Genomics and Applied Biology 2026, Vol.17, No.1, 1-15 http://bioscipublisher.com/index.php/gab 7 known phenotypes. Among the 10 cultivars, the RKHS model correctly predicted the phenotypes for 7 cultivars, demonstrating its strong predictive capability. Based on genotyping results from the Rice3K56 chip for hundreds of multi-parent advanced generation inter-cross and RIL populations, Zhang et al. (2023) conducted genomic selection analysis on plant height and heading date using nine prediction models, including GBLUP and EGBLUP. The results demonstrated that prediction accuracy is co-influenced by three key factors: population structure, trait heritability, and statistical model, with the optimal model varying depending on the target trait. This study further validates the application potential of genomic selection in breeding programs. Guan et al. (2024) tested the performance of the MaizeGerm50K array in genomic selection analysis by applying RKHS and RR-BLUP models to predict five maize traits (days to anthesis, plant height, ear height, ear weight, and grain yield per plant). The results showed that both models achieved relatively high prediction accuracy (>0.59), with the highest accuracy observed for days to anthesis (0.76 ± 0.03 for RKHS and 0.71 ± 0.029 for RR-BLUP). Furthermore, the RKHS model outperformed RR-BLUP in predicting the four traits above, demonstrating the particular suitability of the MaizeGerm50K array for genomic selection. The integration of GS with gene chip provides an efficient and precise predictive tool for modern crop breeding. GS harnesses genome-wide, high-density SNP markers to predict breeding values through statistical and machine learning models. This approach is successfully improving traits such as yield (Kim et al., 2022), advancing the transition to digital breeding. 4 Conclusion As a vital tool in crop improvement, nearly 100 gene chips have been developed for plant research, covering over 25 crop species and perennial trees. These chips have facilitated the identification of QTLs and major genes closely linked to crop traits, significantly advancing breeding improvement in wheat, rice, maize, soybean, cotton, and other crops (Rasheed et al., 2017). Although solid-phase and liquid-phase gene chips differ in their technical principles, they offer analogous solutions for genotyping. While the former is less flexible, the SNP marker sets accumulated during their development continue to provide valuable resources, informing the design and optimization of liquid-phase gene chips. For instance, Liu et al. (2025b) referenced SNP datasets from the solid-phase gene chip Wheat 660K during the development of the WheatSNP16K chip, while Guo et al. (2019) optimized the existing SNP set from a 55K solid-phase gene chip (Xu et al., 2017) to develop 20K GBTS probes, and conducted a comparative analysis between the new and old chips based on genotyping data from diverse maize germplasms. Among the various applications of gene chips, kinship analysis and GWAS are the most frequently applied (Table 1), primarily because the genotype information obtained can be directly correlated with phenotype, converting sequence variation into actionable insights for breeders' selection decisions. Research on gene chips has slowed in recent years, mainly due to the increasingly comprehensive mining of SNPs across crop genomes. However, for polyploid crops like wheat, whose chromosome complexity makes whole-genome sequencing costly, there remains considerable scope for developing more cost-effective gene chips. Furthermore, novel genotyping technologies such as Hyper-seq (Zou et al., 2022) have emerged, which combine PCR with high-throughput sequencing. This method offers advantages such as low cost, high efficiency, a high degree of automation, and good flexibility. It has been successfully applied to library construction, sequencing, and genotyping of 2 094 samples from six crops. Despite continuous innovation in genotyping technologies, methods such as KASP, solid-phase gene chips, GBS, GBTS, and Hyper-seq do not simply replace one another. Instead, each demonstrates unique strengths and applicability within specific domains. This diversity enriches the toolbox for genotyping, enabling researchers to select the most appropriate method based on their specific objectives and conditions to unravel genetic information.
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