International Journal of Molecular Evolution and Biodiversity, 2025, Vol.15, No.2, 64-72 http://ecoevopublisher.com/index.php/ijmeb 65 This study will focus on the practical transformation and strategy optimization of GS technology in potato breeding. In view of the dosage effect and allele interaction problems brought about by the selfing tetraploid genome, the study proposed an improved statistical modeling framework to enhance the prediction ability and applicability. While improving the efficiency of multi-trait selection, this model also provides methodological support for precision breeding under complex genome structures. This study not only provides a theoretical basis for addressing global food security challenges, but also provides a practical technical path for potato breeders. At the same time, it also has important reference significance for promoting the application of genomic selection in other polyploid crops. 2 Principles and Methods of Genomic Selection 2.1 Basic theories of genomic selection Genomic selection (GS) technology, as a revolutionary tool for modern breeding, has completely reshaped the traditional phenotype-based selection method. Its basic concept is to use high-density molecular marker information on a genome-wide scale to predict the genetic potential of individuals, thereby achieving early screening without phenotypic measurement (Pandey et al., 2023). This method is particularly suitable for quantitative traits regulated by multiple genes, and is particularly prominent in polyploid crops. Taking tetraploid potatoes as an example, GS technology has been widely used in the efficient breeding of key traits such as disease resistance, tuber yield and nutritional quality through the accurate prediction of genomic estimated breeding values (GEBV). It is reported that this method has brought more than 35% genetic gain in actual breeding (Slater et al., 2016). In addition, the application advantages of GS technology in asexually propagated crops are particularly obvious. It not only significantly improves the selection efficiency, but also shortens the breeding cycle of more than ten years by 40%~60% (Enciso-Rodríguez et al., 2018). 2.2 Model construction for genotype-phenotype association Constructing an efficient prediction model is the core link of GS technology. The whole-genome regression model can explain more than 85% of the genetic variation of traits such as potato late blight resistance by integrating additive genetic effects and dominant effects (Wu et al., 2025). The optimization of model parameters directly affects the prediction accuracy: when the marker density reaches one SNP per 5 cM, the prediction accuracy can reach 0.65~0.78. It is worth noting that the dosage effect model unique to tetraploids can more accurately capture allele interactions, further improving the prediction accuracy by 12%~15%. 2.3 Key technologies The current development of GS technology is based on the rapid progress of three core supporting technologies: (1) High-throughput genotyping platform: The high-throughput platform represented by genotyping-by-sequencing (GBS) can complete SNP detection of large-scale samples in a short time. For example, the analysis of 1,000 samples can be completed within 48 hours, and the cost of single sample detection has been reduced to less than US$5, which greatly improves the efficiency of resource utilization. (2) Advanced statistical modeling methods: genomic best linear unbiased prediction (GBLUP) is a mainstream algorithm that effectively solves the problems of uneven marker distribution and allele frequency deviation by constructing a genomic relationship matrix, thereby improving the prediction ability of complex traits (Selga et al., 2020); (3) Artificial intelligence and deep learning algorithms: In recent years, the introduction of AI technology has significantly promoted the leap in GS accuracy. Deep neural networks can identify complex nonlinear relationships between SNPs and target traits, and the model prediction performance is 20%~30% higher than traditional methods (Caruana et al., 2019). The integration and synergy of the three have promoted the rapid maturity of the GS technology system, increasing the breeding efficiency of crops such as potatoes by 3~5 times, and significantly strengthening the scientific and technological reserves for responding to global food security challenges (Ortiz, 2020). 3 Current Applications of Genomic Selection in Potato Breeding 3.1 Breeding goals for yield and quality improvement In recent years, genomic selection (GS) has become an important breeding method to improve potato yield and quality. Through genomic valuation of complex agronomic traits such as yield, maturity, and processing
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