Maize Genomics and Genetics 2024, Vol.15, No.3, 111-122 http://cropscipublisher.com/index.php/mgg 116 Furthermore, genomic studies have identified SNPs associated with resistance to multiple diseases, including maize lethal necrosis, gray leaf spot, and turcicum leaf blight, facilitating the development of disease-resistant maize lines (Sadessa et al., 2022). 4.3 Nutritional quality improvement Improving the nutritional quality of maize is another critical application of genomics-assisted breeding. Advances in genomic technologies have enabled the identification and manipulation of alleles associated with higher nutritional value. For instance, genomic breeding strategies aim to optimize crop genomes by accumulating beneficial alleles and purging deleterious ones, thereby enhancing the nutritional profile of maize cultivars (Varshney et al., 2021). This approach is expected to play a crucial role in breeding climate-smart crops with higher nutritional value in a cost-effective and timely manner. 4.4 Yield enhancement Yield enhancement remains a primary goal in maize breeding. Genomics-assisted breeding has significantly contributed to this objective by identifying genomic regions associated with yield-related traits and developing high-yielding maize varieties. For example, a study on genomic prediction models demonstrated that effective genomic prediction of hybrid performance could be achieved with a small training set, enabling efficient exploration of genetic combinations for yield improvement (Guo et al., 2019). Additionally, QTL analysis has identified key chromosome regions associated with yield traits under various environmental conditions, supporting marker-assisted selection for yield enhancement (Hu et al., 2020). 4.5 Hybrid breeding Hybrid breeding has benefited immensely from genomics-assisted approaches. The integration of genomic selection and other advanced breeding techniques has optimized the prediction of hybrid performance, leading to the development of superior hybrid maize varieties. For instance, genomic prediction models have been validated in multiple crops, including maize, demonstrating their effectiveness in predicting hybrid performance and facilitating the breeding of high-yielding hybrids (Guo et al., 2019) (Figure 3). Moreover, the identification of genomic regions associated with agronomic traits and disease resistance has further supported the development of robust hybrid maize lines (Sadessa et al., 2022). Guo et al. (2019) explores the genomic relationships and phenotypic variations in maize by examining 24 inbred lines and 276 hybrids. Using principal component analysis (PCA) and hierarchical clustering, the research identifies distinct separation patterns between temperate and mixed (TM) and tropical and subtropical (TS) germplasm. The phenotypic evaluation of hybrids for traits like flowering time, ear height, and grain yield shows that inter-group hybrids (TM×TS) generally exhibit intermediate values for flowering time and ear height but display higher grain yield, indicating a heterosis effect. The variance component analysis reveals that additive genetic variance is more influential for flowering time and ear height, while dominance variance plays a larger role in grain yield. These findings highlight the potential of leveraging genomic diversity and specific hybrid combinations to enhance desirable traits in maize breeding programs. In summary, genomics-assisted breeding has revolutionized maize breeding by providing advanced tools and techniques to address critical challenges such as drought and heat tolerance, disease resistance, nutritional quality improvement, yield enhancement, and hybrid breeding. The integration of genomic data and breeding strategies continues to accelerate the development of superior maize varieties, ensuring sustainable agricultural practices and food security. 5 Outcomes and Impact 5.1 Success stories in maize breeding Genomics-assisted breeding (GAB) has led to significant advancements in maize breeding, particularly in developing disease-resistant and high-yielding varieties. For instance, the integration of genomic data has facilitated the breeding of maize varieties resistant to Gibberella ear rot (GER) and Fusarium ear rot (FER), which are major threats to maize production (Miedaner et al., 2020). Additionally, the use of genomic selection has enabled the prediction and selection of superior hybrids, significantly enhancing breeding efficiency and outcomes
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