Tree Genetics and Molecular Breeding 2024, Vol.14, No.4, 206-217 http://genbreedpublisher.com/index.php/tgmb 208 possibilities for grapevine improvement (Gaspero and Cattonaro, 2010). Additionally, the use of NGS in conjunction with CRISPR/Cas genome editing has accelerated the genetic improvement of grapevine by enabling precise modifications of specific genes (Ren et al., 2024). 3.3 Integration of transcriptomics, proteomics, and metabolomics The integration of transcriptomics, proteomics, and metabolomics has provided a comprehensive understanding of grapevine biology, linking gene expression to phenotypic traits. Transcriptomics allows for the analysis of gene expression patterns, while proteomics and metabolomics provide insights into protein functions and metabolic pathways, respectively (Berger et al., 2022). This multi-omics approach is essential for functional genomics, as it helps to elucidate the regulatory networks governing important traits such as stress tolerance and fruit quality (Vidal et al., 2010). The ability to extract high-quality DNA and RNA from grapevine tissues has further facilitated the application of these technologies, enabling the analysis of "omics" data from a single plant sample (Berger et al., 2022). By combining these approaches, researchers can gain a holistic view of grapevine physiology and adaptation, ultimately leading to the development of improved grapevine varieties (Gray et al., 2014; Zhang et al., 2021). 4 Identification of Key Traits in Grapevine Varieties 4.1 Traits affecting wine quality Grapevine varieties are selected for wine production based on several key traits that significantly influence wine quality, including flavor, aroma, and phenolic content. The metabolomic profiling of novel grapevine genotypes has shown that these traits can be enhanced through breeding cycles aimed at improving climate adaptation and berry characteristics. For instance, wines from these genotypes exhibited increased polyphenol content, such as anthocyanins, which are crucial for color and mouthfeel attributes like astringency and body (Gómez et al., 2024). Additionally, the identification of stable quantitative trait loci (QTLs) for traits like Muscat flavor and berry firmness further underscores the genetic basis for these quality attributes, allowing for more targeted breeding strategies (Wang et al., 2020). The integration of genomic selection with polyploidization techniques also offers a promising avenue for developing grapevine varieties with superior wine quality traits by combining desirable genetic combinations (Bharati et al., 2023). 4.2 Resistance to biotic stresses Resistance to biotic stresses, such as pests and diseases, is a critical trait for grapevine varieties used in wine production. Traditional Vitis vinifera varieties are often susceptible to these stresses, necessitating the development of resistant varieties through inter-specific hybridization and breeding programs. These efforts have led to the creation of grapevine varieties that can withstand pests and diseases without compromising organoleptic qualities (Teissèdre, 2018). Moreover, genomic studies have identified gene clusters associated with disease resistance, providing a genetic framework for breeding programs to enhance biotic stress resistance in grapevines (Figure 1) (Shi et al., 2023). The use of genomic prediction methods has further facilitated the identification of resistant individuals, accelerating the breeding process and improving the efficiency of developing biotic stress-resistant grapevine varieties (Brault et al., 2024). 4.3 Adaptation to abiotic stresses Adaptation to abiotic stresses, such as drought and temperature changes, is increasingly important for grapevine varieties due to the impacts of climate change. Breeding programs have focused on developing genotypes that can thrive in warm climates, as demonstrated by the successful adaptation of novel grapevine genotypes with improved climate resilience (Gómez et al., 2024). The identification of genes linked to fruit set-related traits, which are influenced by environmental conditions, highlights the complex genetic architecture underlying abiotic stress adaptation (Zinelabidine et al., 2021). Additionally, the integration of genomic selection with omics data allows for the prediction and selection of genotypes with enhanced abiotic stress tolerance, thereby supporting the development of grapevine varieties that can maintain high yields and quality under challenging environmental conditions (Bharati et al., 2023). The comprehensive understanding of these genetic mechanisms is crucial for optimizing grapevine production in the face of climate variability.
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