Research Report

Genomic Approaches to Enhance Disease Resistance in Grapevine Breeding Programs  

Jie Huang , Meifang Li
Animal Science Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572024, Hainan, China
Author    Correspondence author
Molecular Pathogens, 2024, Vol. 15, No. 1   doi: 10.5376/mp.2024.15.0005
Received: 29 Dec., 2023    Accepted: 31 Jan., 2024    Published: 20 Feb., 2024
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This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Huang J., and Li M.F., 2024, Genomic approaches to enhance disease resistance in grapevine breeding programs, Molecular Pathogens, 15(1): 40-49 (doi: 10.5376/mp.2024.15.0005)

Abstract

Enhancing disease resistance in grapevine breeding programs is a key strategy for improving grape productivity and quality. This study provides an overview of grapevine diseases and their economic and agricultural impact, discusses traditional methods of disease control and their limitations, and introduces genomic tools and technologies such as high-throughput sequencing, genotyping and single nucleotide polymorphism (SNP) analysis, and genome-wide association studies (GWAS) used in grapevine breeding. It also explores the development and successful cases of marker-assisted selection (MAS) in grapevine breeding, elucidates the principles and advantages of genomic selection (GS), and its application in grapevine breeding. By examining the existing genomic technologies and strategies, this study emphasizes the importance of genomics in enhancing grapevine disease resistance and provides recommendations for future research. These genomic approaches not only offer new perspectives for grapevine breeding but also provide valuable references for disease-resistant breeding in other crops.

Keywords
Disease resistance; Grapevine breeding; Genomic tools; Marker-assisted selection (MAS); Genomic selection (GS)
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