1. Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple
- Author
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Xabi Cazenave, Bernard Petit, Marc Lateur, Hilde Nybom, Jiri Sedlak, Stefano Tartarini, François Laurens, Charles-Eric Durel, Hélène Muranty, Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre Wallon de Recherches Agronomiques (CRA-W), University College of Kristianstad, Research and Breeding Institute of Pomology Holovousy (VSUO), University of Bologna, INRAE metaprogram SelGen and more specifically from the GdivSelgen project. The framework of the regional programme 'Objectif Végétal, Research, Education and Innovation in Pays de la Loire', supported by the French Region Pays de la Loire, Angers Loire Métropole and the European Regional Development Fund. The Commission of the European Communities (contract N° QLK5-CT-822 2002-01492), Directorate—General Research— Quality of Life and Management of Living Resources Programme., European Project: 265582,EC:FP7:KBBE,FP7-KBBE-2010-4,FRUIT BREEDOMICS(2011), European Project: 817970,H2020 INVITE, Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Bologna/Università di Bologna, Cazenave X., Petit B., Lateur M., Nybom H., Sedlak J., Tartarini S., Laurens F., Durel C.-E., and Muranty H.
- Subjects
0106 biological sciences ,Genotype ,Malus domestica ,01 natural sciences ,Polymorphism, Single Nucleotide ,genomic selection ,[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics ,03 medical and health sciences ,training set design ,population combination ,Genetics ,Selection, Genetic ,Agricultural Science ,Molecular Biology ,Genetics (clinical) ,030304 developmental biology ,0303 health sciences ,Genome ,Models, Genetic ,Genomics ,Shared Data Resource ,germplasm ,[SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding ,Plant Breeding ,GenPred ,Phenotype ,Genomic Prediction ,Malus ,Genomic ,010606 plant biology & botany - 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.
- Published
- 2022
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