain more unique sequences and structural variants, while domesticated breeds are more uniform (Yang et al., 2024). This pattern supports the view that the "domestication syndrome" has led to a decline in diversity. The differences were equally striking - the main changes in sheep pointed to lipid metabolism (Dai et al., 2023); The signals of goats are more focused on behavioral and reproductive pathways (Li et al., 2023b). Intergenus studies provide clues to speciation and adaptation. The bison pan-genome simultaneously identified unique and shared sequences pointing to a common ancestor or cross-species gene flow (Zhou et al., 2022). Extending similar work to all goat species can help clarify pedigree boundaries and highlight conserved genes and innovative genes. Methods from other fields can also be referred to: The human pan-genome uses graph structure and long-read sequencing to resolve complex regions (Liao et al., 2023), and this approach is applicable to the study of immune loci in goats (Gong et al., 2023). Studies on pigs have also shown that disease-resistant genes are often preserved in local breeds (Li et al., 2020); Goat breeding should incorporate these local superior genes into the system. 7 Research Challenges and Future Prospects 7.1 Technical and computational challenges The application of the pan-genome in higher animals such as goats still faces technical and computational challenges. Constructing a high-quality pan-genome requires high-precision assembly of multiple individuals. However, due to the existence of repetitive sequences and complex regions, chromosome-level assembly is still not perfect, and there are often blanks in regions such as centromeres and ribosomal DNA. Future T2T assembly technology is expected to improve this situation (Gong et al., 2023). The pan-genome integrates dozens of long-read assemblies and has extremely high requirements for storage, memory and computing efficiency. The accuracy and speed of the existing Graph Genome algorithm in repetitive regions are still insufficient. Genome-wide genotype inference and association analysis require more efficient processes, and visualization and data sharing also urgently need new tool support. The integration of genomic, phenotypic and environmental data from different sources is rather difficult, and there is a lack of unified processing standards. The field of livestock has not yet formed a mature graphic genome format and database, which limits the comparison and application among studies. To unlock the potential of the pan-genome, it is necessary to improve assembly algorithms, utilize cloud computing and parallel computing to process big data, and develop specialized analysis and sharing platforms, thereby promoting its regular application in livestock genomics. 7.2 Trends in functional verification and multi-omics integration The pan-genome has produced a large number of candidate variations, but it is still not easy to prove their true functions. It is difficult to conduct large-scale functional tests on domestic animals such as goats, but molecular-level verification is advancing. For instance, CRISPR/Cas9 has been used in hornless goats, indicating that it is feasible to conduct functional tests in livestock. Combining transcriptome, proteome and epigenome data can narrow the candidate range and enhance the strength of evidence (Li et al., 2020; Gong et al., 2023). Structural variations in non-coding regions may regulate gene activity and need to be detected by means of ATAC-seq or ChIP-seq. Population genetic analysis can also provide clues: variations enriched in extreme trait groups are often related to phenotypes. Global collaboration and resource sharing will promote the functional analysis of both encoded and non-encoded components. Subsequent research should pay more attention to the
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