Maize Genomics and Genetics 2025, Vol.16, No.3, 119-128 http://cropscipublisher.com/index.php/mgg 124 associated with pollen viability found in previous studies. In addition, heat-sensitive QTL hotspots associated with leaf burn severity and plant height changes were also found on chromosome 9 (Inghelandt et al., 2019), further enriching the genetic basis map of maize heat tolerance traits. 4.2 Core germplasm pool and allele mining based on molecular markers Molecular marker technology provides a powerful tool for the efficient utilization and genetic improvement of crop germplasm resources. Through molecular marker-assisted analysis, researchers can systematically and accurately screen representative core germplasm subsets from a large number of breeding materials and germplasm resource pools to maximize the retention of genetic diversity. These core germplasm pools are not only of great value in germplasm resource management, but also provide a basis for subsequent allele mining and target trait improvement (Naveed et al., 2016). In recent years, several international projects, including the Global Crop Generation Challenge Program (GCP), have established micro-core germplasm pools for a number of major food crops, and combined molecular and phenotypic methods to conduct in-depth analysis, aiming to discover new functional genes and genetic variations with potential application value. In corn, based on high-throughput molecular markers and whole genome resequencing technology, researchers have identified a large number of SNP sites and haplotypes associated with key agronomic traits. These genomic information provides data support for gene positioning, allele mining and subsequent molecular breeding. For example, association maps and genome-wide association analysis (GWAS) methods can not only locate QTL regions associated with target traits, but also further identify excellent alleles with breeding potential (Nelimor et al., 2019). By analyzing the "haplotype" structure, single feature polymorphism (SFP) and near alleles (NIP) between different inbred lines, we can have a deeper understanding of the functional diversity and evolutionary laws of the maize genome. At present, allele mining is mainly determined by genome resequencing and ecological planting screening, and gene-based resequencing methods are more widely used in maize. Through whole genome genotyping, not only can sequence variations be detected, but also functional association analysis can be performed in combination with phenotypic data to determine whether new alleles have beneficial effects on specific traits (Driedonks et al., 2016). Studies have also shown that the function and application value of alleles can be further confirmed through marker-assisted backcrossing (MABC), genetic transformation, transient expression analysis and association verification between different germplasms. 4.3 Challenges and prospects of integrating genomic data into breeding programs Although genomic data has broad application prospects in breeding, there are still many challenges in the actual integration process, such as the high demand for large-scale, accurate phenotypic data to support the population, and the need for extensive testing under multiple environmental conditions (Miedaner et al., 2020). In addition, heat stress tolerance, as a typical quantitative trait, is greatly affected by the interaction between genotype and environment, which also increases the difficulty of accurately predicting breeding values. However, with the continuous advancement of molecular tools and technologies, such as the development of low-cost marker detection technology and in-depth mining of candidate genes, these problems are gradually being overcome (Jagtap et al., 2020; Tyagi et al., 2021). Future research should focus on optimizing genomic selection models and strengthening multi-omics data integration to further improve the accuracy and efficiency of heat-tolerant maize breeding programs. 5 Case Studies 5.1 CIMMYT and GWAS studies promote progress in heat-tolerant maize breeding Against the backdrop of increasingly severe global climate change, maize yield stability under heat stress has become a focus of breeding research. The International Maize and Wheat Improvement Center (CIMMYT), in collaboration with Purdue University and partners from the South Asian National Agricultural Research System (NARS), conducted a large-scale genome-wide association study (GWAS) to analyze the genetic basis of maize yield and related traits under heat stress. The study included more than 500 testcross lines of maize lines with different genetic backgrounds, aiming to identify key genomic regions associated with heat tolerance traits.
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