Molecular Plant Breeding 2025, Vol.16, No.1, 24-34 http://genbreedpublisher.com/index.php/mpb 31 demonstrated by the identification of 71 311 KASP SNP markers from RNA-Seq data, provides a valuable resource for map-based cloning and marker-assisted selection (MAS) in maize breeding (Chen et al., 2021). Additionally, the use of genotyping-by-sequencing (GBS) technology has facilitated the construction of high-resolution linkage maps and the identification of numerous QTLs associated with yield traits, which are crucial for mechanization (Su et al., 2017). The integration of genome-wide association studies (GWAS) with advanced statistical models, such as the Anderson-Darling test, has further refined our understanding of the genetic architecture of complex traits, including those relevant to mechanization (Yang et al., 2014). 9.2 Potential for collaboration between breeders and technologists The intersection of plant breeding and technological innovation presents a unique opportunity for enhancing mechanization in maize production. Collaboration between breeders and technologists can lead to the development of automated genotyping platforms, such as the KASP SNP markers, which enable high-throughput and accurate genotyping (Chen et al., 2021). Furthermore, the comprehensive genotyping of large maize germplasm collections using GBS technology underscores the potential for integrating diverse genetic resources into breeding programs (Romay et al., 2013). By leveraging the expertise of technologists in next-generation sequencing and data analysis, breeders can more effectively identify and select for traits that improve mechanization, such as plant architecture traits that facilitate high-density planting and mechanical harvesting (Ledesma et al., 2023). 9.3 Recommendations for enhancing mechanization in maize production To enhance mechanization in maize production, several key recommendations can be made based on recent research findings. Utilize high-density SNP markers, such as those developed through KASP and GBS technologies, to fine-map QTLs and identify candidate genes associated with mechanization-friendly traits (Su et al., 2017; Chen et al., 2021). Employ GWAS combined with advanced statistical models, like the Anderson-Darling test, to dissect the genetic architecture of complex traits and improve the accuracy of trait selection (Yang et al., 2014). Incorporate diverse genetic resources from global germplasm collections into breeding programs to enhance genetic diversity and identify novel alleles that contribute to mechanization-friendly traits (Romay et al., 2013). Prioritize the selection of plant architecture traits that facilitate mechanization, such as reduced ear height, upright leaf angles, and optimized tassel branch number, which have been shown to improve adaptation to high planting density and mechanical harvesting (Ledesma et al., 2023). Encourage collaboration between plant breeders, geneticists, and technologists to develop and implement innovative breeding strategies and automated genotyping platforms that streamline the selection process for mechanization-friendly traits (Romay et al., 2013; Chen et al., 2021). By following these recommendations, maize breeding programs can more effectively develop varieties that are well-suited for mechanized agriculture, ultimately improving efficiency and productivity in maize production. 10 Concluding Remarks This study has highlighted significant advancements in the genetic mapping of mechanization-friendly traits in maize using SNP markers. Key findings include the identification of numerous quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs) associated with yield-related traits, kernel size, and other agronomic characteristics. For instance, meta-QTL analysis revealed 68 MQTLs across different genetic backgrounds and environments, with ten breeding-friendly MQTLs (BF-MQTLs) showing significant phenotypic variation and potential for future breeding programs. Additionally, combined linkage and association mapping identified 73 candidate genes regulating seed development, providing insights into the genetic architecture of kernel size traits. High-density linkage maps constructed using genotyping-by-sequencing (GBS) technology have further facilitated the identification of QTLs associated with yield traits, enhancing our understanding of the molecular basis of phenotypic variation. Genome-wide association studies (GWAS) have also been instrumental in identifying genomic regions associated with abiotic and biotic stress tolerance, improving the efficiency of marker-assisted selection in maize breeding.
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