International Journal of Molecular Evolution and Biodiversity, 2025, Vol.15, No.2, 64-72 http://ecoevopublisher.com/index.php/ijmeb 67 Australian researchers have developed a high-density SNP marker panel by integrating high-throughput genotyping and transcriptome sequencing technologies to build a high-precision GS model, which greatly simplifies the traditional breeding process (Caruana et al., 2019). Although the complex genome structure of tetraploid potato poses challenges in technical implementation, the application boundaries of GS are constantly expanding with the help of statistical modeling and algorithm optimization. Several international breeding programs have shown that GS is not only highly feasible but also widely adaptable in polyploid crops (Slater et al., 2016). 4 Enhancing Breeding Efficiency with Genomic Selection 4.1 Shortening breeding cycles and improving efficiency Genomic selection (GS) technology has reshaped the time structure of traditional breeding and achieved a significant leap in efficiency. Traditional potato breeding is extremely dependent on phenotypic screening and molecular marker-assisted breeding. The entire process usually takes more than ten years to breed a stable new variety. The introduction of GS breaks this time bottleneck. By using genomic information to make selection decisions in early generations, breeders can skip multiple lengthy screening steps, thereby significantly compressing the breeding cycle (Wu et al., 2023). Not only that, GS can also achieve simultaneous optimization of multiple key traits, bringing an overall improvement in breeding efficiency. 4.2 The role of genomic data in selection accuracy 4.2.1 The relationship between marker density and prediction accuracy When establishing a genomic prediction model, the choice of marker density is a key decision. Although in theory, higher-density molecular markers can cover more genetic information and help improve prediction accuracy, studies have found that a reasonably optimized marker subset can also show similar predictive ability to whole-genome markers (Sverrisdóttir et al., 2018). This result is particularly important for tetraploid crops such as potatoes, because their genome structure is complex, and high-density typing of large populations will bring huge financial and technical pressures (Selga et al., 2020). Therefore, in actual operations, how to find a balance between information acquisition and cost control has become a key step in promoting the implementation of GS technology. 4.2.2 The impact of genetic background on model performance The genetic structure of different breeding populations significantly affects the performance of prediction models. Models that perform well in a specific population may show significant fluctuations in predictive effectiveness in other genetic backgrounds (Enciso-Rodríguez et al., 2018). This phenomenon emphasizes the importance of developing population-specific prediction models. Only by fully considering the genetic characteristics of the population can the prediction accuracy of genomic selection be maximized. 4.2.3 Integration of genomic data for multi-trait selection Traditional breeding often focuses on a single target trait, while the emergence of genomic selection (GS) technology provides a new path for the simultaneous improvement of multiple traits. In potato breeding, researchers have achieved a coordinated improvement in yield potential, processing quality, and disease resistance by establishing a scientific and reasonable multi-trait selection index (Martins et al., 2023). With the help of integrated analysis of genomic data, breeders can efficiently screen out excellent genotypes with multiple ideal traits. Compared with item-by-item improvement, this comprehensive strategy significantly improves breeding efficiency and enhances the adaptability and comprehensive performance of new materials in actual planting (Pandey et al., 2023). GS not only breaks through the possible negative correlation barriers between traits, but also improves the systematicness and accuracy of selection, and promotes potato varieties to steadily move towards the goal of multiple excellence. 4.3 Comparison with traditional breeding methods Compared with traditional phenotypic selection, genomic selection shows many advantages. In terms of genetic gain, GS has shown obvious advantages in both short-term and long-term breeding results. More importantly,
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