10 results on '"Bonnafous, Fanny"'
Search Results
2. RNA expression dataset of 384 sunflower hybrids in field condition
- Author
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Penouilh-Suzette Charlotte, Pomiès Lise, Duruflé Harold, Blanchet Nicolas, Bonnafous Fanny, Dinis Romain, Brouard Céline, Gody Louise, Grassa Christopher, Heudelot Xavier, Laporte Marion, Larroque Marion, Marage Gwenola, Mayjonade Baptiste, Mangin Brigitte, de Givry Simon, and Langlade Nicolas B.
- Subjects
sunflower ,genetics ,gene expression ,drought ,Oils, fats, and waxes ,TP670-699 - Abstract
This article describes how RNA expression data of 173 genes were produced on 384 sunflower hybrids grown in field conditions. Sunflower hybrids were selected to represent genetic diversity within cultivated sunflower. The RNA was extracted from mature leaves at one time seven days after anthesis. These data allow to differentiate the different genotype behaviours and constitute a valuable resource to the community to study the adaptation of crops to field conditions and the molecular basis of heterosis. It is available on data.inra.fr repository.
- Published
- 2020
- Full Text
- View/download PDF
3. Multi-scale modeling of sunflower crop responses to genetic and environment variations
- Author
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Florie, Gosseau, Bonnafous, Fanny, Pégot-Espagnet, Prune, Mangin, Brigitte, Langlade, Nicolas, Casadebaig, Pierre, Laboratoire des Interactions Plantes Microbes Environnement (LIPME), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cirad, Inrae, Inria, and Casadebaig, Pierre
- Subjects
[SDV] Life Sciences [q-bio] ,[SDV]Life Sciences [q-bio] - Abstract
International audience; Current research in genomics and ecophysiology strive to improve genetophenotype predictions beyond methods based solely on genetic information such as whole genome prediction (WGP). Predictive approaches coupling quantitative genetics and crop modeling are designed, either with an emphasis on ecophysiology or on statistics, according to the modeling scale of traits: from molecular to crop level (BustosKorts et al., 2016). Genetophenotype predictions are generally carried to predict the breeding values from genomewide information using statistical models. The difficulty with this method lies in the prediction of nonadditive gene effect and genebyenvironment interactions. Crop simulation models, by representing dynamic responses of crop and soil processes to the environment, are successfully used to predict phenotypic plasticity for agronomical traits (Chenu et al., 2017). Therefore, coupling quantitative genetics and processbased modeling should improve the accuracy of breeding value prediction, with different options depending on the phenotypic distance, as illustrated by Hammer et al. (2016). This study aims to predict the performance of a panel of sunflower hybrids in different farming environments, and therefore to identify the most promising genotypes and key physiological traits controlling yield stability and adaptation to future climatic scenarios. Our multiscale modeling strategy proceeded in two steps: (1) building whole genome prediction models to predict a set of component traits as a function of allelic combination of genotypes and (2) predicting the hybrid yield as a function of component traits, environmental variables and management practices using a crop simulation model (Casadebaig et al., 2016). We evaluated the accuracy of this multiscale model on a multienvironmental network (13 environments) where the training population was phenotyped and on independent trials with nonobserved genotypes and environments (9 environments). Over these 22 environments, we found that the overall accuracy of our multiscale model was worse than the accuracy of a single genomic prediction model fitted for crop yield. The WGP model showed a good accuracy on the training environments and on the test environments including hybrids selected for their yield range. However, the multiscale model showed a better accuracy than the WGP model in 4 out 9 test environments, specifically when tested hybrids were not selected for their extreme yield levels. Overall, we identified new bottlenecks related to the design of combined crop and genetic models, such as the definition of component traits articulating genomic and ecophysiological models and the environment(s) used to train the genomic models. …/…
- Published
- 2020
4. Prise en compte d’informations a priori en sélection génomique dans un dispositif d’hybrides de tournesol (Helianthus annuus L.)
