Molecular Pathogens 2024, Vol.15, No.1, 40-49 http://microbescipublisher.com/index.php/mp 44 comprehensive approach allows for the capture of both major and minor gene effects, making it particularly effective for traits controlled by multiple genes. The principle of GS is based on the creation of a training population (TP) that is both phenotyped and genotyped. Statistical models are then trained on this data to predict the performance of untested individuals, thereby accelerating the breeding cycle and increasing genetic gain (Rutkoski et al., 2015). 6.2 Advantages of GS over MAS GS offers several advantages over MAS, particularly in the context of complex traits. GS uses genome-wide markers, capturing the effects of both major and minor genes, which is crucial for traits with a complex genetic architecture (Merrick et al., 2021; 2022). Studies have shown that GS models generally have higher prediction accuracies compared to MAS. For instance, GS models for disease resistance in wheat achieved an accuracy of 0.72, significantly higher than MAS models. By predicting the genetic value of breeding candidates early in the breeding cycle, GS can significantly reduce the time required to develop new varieties. The ability to select superior genotypes more accurately and rapidly leads to higher genetic gains per unit of time. This has been demonstrated in various crops, including maize, rice, and wheat (Bassi et al., 2016). 6.3 Implementing GS in grapevine breeding Implementing GS in grapevine breeding involves several key steps and considerations. A diverse and representative training population must be developed. This population should be both phenotyped for relevant traits and genotyped using high-density markers. Statistical models, such as genomic best linear unbiased prediction (gBLUP) and Bayesian methods, are trained on the TP data. These models are then validated using cross-validation techniques to ensure their accuracy and robustness (Huang et al., 2019). Once the models are validated, they can be used to predict the genetic value of untested individuals. Breeding candidates with the highest predicted values are selected for further breeding or direct use (Cappetta et al., 2020). GS can be combined with other advanced technologies, such as hyperspectral imaging and high-throughput phenotyping, to further enhance selection accuracy and efficiency (Crossa et al., 2017; Merrick et al., 2022). In grapevine breeding, GS has the potential to revolutionize the selection process by enabling the rapid and accurate identification of disease-resistant genotypes. This is particularly important for traits like resistance to Pierce’s disease and dagger nematode, which are controlled by multiple genes (Gaspero and Cattonaro, 2010; Viana et al., 2016). By integrating GS into breeding programs, grape breeders can accelerate the development of new, disease-resistant varieties, ultimately improving grapevine health and productivity. 7 Biotechnological Approaches 7.1 CRISPR/Cas9 and genome editing CRISPR/Cas9 technology has revolutionized the field of plant genetics, offering precise and efficient genome editing capabilities. In grapevine breeding, CRISPR/Cas9 has been employed to enhance disease resistance by targeting specific genes associated with susceptibility to pathogens. For instance, the CRISPR/Cas9 system has been used to confer resistance to grapevine leafroll-associated virus 3 (GLRaV-3) by expressing FnCas9 and LshCas13a, which inhibit the virus through RNA-targeting mechanisms (Jiao et al., 2022). Additionally, DNA-free genome editing using CRISPR/Cas9 ribonucleoprotein complexes has been demonstrated, allowing for the regeneration of edited protoplasts into whole plants without introducing foreign DNA, thus addressing regulatory concerns related to genetically modified organisms (Najafi et al., 2022). Furthermore, CRISPR/Cas9-mediated targeted mutagenesis has been successfully applied to generate biallelic mutations in the first generation of grape plants, enhancing resistance to Botrytis cinerea by knocking out the VvWRKY52 transcription factor gene (Wang et al., 2017). 7.2 Transgenic approaches Transgenic approaches involve the introduction of foreign genes into the grapevine genome to confer disease resistance. These methods have been pivotal in developing grapevine varieties with enhanced resistance to fungal
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