International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.1, 19-29 http://ecoevopublisher.com/index.php/ijmec 26 quantitative traits and simple SNP markers. Now SV and potential HGT markers can be included in breeding considerations. For example, through whole genome selection (GWAS), it is found that a certain structural variation is strongly correlated with traits such as disease resistance and heat resistance, which can be used as a target for marker-assisted breeding (Yang et al., 2024). Studies have constructed a pan-genome variation database for goats, which includes unique sequences and SVs of different breeds. Breeding experts can use this to select parent combinations with rich genetic diversity to increase the adaptability potential of offspring (Bian et al., 2024). In addition, genetic engineering methods have opened the door to directly using HGT to inspire genetic improvement. If a species in nature has the stress resistance gene required by goats, HGT can be simulated and introduced into the goat genome through transgenic or gene editing. In terms of the use of structural variation, genome editing (such as CRISPR/Cas9) can precisely copy or delete certain SVs, thereby verifying their functions and applying them to breeding. In the past decade, CRISPR has been used for goat hornless trait editing, disease resistance gene knock-in, etc. It is natural to expand to environmental adaptability traits in the future. 6.2 Development direction of omics technology and detection methods Future research on goat HGT and SV will greatly benefit from the promotion of emerging omics technologies. Sequencing technology will continue to develop, providing higher quality genome assembly and more sensitive variation detection. Pan-genome and multi-genome comparison will become routine methods. By constructing a pan-genome of goats containing wild populations and different breeds, we can systematically discover various environmental-related sequence variations. Furthermore, artificial intelligence and big data analysis have broad application prospects in HGT/SV research. Machine learning algorithms can be trained to identify exogenous gene patterns and improve the accuracy of HGT prediction (Wijaya et al., 2025). In addition, multi-omics fusion will provide new ideas for HGT and SV functional verification. Combining genomic variation data with transcriptome, proteome, and metabolome can map the entire path from variation to phenotype. For example, through the joint analysis of eQTL and GWAS, we can find out which SVs significantly affect gene expression and are associated with adaptive traits (Zhang et al., 2024). New technologies will also help conduct ancient DNA and evolution experiments. Through ancient goat DNA sequencing, we can track the time and frequency changes of HGT fragments or SVs in evolution and verify whether they are selected (Bian et al., 2024). 6.3 Multidisciplinary intersection and functional verification In terms of multidisciplinary intersection, molecular evolution, biochemistry, structural biology, etc. can all contribute. Through molecular evolution analysis, it can be determined whether HGT genes have undergone positive selection in goats, thereby indirectly confirming their functional importance (Verneret et al., 2025). Biochemistry and structural biology can analyze the functional mechanism of a certain HGT protein, such as determining its three-dimensional structure to see if it has a similar effect to the original species (Sun et al., 2015). This helps to understand whether HGT genes have new functions for goats. Systems biology is also one of the future directions. Taking the entire physiological system of goats as the research object, simulating the impact of different environmental pressures on multiple organs and multiple levels of goats, embedding relevant HGT genes and SV into the system model, and seeing if they can explain adaptive changes. Comparative genomics is still a powerful tool. Goats can be compared with other species that are resistant to extreme environments to find common HGT or SV patterns. For example, compare plateau goats, yaks, plateau deer, etc. to see if they share certain HGT fragments or have similar gene amplifications. Through multidisciplinary bridging, the genetic variation on paper will be connected with the adaptive behavior of living goats, making the understanding of adaptive evolution more full and three-dimensional. 6.4 Prospects for the study of adaptive evolution of goats In scientific terms, in-depth analysis of the adaptive evolution of goats will enrich evolutionary theory. In terms of application, research progress will help the sustainable development of animal husbandry. In the context of
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