Molecular Plant Breeding 2024, Vol.15, No.6, 340-350 http://genbreedpublisher.com/index.php/mpb 345 6.3 Using SNPs and QTL data to accelerate breeding programs The integration of SNP and QTL data is crucial for accelerating maize breeding programs aimed at improving resistance to ear rot diseases. By combining GWAS and QTL mapping, researchers can validate significant loci and identify candidate genes for resistance (Leprévost et al., 2023). For instance, the integration of GWAS and QTL mapping in a study identified eight colocated loci on chromosomes 2, 3, 4, 5, 9, and 10, which are associated with FER resistance (Chen et al., 2016). Similarly, another study using a combination of QTL mapping and GradedPool-Seq identified significant SNPs and candidate genes for GER resistance (Yuan et al., 2022). These integrated approaches enable the development of molecular markers that can be used in MAS and GS to enhance the efficiency of breeding programs (Wu et al., 2020; Akohoue and Miedaner, 2022). 7 Improving High Yield and Disease Resistance in Maize 7.1 The relationship between disease resistance and yield potential The relationship between disease resistance and yield potential in maize is complex and multifaceted. Fusarium ear rot (FER) and Gibberella ear rot (GER) are two significant diseases that not only reduce yield but also affect grain quality through mycotoxin contamination (Ding et al., 2008; Zhou et al., 2021). The genetic improvement of maize for resistance to these diseases involves identifying stable quantitative trait loci (QTL) that can be used in breeding programs to enhance both yield and disease resistance (Gaikpa and Miedaner, 2019; Akohoue and Miedaner, 2022). Studies have shown that resistance to these diseases is often controlled by multiple minor-effect QTLs, which collectively contribute to the overall resistance (Chen et al., 2016; Zhou et al., 2021). This polygenic nature of resistance implies that improving disease resistance can be achieved without necessarily compromising yield potential, provided that the breeding strategies are well-designed to balance both traits (Lanubile et al., 2017; Miedaner et al., 2020). 7.2 Strategies for breeding maize varieties with high yield and disease resistance Several strategies have been employed to breed maize varieties that combine high yield and disease resistance. One effective approach is the use of genomics-assisted breeding, which integrates QTL mapping, genome-wide association studies (GWAS), and genomic selection to identify and select for resistance genes (Gaikpa and Miedaner, 2019; Miedaner et al., 2020; Yuan et al., 2022). For instance, meta-analysis and co-expression analysis have been used to identify stable QTL and candidate genes that confer resistance to both FER and GER, which can then be incorporated into elite cultivars through backcross breeding strategies (Akohoue and Miedaner, 2022). Additionally, the use of multi-parent QTL mapping has revealed stable QTL that can be utilized to enhance GER resistance in different maize germplasms (Galiano-Carneiro et al., 2020). Marker-assisted selection (MAS) is another strategy that facilitates the selection of resistance traits by using closely linked markers to major resistance QTL, thereby improving the efficiency of breeding programs (Ding et al., 2008; Giomi et al., 2021). 7.3 Challenges in combining yield and resistance traits in breeding Combining yield and resistance traits in maize breeding presents several challenges. One major challenge is the genetic complexity of disease resistance, which is often controlled by multiple QTL with small effects, making it difficult to achieve significant improvements through traditional breeding methods alone (Chen et al., 2016). Additionally, the interactions between different QTL and the environment can complicate the selection process, as resistance traits may not consistently express across different environments (Ding et al., 2008; Zhou et al., 2021). Another challenge is the potential trade-off between yield and resistance, where selecting for one trait may inadvertently affect the other. For example, some QTL associated with disease resistance may have pleiotropic effects that influence yield-related traits (Giomi et al., 2021). To overcome these challenges, a combination of advanced genomic tools, such as genomic selection and integrated breeding strategies, is necessary to accurately predict and select for genotypes that exhibit both high yield and strong disease resistance (Lanubile et al., 2017; Gaikpa and Miedaner, 2019; Miedaner et al., 2020). 8 Case Study: Successful QTL Mapping and Resistance Breeding in Maize 8.1 Background and significance of the breeding program Fusarium ear rot (FER) and Gibberella ear rot (GER) are significant diseases affecting maize, leading to reduced
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