Molecular Pathogens 2024, Vol.15, No.5, 219-226 http://microbescipublisher.com/index.php/mp 220 varieties to accumulate favorable genes that confer resistance to specific pathogens. The process typically starts with the identification of resistant cultivars, which are then crossed with local cultivars to introduce resistance genes into the breeding pool. Sources of resistance often include landraces, related species, mutations, and wild relatives (Khan et al., 2020). The breeding programs focus on two types of resistance: vertical resistance, controlled by major genes, and horizontal resistance, controlled by minor genes. Vertical resistance tends to be more specific and can be overcome by pathogen evolution, whereas horizontal resistance offers broader, albeit often weaker, protection (Jiménez et al., 2022). Despite the challenges, conventional breeding remains a cost-effective and environmentally friendly approach to managing plant diseases. 2.2 Limitations of Traditional Breeding in Addressing Disease Resistance While conventional breeding techniques have been instrumental in developing disease-resistant strawberry cultivars, they come with several limitations. One significant challenge is the rapid evolution of phytopathogens, which can quickly overcome the resistance bred into the plants. This dynamic nature of pathogen evolution makes it difficult to achieve long-lasting resistance (Fu, 2023). The genetic gains in breeding for resistance to certain diseases, such as Verticillium wilt and Phytophthora crown rot, have been negligible over the past decades. Studies have shown that a large proportion of the genetic resources preserved in public germplasm collections are moderately to highly susceptible to these diseases, indicating that traditional breeding methods have not been sufficiently effective (Feldmann et al., 2023). The heritability of resistance traits is often low to moderate, further complicating the breeding process (Pincot et al., 2020). 3 New Methods in Disease-Resistant Strawberry Breeding 3.1 Marker-assisted selection (MAS) Marker-assisted selection (MAS) is a method that utilizes molecular markers to assist in the selection of desirable traits, such as disease resistance, in plant breeding. This technique has been particularly effective in cases where disease resistance is controlled by one or a few genes with a large effect on the phenotype. MAS can significantly increase the efficiency of breeding programs by allowing for the precise targeting of these genes, thereby accelerating the development of disease-resistant varieties (He et al., 2014; Collins et al., 2018). For instance, genotyping-by-sequencing (GBS) has been developed as an advanced MAS tool, combining molecular marker discovery and genotyping to facilitate genome-wide association studies and genomic selection (Figure 1) (Merrick et al., 2021). In strawberries, MAS can be used to identify and select for genes associated with resistance to common diseases, thereby improving the overall resilience of the crop. 3.2 Genome editing (CRISPR-Cas9) Genome editing, particularly using the CRISPR-Cas9 system, has revolutionized the field of plant breeding by enabling precise and targeted modifications to the genome. This technology allows for the direct alteration of genes associated with disease resistance, thereby creating transgene-free, disease-resistant plant varieties (Chen, 2024). CRISPR-Cas9 has been successfully applied to develop disease-resistant crops by knocking out susceptibility genes or introducing resistance genes. The precision and efficiency of CRISPR-Cas9 make it a powerful tool for strawberry breeding, where it can be used to enhance resistance to pathogens that significantly impact yield and quality. The ability to make specific genetic changes without introducing foreign DNA is particularly advantageous for meeting regulatory requirements and consumer acceptance (Ahmad et al., 2020). 3.3 Genomic selection (GS) Genomic selection (GS) is an advanced breeding method that uses genome-wide markers to predict the breeding value of individuals, thereby facilitating the selection of superior genotypes. Unlike MAS, which focuses on a few specific markers, GS considers the entire genome, making it more effective for traits controlled by multiple genes with small effects (Merrick et al., 2022). GS has shown high predictive accuracy for quantitative disease
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