Plant Gene and Trait 2024, Vol.15, No.1, 23-32 http://genbreedpublisher.com/index.php/pgt 26 In genomic selection, a large number of genetic markers need to be analyzed to identify advantageous genetic characteristics, but the complex polyploid nature of sugarcane makes it extremely difficult to precisely demarcate the location of each genetic variation (Trujillo-Montenegro et al., 2021). Each specific trait may involve complex interactions of multiple genes, which is particularly evident in polyploid sugarcane, as a single trait may be regulated by similar genes on multiple chromosomes. In addition, the hybrid background of sugarcane is complex and there may be extensive genetic diversity among different genotypes. While this diversity provides a rich resource for breeding, it also poses additional challenges to the accuracy and prediction of genomic selection. Especially when predicting the effects of individual genes on traits and achieving genetic gain, the complexity of the genetic background may lead to inaccurate predictions or unstable breeding effects. Figure 1 Schematic for genomic selection (GS) approach in sugarcane for increasing the rate of genetic gain and reducing generation intervals (Adopted from Meena et al., 2022) Therefore, in order to overcome these biological obstacles, it is necessary to develop more advanced genetic analysis tools and technologies, such as using high-throughput sequencing technology and precise bioinformatics methods to process and analyze large-scale genetic data. Through the application of these technologies, complex polyploid genomes can be better understood and utilized, thereby improving the accuracy and efficiency of genome selection (Chen et al., 2023). At the same time, continued research efforts also need to focus on improving the understanding of the genetic structure of sugarcane, which will provide a stronger scientific basis for future breeding efforts. 4.2 Technology and data challenges With the explosive growth of genomic data, data management and processing have become another major technical challenge. Genomic selection relies on a large amount of genetic marker information to predict genetic potential, which requires the storage, management and analysis of massive data. In sugarcane breeding projects, each genotype may involve millions of SNP markers, and processing this data requires powerful computing resources and efficient data processing algorithms. In addition, the processing of genetic data involves complex statistical analysis and bioinformatics tools, which requires not only a stable computing platform, but also professional technical support to ensure the accuracy and efficiency of data analysis.
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