- Author
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Bonnafous, Fanny and STAR, ABES
- Subjects
Sunflower ,Tournesol ,Genomic selection ,Non-additive ,[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,Heterosis ,MultiBLUP ,Hétérosis ,Non-additif ,Sélection génomique - Abstract
Genomic selection is a powerful tool for predicting phenotypes or genetic values of non-observed individuals, based on a panel both phenotyped and genotyped. The mixed models GBLUP usually utilized take into account all markers simultaneously, assuming that all their effects all follow the same Gaussian distribution. Knowledge of the biological mechanisms underlying phenotypic variation is therefore not taken into account in such modeling. The aim of this thesis is to integrate in GBLUP models a priori knowledge, such as genomic regions involved in the variation of the traits of interest or networks of genes, in order to evaluate the potential for improvement of accuracies. These models were applied to the Helianthus annuus L. sunflower specie on three traits (flowering time, yield and leaf senescence) in 13 several environments. One of the main challenges of genetic studies on sunflower hybrids is to model hybrid vigor, or heterosis. Different hypotheses, including dominance, over-dominance and epistasis have been proposed to clarify the genetic mechanisms underlying the heterosis phenomenon, but their importance is not clearly known. In this context, the first part of this study aimed to test the efficiency of the GS in an hybrid population from the crossing of 36 female lines with 36 male lines. For this purpose, models taking into account non-additive effects were experimented, and the results validated experimentally in field over two years. The prediction of the genetic values of the hybrids was conclusive, so we looked for a priori information to integrate with these models. SNPs involved in the variation of the three traits of interest were searched using several models of GWAS (additive and non-additive). Moreover, in order to test models taking into account epistatic interactions, SNPs located in known gene networks have been sought. Finally the integration of the genomic regions involved in the variation of the traits, into the GBLUP models, was conducted. Two methods were implemented for this, namely the modeling of a priori information in the random part (MultiBLUP model) or in the fixed part of the models. These methods do not show significant improvement in accuracies compared to GBLUP models without a priori information., La sélection génomique (GS) est un outil puissant pour prédire les phénotypes ou les valeurs génétiques d'individus encore non observés, sur la base d'un panel à la fois phénotypé et génotypé. Les modèles mixtes GBLUP habituellement utilisés prennent en compte tous les marqueurs simultanément, en postulant que leurs effets suivent tous la même distribution gaussienne. Les connaissances des mécanismes biologiques sous-jacent à la variation phénotypique ne sont donc pas pris en compte dans une telle modélisation. Le but de cette thèse est d'intégrer dans des modèles GBLUP des connaissances a priori, comme des régions génomique impliquées dans la variation des caractères d'intérêt ou encore des réseaux de gènes, afin d'évaluer le potentiel d'amélioration de la précision de prédiction. Ces modèles ont été appliqués à l'espèce de tournesol Helianthus annuus L., sur trois caractères (la floraison, le rendement et la sénescence foliaire) dans 13 environnements différents. L'un des principaux défis des études sur les hybrides de tournesol est de modéliser la vigueur hybride, ou hétérosis. Différentes hypothèses, incluant la dominance, la superdominance et l'épistasie ont été proposées pour clarifier les mécanismes génétiques sous-jacents au phénomène de l'hétérosis, mais leur importance n'est pas clairement connue. Dans ce contexte, la première partie de cette étude a eu pour but de tester l'efficacité de la GS dans une population d'hybrides provenant du croisement de 36 lignées femelles avec 36 lignées mâles. Pour cela des modèles prenant en compte des effets non-additifs ont été expérimentés, et les résultats validés expérimentalement en champ sur deux années. La prédiction des valeurs génétiques des hybrides ayant été concluante, nous avons ensuite cherché des informations a priori à intégrer à ces modèles. Des SNPs impliqués dans la variation des trois caractères d'intérêt ont été recherchés à l'aide de plusieurs modèles de GWAS (additifs et non-additifs). De plus, dans la perspective de tester des modèles prenant en compte des interactions épistatiques, des SNPs localisés dans des réseaux de gènes connus ont été recherchés. La dernière partie de cette thèse a eu pour but d'intégrer aux modèles GBLUP ces régions génomiques impliquées dans la variation des caractères. Deux méthodes ont été utilisées pour cela, à savoir la modélisation des informations a priori dans la partie aléatoire (modèle MultiBLUP) ou dans la partie fixe des modèles. Ces méthodes ne montrent pas d'amélioration significative des précisions de prédiction par rapport aux modèles GBLUP sans information a priori.
- Published
- 2017
5. Genomic Prediction of Sunflower Hybrids Oil Content
- Author
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Mangin, Brigitte, Bonnafous, Fanny, Blanchet, Nicolas, Boniface, Marie-Claude, Bret-Mestries, Emmanuelle, Carrere, Sebastien, Cottret, Ludovic, Legrand, Ludovic, Marage, Gwenola, Pegot - Espagnet, Prune, Munos, Stephane, Pouilly, Nicolas, Vear, Felicity, Vincourt, Patrick, Langlade, Nicolas, Laboratoire des interactions plantes micro-organismes (LIPM), Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), ANR-11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c(2011), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
- Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Vegetal Biology ,sunflower ,hybrid ,genomic selection ,factorial design ,oil content ,GBS ,prédiction génétique ,Plant Science ,lcsh:Plant culture ,huile de tournesol ,hybride ,Agricultural sciences ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,lcsh:SB1-1110 ,Biologie végétale ,Sciences agricoles ,Original Research - Abstract
International audience; Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore. GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.
