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Yusuf M, Miller MD, Stefaniak TR, Haagenson D, Endelman JB, Thompson AL, Shannon LM. Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis. THE PLANT GENOME 2024:e20507. [PMID: 39256988 DOI: 10.1002/tpg2.20507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/08/2024] [Accepted: 08/03/2024] [Indexed: 09/12/2024]
Abstract
Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image-analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non-additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length-to-width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi-trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non-additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.
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Affiliation(s)
- Muyideen Yusuf
- Department of Horticultural Science, University of Minnesota, Saint Paul, Minnesota, USA
| | | | - Thomas R Stefaniak
- Department of Horticultural Science, University of Minnesota, Saint Paul, Minnesota, USA
| | - Darrin Haagenson
- USDA-ARS, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, USA
| | - Jeffrey B Endelman
- Department of Plant & Agroecosystem Sciences, University of Wisconsin, Madison, Wisconsin, USA
| | - Asunta L Thompson
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Laura M Shannon
- Department of Horticultural Science, University of Minnesota, Saint Paul, Minnesota, USA
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2
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Sood S, Bhardwaj V, Bairwa A, Dalamu, Sharma S, Sharma AK, Kumar A, Lal M, Kumar V. Genome-wide association mapping and genomic prediction for late blight and potato cyst nematode resistance in potato ( Solanum tuberosum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1211472. [PMID: 37860256 PMCID: PMC10582711 DOI: 10.3389/fpls.2023.1211472] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/12/2023] [Indexed: 10/21/2023]
Abstract
Potatoes are an important source of food for millions of people worldwide. Biotic stresses, notably late blight and potato cyst nematodes (PCN) pose a major threat to potato production worldwide, and knowledge of genes controlling these traits is limited. A genome-wide association mapping study was conducted to identify the genomic regulators controlling these biotic stresses, and the genomic prediction accuracy was worked out using the GBLUP model of genomic selection (GS) in a panel of 222 diverse potato accessions. The phenotype data on resistance to late blight and two PCN species (Globodera pallida and G. rostochiensis) were recorded for three and two consecutive years, respectively. The potato panel was genotyped using genotyping by sequencing (GBS), and 1,20,622 SNP markers were identified. A total of 7 SNP associations for late blight resistance, 9 and 11 for G. pallida and G. rostochiensis, respectively, were detected by additive and simplex dominance models of GWAS. The associated SNPs were distributed across the chromosomes, but most of the associations were found on chromosomes 5, 10 and 11, which have been earlier reported as the hotspots of disease-resistance genes. The GS prediction accuracy estimates were low to moderate for resistance to G. pallida (0.04-0.14) and G. rostochiensis (0.14-0.21), while late blight resistance showed a high prediction accuracy of 0.42-0.51. This study provides information on the complex genetic nature of these biotic stress traits in potatoes and putative SNP markers for resistance breeding.
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Affiliation(s)
- Salej Sood
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Vinay Bhardwaj
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Aarti Bairwa
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Dalamu
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Sanjeev Sharma
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Ashwani K. Sharma
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Ashwani Kumar
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
| | - Mehi Lal
- ICAR-Central Potato Research Institute, Regional Station, Modipuram, UP, India
| | - Vinod Kumar
- Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute, Shimla, HP, India
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Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E. Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. FRONTIERS IN PLANT SCIENCE 2023; 14:1178902. [PMID: 37546247 PMCID: PMC10401275 DOI: 10.3389/fpls.2023.1178902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | | | | | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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Wu PY, Stich B, Renner J, Muders K, Prigge V, van Inghelandt D. Optimal implementation of genomic selection in clone breeding programs-Exemplified in potato: I. Effect of selection strategy, implementation stage, and selection intensity on short-term genetic gain. THE PLANT GENOME 2023:e20327. [PMID: 37177848 DOI: 10.1002/tpg2.20327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 05/15/2023]
Abstract
Genomic selection (GS) is used in many animal and plant breeding programs to enhance genetic gain for complex traits. However, its optimal integration in clone breeding programs, for example potato, that up to now relied on phenotypic selection (PS) requires further research. In this study, we performed computer simulations based on an empirical genomic dataset of tetraploid potato to (i) investigate under a fixed budget how the weight of GS relative to PS, the stage of implementing GS, the correlation between an auxiliary trait and the target trait, the variance components, and the prediction accuracy affect the genetic gain of the target trait, (ii) determine the optimal allocation of resources maximizing the genetic gain of the target trait, and (iii) make recommendations to breeders how to implement GS in clone and especially potato breeding programs. In our simulation results, any selection strategy involving GS had a higher short-term genetic gain for the target trait than Standard-PS. In addition, we showed that implementing GS in consecutive selection stages can largely enhance short-term genetic gain and recommend the breeders to implement GS at single hills and A clone stages. Furthermore, we observed for selection strategies involving GS that the optimal allocation of resources maximizing the genetic gain of the target trait differed considerably from those typically used in potato breeding programs and, thus, require the adjustment of the selection and phenotyping intensities. The trends are described in our study. Therefore, our study provides new insight for breeders regarding how to optimally implement GS in a commercial potato breeding program to improve the short-term genetic gain for their target trait.
