Molecular Pathogens 2024, Vol.15, No.5, 219-226 http://microbescipublisher.com/index.php/mp 223 5 Prospects of Integrated Breeding Approaches 5.1 Combining conventional and modern breeding techniques Combining conventional breeding techniques with modern advancements offers a promising pathway to enhance disease resistance in strawberries. Traditional methods, while foundational, often fall short in efficiency and precision. Integrating high-throughput phenotyping (HTP) with genomic information, for instance, has shown significant improvements in selection accuracy for traits like powdery mildew resistance in strawberries. This approach leverages spectral analysis of canopy reflectance and genomic markers, resulting in up to a two-fold increase in predictive ability over models using markers alone (Tapia et al., 2022). The integration of speed breeding with AI and genomics-assisted breeding (GAB) can drastically reduce the breeding cycle time, making it possible to develop new cultivars more quickly and accurately (Bhat et al., 2023). 5.2 Use of multi-omics approaches for enhanced breeding efficiency The integration of multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—has revolutionized plant breeding by providing a comprehensive understanding of the biological processes underlying desirable traits. This holistic view allows for the precise assembly of desired alleles using genome editing techniques, thereby enhancing breeding strategies for climate-resilient and nutrient-sufficient crops (Mahmood et al., 2022). The integration of omics databases is crucial for this process, as it enables the comprehensive analysis of complex traits and their interactions, ultimately improving the efficiency of crop breeding (Chao et al., 2023). By leveraging these advanced techniques, breeders can develop strawberry varieties with improved disease resistance and other beneficial traits more effectively (Langridge and Fleury, 2011). 5.3 The potential of big data and AI in strawberry breeding The advent of big data and artificial intelligence (AI) has opened new avenues for plant breeding. Machine learning (ML) algorithms, in particular, have shown great promise in analyzing vast amounts of complex data generated by high-throughput omics technologies. These tools can identify key genetic markers and predict breeding outcomes with high accuracy, thereby accelerating the development of new plant varieties (Najafabadi et al., 2023). AI techniques also enable the capture of non-linear and epistatic interactions in genome-wide association studies (GWAS) and genomic selection (GS), making these variations available for genomics-assisted breeding. The integration of big data and AI in strawberry breeding can thus significantly enhance the efficiency and precision of developing disease-resistant cultivars. 6 Challenges in Disease-Resistant Strawberry Breeding 6.1 Genetic complexity of disease resistance The genetic complexity of disease resistance in strawberries poses a significant challenge for breeders. The octoploid nature of the strawberry genome complicates the identification and introgression of resistance genes. Recent studies have highlighted the intricate genetic architecture of resistance traits, which often involve multiple loci and complex interactions between genes. For instance, the development of resistance to Phytophthora crown rot has been hindered by the quantitative nature of the resistance phenotypes and their moderate heritability (Mangandi et al., 2017; Jiménez et al., 2022). Additionally, the genetic background can significantly influence the expression and durability of resistance traits, as seen in the resistance to plant viruses (Gallois et al., 2018). 6.2 Environmental influences on disease resistance Environmental factors play a crucial role in the effectiveness of disease resistance in strawberries. The interaction between genotype and environment can lead to variability in resistance levels, making it challenging to develop universally resistant cultivars. For example, genotype-environment interactions were observed in the resistance to Colletotrichum gloeosporioides, with different transplant types showing varying levels of genetic control across locations (Osorio et al., 2014). Similarly, the spread of pests and pathogens due to climate change can alter the effectiveness of resistance traits, necessitating continuous adaptation in breeding programs (Súnico et al., 2021).
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