Plant Gene and Trait 2024, Vol.15, No.1, 23-32 http://genbreedpublisher.com/index.php/pgt 24 breeding efficiency by shortening the breeding cycle and improving the agronomic traits of varieties. Although genomic selection technology has shown great potential in sugarcane breeding, its practical application still faces a series of challenges, including the high complexity of the genome, difficulties in the management and analysis of big data, and high implementation costs. These challenges need to be overcome through technological innovation and method optimization. The purpose of this review is to comprehensively evaluate the application of genomic selection technology in sugarcane breeding and explore the breeding improvements achieved through this technology. The article ultimately aims to provide a forward-looking perspective on how to achieve more effective and economical sugarcane breeding strategies through continuous technological innovation and enhanced international cooperation. With the continuous development and optimization of genomic selection technology (Mahadevaiah et al., 2021), it is expected that in the future, sugarcane breeding will be more efficient and precise, thereby better meeting the global demand for sugarcane products, promoting the development of sustainable agriculture, and achieving the dual goals of productivity improvement and environmental protection. 2 Genomic Selection Technology 2.1 Technical overview Genomic selection (GS) is a revolutionary modern breeding method that allows breeders to conduct genome-wide selection in a population rather than relying solely on limited markers or phenotypic traits. This technique was originally proposed by Meuwissen et al. (2001) and aims to predict and select individuals with the best genetic potential by covering the entire genetic map using genome-wide dense markers. In traditional breeding, the selection process usually relies on phenotypic performance and pedigree information, which is not only inefficient but may also be affected by environmental variation. In contrast, genome selection can more accurately predict the genetic value of individuals by analyzing thousands of single nucleotide polymorphism (SNP) markers associated with important agronomic traits, thereby significantly improving the efficiency and accuracy of breeding. 2.2 Key technologies The implementation of genomic selection relies on several key technologies, the first being molecular marker technology. Molecular markers, especially SNP markers, are the most commonly used tools in genome selection. SNPs serve as direct representations of genetic variation and can accurately mark genetic differences throughout the genome. Through high-throughput sequencing technology, breeders can quickly and accurately detect SNP variations of individuals in the population, which provides the possibility for comprehensive assessment of genetic potential. In addition, high-throughput sequencing technology itself is an integral part of genome selection, allowing rapid sequence determination from thousands to millions of DNA fragments simultaneously, greatly improving the depth and breadth of coverage of genetic analysis. In addition to high-throughput sequencing, genome selection also relies on other molecular biotechnologies, such as genome amplification, microarray analysis, etc. Together, these technologies allow breeders to rely on genetic markers to evaluate and select superior individuals in a population without detailed information on gene function. 2.3 Data analysis methods In genome selection, the processing and analysis of large amounts of genomic data is a complex and critical step. This requires the application of various statistical tools and computational methods to process these data to ensure that useful genetic information is obtained. First, widely used is association analysis (GWAS), which identifies SNPs associated with specific traits. This analysis often involves complex statistical models such as mixed linear models (MLM), which can take into account both genetic background and environmental noise, thereby improving the accuracy of genetic value predictions. In addition to statistical models, machine learning technology is also increasingly widely used in genome selection (Anilkumar et al., 2022). For example, use random forests, support vector machines, or artificial neural networks to predict genetic potential based on SNP markers. These methods can handle nonlinear relationships and can learn and predict trait performance from complex genetic data, further improving the accuracy and efficiency of
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