Molecular Plant Breeding 2025, Vol.16, No.1, 24-34 http://genbreedpublisher.com/index.php/mpb 30 Figure 3 Distribution frequencies of the seven seedling root traits in the GWAS population across three stages (Adopted from Moussa et al., 2021) Image caption: A, B, and C represent the results from V1 (in turquoise color), V2 (in red color), and V3 (in blue color) stages, respectively). RDW = root dry weight; RDW/SDW = root per shoot dry weight; TRL = total root length; SUA = surface area; ARD = average root diameter; ROV = root volume; RBN = root branching number (Adopted from Moussa et al., 2021) 8.2 Key outcomes and benefits of the developed varieties The breeding program led to the development of maize varieties with several mechanization-friendly traits. The identification of QTLs associated with yield traits such as kernel weight, ear length, and grain weight per plant facilitated the development of high-yielding maize varieties. For example, QTLs identified by the composite interval mapping (CIM) method accounted for 6.4% to 19.7% of the phenotypic variation in yield traits (Su et al., 2017). The program successfully identified significant SNPs associated with root traits, which are essential for efficient water and nutrient uptake. This led to the development of maize varieties with robust root systems, enhancing their adaptability to mechanized farming practices (Moussa et al., 2021). The integration of meta-QTL analysis revealed breeding-friendly MQTLs that were associated with both quality and yield traits. These MQTLs were further validated and recommended for use in future breeding programs to develop biofortified, high-yielding maize varieties (Sethi et al., 2023). 8.3 Lessons learned and implications for future research Several lessons were learned from this breeding program, which have important implications for future research. The use of high-resolution genetic mapping and GWAS proved to be highly effective in identifying key QTLs and SNPs associated with mechanization-friendly traits. Future research should continue to leverage these advanced tools to uncover the genetic basis of other important traits (Su et al., 2017; Liu et al., 2019). The coordination of multiple targets within a single breeding program is complex but essential. The successful identification of MQTLs that influence both yield and quality traits highlights the importance of multi-trait breeding approaches (Sethi et al., 2023). The functional verification of candidate genes associated with key traits is crucial for ensuring their effectiveness in breeding programs. Future research should prioritize the validation of identified genes through various approaches such as marker-assisted breeding, genetic engineering, and genome editing (Moussa et al., 2021). By incorporating these lessons, future breeding programs can continue to develop maize varieties that are well-suited for mechanized farming, ultimately contributing to increased agricultural productivity and sustainability. 9 Future Directions for Research and Breeding 9.1 Emerging trends in mechanization-friendly trait research Recent advancements in high-throughput sequencing technologies and SNP marker development have significantly enhanced our ability to fine-map quantitative trait loci (QTL) and understand the genetic basis of mechanization-friendly traits in maize. The development of high-density polymorphic KASP SNP markers, as
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