MPB_2024v15n5

Molecular Plant Breeding 2024, Vol.15, No.5, 220-232 http://genbreedpublisher.com/index.php/mpb 227 Nucleotide polymorphisms significantly influence maize crop traits, including stress resistance, yield, and disease resistance. These genetic variations provide valuable resources for breeding programs aimed at developing more resilient and high-yielding maize varieties. By leveraging these polymorphisms, researchers and breeders can enhance maize’s adaptability to various environmental conditions and improve overall crop performance. 6 Application of Nucleotide Polymorphism in Maize Breeding 6.1 Marker-assisted selection Marker-assisted selection (MAS) leverages molecular markers to enhance the efficiency and accuracy of selecting desirable traits in crop breeding. In maize, single nucleotide polymorphisms (SNPs) have become a pivotal tool in MAS due to their abundance and stability. The development of SNP markers has significantly advanced the ability to identify and select for traits such as yield, disease resistance, and stress tolerance. For instance, the use of SNP and SilicoDArT markers has been shown to predict heterosis effects for yield traits in maize, thereby aiding in the selection of superior parental lines for hybrid production (Tomkowiak et al., 2019). Additionally, the application of next-generation sequencing (NGS) technologies, such as genotyping-by-sequencing (GBS), has revolutionized plant genotyping and breeding by providing high-throughput sequences that facilitate the discovery and genotyping of SNPs across large crop genomes like maize (He et al., 2014). This approach has been successfully implemented in genome-wide association studies (GWAS), genetic linkage analysis, and molecular marker discovery, making it an ultimate MAS tool (He et al., 2014). 6.2 Genomic selection techniques Genomic selection (GS) techniques involve the use of genome-wide markers to predict the genetic value of breeding candidates. This method has gained traction in maize breeding due to its ability to enhance the accuracy of selection and reduce the breeding cycle time. Recent studies have demonstrated the efficacy of deep learning models in improving the prediction accuracy of flowering-related traits in maize. These models outperformed traditional Bayesian models, showing a 14.4% increase in prediction accuracy when employing multi-trait models compared to single-trait approaches (Mora-Poblete et al., 2023). The integration of SNP markers in GS has also been facilitated by the development of high-quality SNP markers that meet stringent criteria for polymorphism and genetic stability (Mammadov et al., 2010). These markers are essential for the accurate prediction of quantitative traits and the identification of key genomic regions associated with agronomic traits (Mora-Poblete et al., 2023). Moreover, the use of SNP arrays in GS has proven to be a robust and cost-effective tool for generating high-throughput genotype data, which is crucial for the selection of superior genotypes in large breeding populations (Weber et al., 2023). 6.3 Association analysis of SNPs with breeding targets Association analysis, particularly genome-wide association studies (GWAS), has been instrumental in identifying SNPs linked to important breeding targets in maize. This method involves scanning the genome to find associations between SNPs and phenotypic traits, thereby uncovering the genetic basis of complex traits. For example, a study on maize kernel size traits identified 21 SNPs significantly associated with kernel length, width, and thickness, providing insights into the genetic architecture of these yield-related traits (Liu et al., 2019). Similarly, association analyses of SNPs in candidate genes have revealed polymorphisms linked to root traits in maize seedlings, which are crucial for improving nutrient uptake and plant growth under nitrogen-deficient conditions (Kumar et al., 2014). These findings highlight the potential of SNP-based association analysis in enhancing the selection of maize lines with desirable traits. The application of nucleotide polymorphism in maize breeding through MAS, GS, and association analysis has significantly advanced the field. The development and utilization of SNP markers have enabled more precise and efficient selection of breeding targets, ultimately contributing to the improvement of maize varieties with enhanced yield, stress tolerance, and other agronomic traits (Mammadov et al., 2010; He et al., 2014; Kumar et al., 2014; Liu et al., 2019; Tomkowiak et al., 2019; Mora-Poblete et al., 2023; Weber et al., 2023).

RkJQdWJsaXNoZXIy MjQ4ODYzMg==