Molecular Plant Breeding 2025, Vol.16, No.1, 24-34 http://genbreedpublisher.com/index.php/mpb 29 Figure 2 A generalized procedure of genome-wide association study (Adopted from Shikha et al., 2021) 7.2 Integration of multi-omics approaches for comprehensive trait analysis The integration of multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, offers a comprehensive framework for trait analysis in maize. By combining data from various omics layers, researchers can gain a holistic understanding of the molecular mechanisms underlying complex traits. For example, the use of RNA, protein, and metabolite-based omics studies in GWAS has been discussed as a means to uncover hidden causes of phenotypic variation (Xiao et al., 2017). This integrative approach allows for the identification of key regulatory networks and pathways that contribute to trait expression. Moreover, the combination of QTL mapping and GWAS has been shown to enhance the resolution of trait mapping, as demonstrated by the identification of significant SNPs and candidate genes for yield-related traits in maize (Zhang et al., 2020). Such integrative strategies are crucial for advancing our understanding of trait architecture and improving the efficiency of molecular breeding. 7.3 Future prospects of genomic technologies in maize breeding The future of genomic technologies in maize breeding looks promising, with several advancements on the horizon. The continuous development of high-density SNP arrays and next-generation sequencing technologies will further enhance the resolution and accuracy of GWAS, enabling the identification of novel loci associated with important agronomic traits (Ganal et al., 2011; Xiao et al., 2017). Additionally, the adoption of machine learning methods for dimensionality reduction and the use of advanced statistical models will improve the prediction accuracy of genomic selection (Susmitha et al., 2023). The integration of multi-omics data will also play a pivotal role in elucidating the complex genetic networks that govern trait expression, paving the way for more targeted and efficient breeding strategies. Furthermore, the use of joint-GWAS approaches, which combine data from multiple populations, will increase the statistical power of trait mapping and facilitate the discovery of rare and common genetic variants (Müller et al., 2018). These advancements will ultimately contribute to the development of maize varieties with enhanced yield, quality, and stress tolerance, addressing the global challenges of food security and climate change (Huang et al., 2010; Shikha et al., 2021). 8 Case Study: Breeding Mechanization-Friendly Maize Varieties 8.1 Overview of a successful breeding program focused on mechanization A successful breeding program aimed at developing mechanization-friendly maize varieties involves the integration of advanced genetic mapping techniques and marker-assisted selection (MAS). One such program utilized high-density linkage maps and genome-wide association studies (GWAS) to identify quantitative trait loci (QTL) associated with key traits such as kernel size, yield, and root architecture. For instance, the use of genotyping-by-sequencing (GBS) technology enabled the discovery of over 29 000 high-quality SNPs, which were instrumental in constructing a detailed genetic linkage map and identifying 28 QTLs associated with yield traits (Su et al., 2017). Additionally, the program focused on root architectural traits, which are crucial for improving water and nutrient use efficiency, thereby enhancing plant adaptation under suboptimal conditions (Figure 3) (Moussa et al., 2021).
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