- Published
- 2017
6. Prédiction du gène au phénotype par une approche couplant génétique quantitative et écophysiologie. (rapport de stage)
- Author
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Gosseau, Florie, Langlade, Nicolas, Casadebaig, Pierre, Mangin, Brigitte, and Bonnafous, Fanny
- Abstract
Prédiction du gène au phénotype par une approche couplant génétique quantitative et écophysiologie. (rapport de stage)Nous proposons une nouvelle approche permettant d’identifier les combinaisons alléliques pouvant apporter un gain génétique dans des conditions de cultures données. En alliant un modèle génétique d’association avec un modèle de culture, nous pouvons ainsi prédire des traits phénotypiques complexes à partir du génotype. Pour cela, 14 marqueurs génétiques ont été sélectionnés par association sur 7 caractères phénotypiques d’entrée du modèle de culture SUNFLO mesurés sur 5 expériences en champ et une en conditions contrôlées. Les effets des allèles à ces marqueurs sont utilisés pour construire les caractères phénotypiques potentiels utilisés en variable d’entrée du modèle de culture. Ce modèle prédit des traits complexes (rendement et contenu en huile) en fonction de l’environnement et de la conduite de culture dans trois dispositifs : les environnements ayant servi à l’analyse génétique, un environnement indépendant et une population d’environnements virtuels représentant l’aire de culture du tournesol ces dix dernières années.La comparaison des données simulées et des données observées à mis en évidence une bonne capacité prédictive du modèle SUNFLO sur la population utilisée en génétique pour le contenu en huile à l’inverse du rendement. La prédiction du rendement sur la population d’environnements virtuels permet de mettre en évidence le lien fort entre rendement et stabilité. Trois SNPs sont plus fréquents dans les génotypes avec de forts rendements, les deux associés à l’indic de récolte et un à la date de maturité. Nous avons également identifié dix SNPs permettant un compromis optimal entre rendement et stabilité. Les caractères phénotypiques importants pour ce compromis sont l’indice de récolte, la précocité (date de floraison et date de maturité) et l’architecture (nombre de feuilles et position de la plus grande feuille).Le travail effectué constitue une tentative de combinaison entre génétique quantitative et modélisation écophysiologique et agronomique. Il a permis d’appréhender certaines difficultés que l’on peut rencontrer dans cette approche de biologie des systèmes appliquée à l’agronomie.Prediction of gene to phenotype by approach coupling quantitative genetic and ecophysiology. (internship report) We propose a new approach for identifying allelic combinations which can provide genetic gain in a given crop conditions. By combining a genetic model of association with a crop growth model we can predict complex phenotypic traits from genotype.For that, 14 genetic markers were selected by association on 7 phenotypic traits in input of SUNFLO a crop growth model. Phenotypic traits are measured on 5 experiments and in controlled conditions. The effects of alleles at these markers are used to construct potential phenotypic traits for input variables of the crop growth model. This model predicts complex traits (yield and oil content) depending on the environment and culture management in three devices: the environments used in genetic association, an independent environment and a population of virtual environments representing sunflower culture area over the past decade in France.Comparing the simulated data and observed data revealed that SUNFLO model have agood predictive capacity on oil content and not on yield. The prediction performance on the virtual trial allows to highlight the strong link between performance and stability. Three SNPs are more common in genotypes with high yields, both associated with the harvest informant and the date of maturity. We also identified ten SNPs for an optimal compromise between performance and stability. Significant phenotypic traits for this compromise is the harvest index, early (date of flowering and maturity) and architecture (leaf number and position of the larger sheet).The work done is an attempt to combination of quantitative genetics and ecophysiological and agronomic modeling. It helped to understand difficulties that may be encountered in systems biology approach applied to agronomy.
