학술논문
Enhancing grapevine breeding efficiency through genomic prediction and selection index.
Document Type
Academic Journal
Author
Brault C; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Segura V; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Roques M; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Lamblin P; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Bouckenooghe V; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Pouzalgues N; Centre du Rosé, Vidauban 83550, France.; Cunty C; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Centre du Rosé, Vidauban 83550, France.; Breil M; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Frouin M; Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France.; Garcin L; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France.; Camps L; Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France.; Ducasse MA; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Romieu C; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Masson G; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.; Centre du Rosé, Vidauban 83550, France.; Julliard S; Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France.; Flutre T; INRAE, CNRS, AgroParisTech, Université Paris-Saclay, GQE-Le Moulon, Gif-sur-Yvette 91190, France.; Le Cunff L; UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France.; Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France.
Source
Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Print Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE
Subject
Language
English
Abstract
Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20-25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.
Competing Interests: Conflicts of interest. The author(s) declare no conflicts of interest.
(© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)
Competing Interests: Conflicts of interest. The author(s) declare no conflicts of interest.
(© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)