Molecular Pathogens 2024, Vol.15, No.1, 40-49 http://microbescipublisher.com/index.php/mp 43 4.3 Candidate gene validation Validating candidate genes is crucial to confirm their role in disease resistance. This can be achieved through various methods such as marker-assisted selection, gene knockout, and overexpression studies. For instance, the REN11 locus fromVitis aestivalis was validated in a pseudo-testcross with the grandparent source of resistance, demonstrating its effectiveness in nearly all vineyard environments (Karn et al., 2021). Another study utilized positional cloning to map and clone disease resistance genes from the wild North American grape species Muscadinia rotundifolia, providing sequence information that can be used to design perfect genetic markers for marker-assisted selection. Additionally, genetic linkage maps displaying the chromosomal locations of microsatellite markers and R-gene candidates have been constructed, aiding in the identification of markers linked to genetic determinants of disease resistance. 5 Marker-Assisted Selection (MAS) in Grapevine Breeding 5.1 Development of molecular markers The development of molecular markers is a critical step in the implementation of marker-assisted selection (MAS) in grapevine breeding. Molecular markers such as single nucleotide polymorphisms (SNPs) and microsatellite markers are commonly used due to their high polymorphism and reproducibility. Next-generation sequencing (NGS) technologies have significantly advanced the discovery of these markers, enabling high-throughput genotyping and the identification of trait-associated markers. For instance, the AmpSeq platform has been utilized to develop a MAS package for grapevine, targeting traits such as disease resistance and acylated anthocyanins (Yang et al., 2016). The genotyping-by-sequencing (GBS) approach has been employed to discover and genotype SNPs in crop genomes, providing a cost-effective and efficient tool for MAS (He et al., 2014). 5.2 Integration of MAS in breeding programs Integrating MAS into grapevine breeding programs involves several steps, including the identification of trait-associated markers, genotyping of breeding populations, and selection of individuals carrying desirable alleles. MAS allows for the selection of traits at the seedling stage, thereby accelerating the breeding process and reducing costs. The use of MAS is particularly advantageous for traits controlled by single genes or major quantitative trait loci (QTLs) with large effects. For example, MAS has been successfully integrated into breeding programs for disease resistance in various crops, including wheat and barley, by targeting specific resistance genes and QTLs (Collins et al., 2018). The integration of MAS in grapevine breeding can similarly enhance the efficiency of selecting disease-resistant varieties. 5.3 Case studies of MAS success Several case studies highlight the success of MAS in breeding programs. In wheat, MAS has been effectively used to incorporate resistance genes such as Lr34 and Yr36 for rust resistance, and Fhb1 for Fusarium head blight resistance (Miedaner and Korzun, 2012; Arruda et al., 2016). In lupin, a co-dominant, sequence-specific marker linked to anthracnose resistance was developed and successfully implemented in the Australian lupin breeding program. In grapevine, the AmpSeq platform has been employed to develop a MAS package for traits including disease resistance, demonstrating the potential of MAS to enhance grapevine breeding (Yang et al., 2016). These examples underscore the utility of MAS in improving disease resistance and other agronomically important traits in various crops. By leveraging molecular markers and integrating MAS into breeding programs, grapevine breeders can achieve more precise and efficient selection of disease-resistant varieties, ultimately enhancing the sustainability and productivity of grapevine cultivation. 6 Genomic Selection (GS) and Its Applications 6.1 Principles of genomic selection Genomic Selection (GS) is a modern breeding approach that leverages genome-wide markers to predict the genetic value of breeding candidates. Unlike traditional marker-assisted selection (MAS), which focuses on a few significant markers, GS considers the effects of all markers across the genome simultaneously. This
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