- Published
- 2016
- Full Text
- View/download PDF
7. Prédiction du gène au phénotype par une approche couplant génétique quantitative et écophysiologie
- Author
-
Gosseau, Florie, Langlade, Nicolas, Casadebaig, Pierre, Mangin, Brigitte, and Bonnafous, Fanny
- Abstract
Prédiction du gène au phénotype par une approche couplant génétique quantitative et écophysiologie. (rapport de stage)Nous proposons une nouvelle approche permettant d’identifier les combinaisons alléliques pouvant apporter un gain génétique dans des conditions de cultures données. En alliant un modèle génétique d’association avec un modèle de culture, nous pouvons ainsi prédire des traits phénotypiques complexes à partir du génotype. Pour cela, 14 marqueurs génétiques ont été sélectionnés par association sur 7 caractères phénotypiques d’entrée du modèle de culture SUNFLO mesurés sur 5 expériences en champ et une en conditions contrôlées. Les effets des allèles à ces marqueurs sont utilisés pour construire les caractères phénotypiques potentiels utilisés en variable d’entrée du modèle de culture. Ce modèle prédit des traits complexes (rendement et contenu en huile) en fonction de l’environnement et de la conduite de culture dans trois dispositifs : les environnements ayant servi à l’analyse génétique, un environnement indépendant et une population d’environnements virtuels représentant l’aire de culture du tournesol ces dix dernières années.La comparaison des données simulées et des données observées à mis en évidence une bonne capacité prédictive du modèle SUNFLO sur la population utilisée en génétique pour le contenu en huile à l’inverse du rendement. La prédiction du rendement sur la population d’environnements virtuels permet de mettre en évidence le lien fort entre rendement et stabilité. Trois SNPs sont plus fréquents dans les génotypes avec de forts rendements, les deux associés à l’indic de récolte et un à la date de maturité. Nous avons également identifié dix SNPs permettant un compromis optimal entre rendement et stabilité. Les caractères phénotypiques importants pour ce compromis sont l’indice de récolte, la précocité (date de floraison et date de maturité) et l’architecture (nombre de feuilles et position de la plus grande feuille).Le travail effectué constitue une tentative de combinaison entre génétique quantitative et modélisation écophysiologique et agronomique. Il a permis d’appréhender certaines difficultés que l’on peut rencontrer dans cette approche de biologie des systèmes appliquée à l’agronomie.Prediction of gene to phenotype by approach coupling quantitative genetic and ecophysiology. (internship report) We propose a new approach for identifying allelic combinations which can provide genetic gain in a given crop conditions. By combining a genetic model of association with a crop growth model we can predict complex phenotypic traits from genotype.For that, 14 genetic markers were selected by association on 7 phenotypic traits in input of SUNFLO a crop growth model. Phenotypic traits are measured on 5 experiments and in controlled conditions. The effects of alleles at these markers are used to construct potential phenotypic traits for input variables of the crop growth model. This model predicts complex traits (yield and oil content) depending on the environment and culture management in three devices: the environments used in genetic association, an independent environment and a population of virtual environments representing sunflower culture area over the past decade in France.Comparing the simulated data and observed data revealed that SUNFLO model have agood predictive capacity on oil content and not on yield. The prediction performance on the virtual trial allows to highlight the strong link between performance and stability. Three SNPs are more common in genotypes with high yields, both associated with the harvest informant and the date of maturity. We also identified ten SNPs for an optimal compromise between performance and stability. Significant phenotypic traits for this compromise is the harvest index, early (date of flowering and maturity) and architecture (leaf number and position of the larger sheet).The work done is an attempt to combination of quantitative genetics and ecophysiological and agronomic modeling. It helped to understand difficulties that may be encountered in systems biology approach applied to agronomy.
- Published
- 2016
- Full Text
- View/download PDF
8. Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids
- Author
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Bonnafous, Fanny, primary, Fievet, Ghislain, additional, Blanchet, Nicolas, additional, Boniface, Marie-Claude, additional, Carrère, Sébastien, additional, Gouzy, Jérôme, additional, Legrand, Ludovic, additional, Marage, Gwenola, additional, Bret-Mestries, Emmanuelle, additional, Munos, Stéphane, additional, Pouilly, Nicolas, additional, Vincourt, Patrick, additional, Langlade, Nicolas, additional, and Mangin, Brigitte, additional
- Published
- 2017
- Full Text
- View/download PDF
9. Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids.
- Author
-
Bonnafous, Fanny, Fievet, Ghislain, Blanchet, Nicolas, Boniface, Marie-Claude, Carrère, Sébastien, Gouzy, Jérôme, Legrand, Ludovic, Marage, Gwenola, Bret-Mestries, Emmanuelle, Munos, Stéphane, Pouilly, Nicolas, Vincourt, Patrick, Langlade, Nicolas, and Mangin, Brigitte
- Subjects
- *
SUNFLOWER genetics , *FLOWERING time , *SUNFLOWER hybridization , *ALLELES in plants , *HETEROSIS in plants - Abstract
Key message: This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Abstract: Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. Genomic Prediction of Sunflower Hybrids Oil Content.
- Author
-
Mangin B, Bonnafous F, Blanchet N, Boniface MC, Bret-Mestries E, Carrère S, Cottret L, Legrand L, Marage G, Pegot-Espagnet P, Munos S, Pouilly N, Vear F, Vincourt P, and Langlade NB
- Abstract
Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.
- Published
- 2017
- Full Text
- View/download PDF
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