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Affiliation(s)
- Po-Ya Wu
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University, Düsseldorf, Germany
- Max Planck Institute for Plant Breeding Research, Köln, Germany
| | - Juliane Renner
- Böhm-Nordkartoffel Agrarproduktion GmbH & Co. OHG, Hohenmocker, Germany
| | | | | | - Delphine van Inghelandt
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany
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Pandey J, Scheuring DC, Koym JW, Vales MI. Genomic regions associated with tuber traits in tetraploid potatoes and identification of superior clones for breeding purposes. FRONTIERS IN PLANT SCIENCE 2022; 13:952263. [PMID: 35937326 PMCID: PMC9354404 DOI: 10.3389/fpls.2022.952263] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 06/29/2022] [Indexed: 05/05/2023]
Abstract
In potato breeding, morphological tuber traits are important selection targets to meet the demands of the fresh and processing markets. Understanding the genetic basis of tuber traits should guide selection and improve breeding efficiencies. However, this is challenging in potato due to the complexity of the traits and the polyploid nature of the potato genome. High-throughput affordable molecular markers and new software specific for polyploid species have the potential to unlock previously unattainable levels of understanding of the genetic basis of tuber traits in tetraploid potato. In this study, we genotyped a diversity panel of 214 advanced clones with the 22 K SNP potato array and phenotyped it in three field environments in Texas. We conducted a genome-wide association study using the GWASpoly software package to identify genomic regions associated with tuber morphological traits. Some of the QTLs discovered confirmed prior studies, whereas others were discovered for the first time. The main QTL for tuber shape was detected on chromosome 10 and explained 5.8% of the phenotypic variance. GWAS analysis of eye depth detected a significant QTL on chromosome 10 and explained 3.9% of the phenotypic variance. Another QTL peak for eye depth on chromosome 5 was located near the CDF1 gene, an important regulator of maturity in potato. Our study found that multiple QTLs govern russeting in potato. A major QTL for flesh color on chromosome 3 that explained 26% of the phenotypic variance likely represents the Y locus responsible for yellow flesh in potato tubers. Several QTLs were detected for purple skin color on chromosome 11. Furthermore, genomic estimated breeding values were obtained, which will aid in the early identification of superior parental clones that should increase the chances of producing progenies with higher frequencies of the desired tuber traits. These findings will contribute to a better understanding of the genetic basis of morphological traits in potato, as well as to identifying parents with the best breeding values to improve selection efficiency in our potato breeding program.
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Affiliation(s)
- Jeewan Pandey
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - Douglas C. Scheuring
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
| | - Jeffrey W. Koym
- Texas A&M University AgriLife Research and Extension Center, Lubbock, TX, United States
| | - M. Isabel Vales
- Department of Horticultural Sciences, Texas A&M University, College Station, TX, United States
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Ortiz R, Crossa J, Reslow F, Perez-Rodriguez P, Cuevas J. Genome-Based Genotype × Environment Prediction Enhances Potato ( Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling. FRONTIERS IN PLANT SCIENCE 2022; 13:785196. [PMID: 35197995 PMCID: PMC8859116 DOI: 10.3389/fpls.2022.785196] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/05/2022] [Indexed: 05/03/2023]
Abstract
Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model.
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Affiliation(s)
- Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Lomma, Sweden
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Fredrik Reslow
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Lomma, Sweden
| | | | - Jaime Cuevas
- División de Ciencias, Ingeniería y Tecnologías, Universidad de Quintana Roo, Chetumal, Mexico
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Cazenave X, Petit B, Lateur M, Nybom H, Sedlak J, Tartarini S, Laurens F, Durel CE, Muranty H. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. G3 (BETHESDA, MD.) 2021; 12:6459174. [PMID: 34893831 PMCID: PMC9210277 DOI: 10.1093/g3journal/jkab420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/29/2021] [Indexed: 11/12/2022]
Abstract
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
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Affiliation(s)
- Xabi Cazenave
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Bernard Petit
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Marc Lateur
- Plant Breeding and Biodiversity, Centre Wallon de Recherches Agronomiques, Gembloux, Belgium
| | - Hilde Nybom
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Kristianstad, Sweden
| | - Jiri Sedlak
- Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s.r.o, Holovousy, Czech Republic
| | - Stefano Tartarini
- Department of Agricultural Sciences, University of Bologna, Bologna, Italy
| | - François Laurens
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Charles-Eric Durel
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France
| | - Hélène Muranty
- Univ Angers, INRAE, Institut Agro, IRHS, SFR QuaSaV, F-49000 Angers, France,Corresponding author:
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