Genomics and Applied Biology 2026, Vol.17, No.1, 1-15 http://bioscipublisher.com/index.php/gab 6 JS16 and BN64 with a 660K SNP chip, combined with field resistance data and composite interval mapping (CIM), led to the identification of four QTLs related to powdery mildew resistance. These QTLs exhibited additive effects, specific resistance, and no linkage to other traits, effectively utilizing the important cultivar BN64 to uncover novel QTLs. Zhou et al. (2022) genotyped an RIL population from a cross between wheat cultivars Pindong 34 and Mingxian 169 using a 90K SNP chip. By integrating phenotypic data and CIM analysis, they mapped 15 QTLs associated with stripe rust resistance. This work provided the first comprehensive dissection of the genetic basis of resistance in the excellent source 'Pindong 34', revealing that its resistance is controlled by multiple QTLs and thereby enriching the gene resource pool for MAS. Gene chip technology is also widely used for mining QTLs governing other crucial agronomic traits, such as wheat culm morphology (tiller number (Ren et al., 2018), flag leaf morphology (Cheng et al., 2023)), yield (Shi et al., 2017; Cao et al., 2019; Liu et al., 2023) and starch content (Fu et al., 2019), barley disease resistance (Choudhury et al., 2019), cotton fiber quality (Ramesh et al., 2019), and sugarcane tiller number (Fang et al., 2025). Gene chips not only facilitate QTL discovery but are also integrated with genome-wide association studies (GWAS). By associating genotype with phenotype data, GWAS serves as a powerful tool for mapping qualitative trait genes, achieving significant success in crops like rice, wheat, and cotton. Zhang et al. (2023) developed a high-quality custom chip Rice3K56 using resequencing data from 3 024 rice accessions worldwide. By testing extensively in 192 representative rice samples, this chip contains 56,606 SNP markers with a high genotyping reliability of 99.6%. Based on genotype and phenotype data from 84 rice accessions, GWAS identified 108 loci controlling 13 agronomic traits. While the functions of some loci have been reported, the genetic mechanisms underlying many novel loci remain to be explored. In association analysis, factors like population structure and kinship among samples can lead to false positives, where non-functional loci are identified as significantly associated (Vos et al., 2022). In wheat research, Tian et al. (2025) genotyped 341 wheat accessions using a 40K SNP chip and conducted GWAS combined with phenotype data to identify functional genes controlling protein quality traits. PCA divided the 341 accessions into two distinct subpopulations, indicating significant population structure. Consequently, the study employed a linear mixed model to reduce false positives. Sun et al. (2018) genotyped 713 upland cotton accessions at the seedling stage using the CottonSNP63K chip and performed GWAS combined with their salt tolerance phenotypes to dissect the genetic basis of cotton salt tolerance. The analysis identified 280 potential candidate genes. Subsequent validation confirmed differential expression of six candidate genes between salt-tolerant and salt-sensitive cotton varieties, further supporting their important roles in cotton salt tolerance. Currently, researchers using GWAS have successfully identified functional loci related to crop physiology and environmental adaptability, including key agronomic traits such as plant height, flowering time, stress tolerance, and yield (Cai et al., 2017; Tu et al., 2021; Li et al., 2023; Sahito et al., 2024). These findings lay a crucial foundation for crop genetic improvement, enabling breeders to move beyond traditional phenotype selection and achieve precise design and regulation of crop quality at the genetic level (Yasir et al., 2022). 3.3 Genomic selection After identifying QTLs or major genes controlling important agronomic traits, researchers have raised an intriguing question: Can we accurately predict the post-cultivation phenotype of a crop using only its genotype information obtained before cultivation or at the seedling stage? Consequently, genomic selection (GS) emerged. All statistical and machine learning models that use genome wide genetic markers to calculate genomic estimated breeding values (GEBV) fall under GS tools. These models are highly significant for selecting superior parents, increasing genetic diversity, and shortening breeding cycles, and are widely applied in crops such as wheat, rice, and maize (Kumar et al., 2020; Gebremedhin et al., 2024; Lee et al., 2024). Kang et al. (2023) genotyped diverse 567 Korean (K)-wheat core collection using the Axiom® 35K wheat DNA chip and identified multiple SNPs associated with target agronomic traits through GWAS. They evaluated GS prediction accuracy using six predictive models (G-BLUP, LASSO, BayseA, RKHS, SVM, and random forest) and various training populations, while also conducting a "blind test" on a variety of Korean wheat cultivars with
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