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Brault C, Segura V, Roques M, Lamblin P, Bouckenooghe V, Pouzalgues N, Cunty C, Breil M, Frouin M, Garcin L, Camps L, Ducasse MA, Romieu C, Masson G, Julliard S, Flutre T, Le Cunff L. Enhancing grapevine breeding efficiency through genomic prediction and selection index. G3 (BETHESDA, MD.) 2024; 14:jkae038. [PMID: 38401528 PMCID: PMC10989862 DOI: 10.1093/g3journal/jkae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/09/2024] [Accepted: 02/18/2024] [Indexed: 02/26/2024]
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.
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Affiliation(s)
- Charlotte Brault
- 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
| | - Vincent Segura
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France
| | - Maryline Roques
- 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
| | - Pauline Lamblin
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Virginie Bouckenooghe
- 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
| | | | - Constance Cunty
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
- Centre du Rosé, Vidauban 83550, France
| | - Matthieu Breil
- 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
| | - Marina Frouin
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Léa Garcin
- 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
| | - Louise Camps
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Marie-Agnès Ducasse
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Charles Romieu
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France
| | - Gilles Masson
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
- Centre du Rosé, Vidauban 83550, France
| | - Sébastien Julliard
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Timothée Flutre
- INRAE, CNRS, AgroParisTech, Université Paris-Saclay, GQE—Le Moulon, Gif-sur-Yvette 91190, France
| | - Loïc Le Cunff
- 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
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Magon G, De Rosa V, Martina M, Falchi R, Acquadro A, Barcaccia G, Portis E, Vannozzi A, De Paoli E. Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics. FRONTIERS IN PLANT SCIENCE 2023; 14:1293186. [PMID: 38148866 PMCID: PMC10750425 DOI: 10.3389/fpls.2023.1293186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/27/2023] [Indexed: 12/28/2023]
Abstract
The multifaceted nature of climate change is increasing the urgency to select resilient grapevine varieties, or generate new, fitter cultivars, to withstand a multitude of new challenging conditions. The attainment of this goal is hindered by the limiting pace of traditional breeding approaches, which require decades to result in new selections. On the other hand, marker-assisted breeding has proved useful when it comes to traits governed by one or few genes with great effects on the phenotype, but its efficacy is still restricted for complex traits controlled by many loci. On these premises, innovative strategies are emerging which could help guide selection, taking advantage of the genetic diversity within the Vitis genus in its entirety. Multiple germplasm collections are also available as a source of genetic material for the introgression of alleles of interest via adapted and pioneering transformation protocols, which present themselves as promising tools for future applications on a notably recalcitrant species such as grapevine. Genome editing intersects both these strategies, not only by being an alternative to obtain focused changes in a relatively rapid way, but also by supporting a fine-tuning of new genotypes developed with other methods. A review on the state of the art concerning the available genetic resources and the possibilities of use of innovative techniques in aid of selection is presented here to support the production of climate-smart grapevine genotypes.
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Affiliation(s)
- Gabriele Magon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Valeria De Rosa
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
| | - Matteo Martina
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Rachele Falchi
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
| | - Alberto Acquadro
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Gianni Barcaccia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Ezio Portis
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Largo P. Braccini 2, Grugliasco, Italy
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padova, Agripolis, Viale dell’Università 16, Legnaro, Italy
| | - Emanuele De Paoli
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Via delle Scienze, 206, Udine, Italy
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Bharati R, Sen MK, Severová L, Svoboda R, Fernández-Cusimamani E. Polyploidization and genomic selection integration for grapevine breeding: a perspective. FRONTIERS IN PLANT SCIENCE 2023; 14:1248978. [PMID: 38034577 PMCID: PMC10684766 DOI: 10.3389/fpls.2023.1248978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.
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Affiliation(s)
- Rohit Bharati
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Madhab Kumar Sen
- Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Lucie Severová
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Roman Svoboda
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Eloy Fernández-Cusimamani
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
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Brault C, Lazerges J, Doligez A, Thomas M, Ecarnot M, Roumet P, Bertrand Y, Berger G, Pons T, François P, Le Cunff L, This P, Segura V. Interest of phenomic prediction as an alternative to genomic prediction in grapevine. PLANT METHODS 2022; 18:108. [PMID: 36064570 PMCID: PMC9442960 DOI: 10.1186/s13007-022-00940-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. RESULTS For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. CONCLUSION NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
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Affiliation(s)
- Charlotte Brault
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Juliette Lazerges
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Agnès Doligez
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Miguel Thomas
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Pierre Roumet
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Yves Bertrand
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Gilles Berger
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Thierry Pons
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Pierre François
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Loïc Le Cunff
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Patrice This
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Vincent Segura
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France.
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France.
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Brault C, Segura V, This P, Le Cunff L, Flutre T, François P, Pons T, Péros JP, Doligez A. Across-population genomic prediction in grapevine opens up promising prospects for breeding. HORTICULTURE RESEARCH 2022; 9:uhac041. [PMID: 35184162 PMCID: PMC9070645 DOI: 10.1093/hr/uhac041] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/01/2022] [Indexed: 05/15/2023]
Abstract
Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.
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Affiliation(s)
- Charlotte Brault
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Institut Français de la Vigne et du Vin, F-34398 Montpellier, France
| | - Vincent Segura
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Patrice This
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Loïc Le Cunff
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Institut Français de la Vigne et du Vin, F-34398 Montpellier, France
| | - Timothée Flutre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, 91190, Gif-sur-Yvette, France
| | - Pierre François
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Thierry Pons
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jean-Pierre Péros
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Agnès Doligez
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
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