1. Genetic architecture and genomic predictive ability of apple quantitative traits across environments
- Author
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Michaela Jung, Beat Keller, Morgane Roth, Maria José Aranzana, Annemarie Auwerkerken, Walter Guerra, Mehdi Al-Rifaï, Mariusz Lewandowski, Nadia Sanin, Marijn Rymenants, Frédérique Didelot, Christian Dujak, Carolina Font i Forcada, Andrea Knauf, François Laurens, Bruno Studer, Hélène Muranty, Andrea Patocchi, Producció Vegetal, Genòmica i Biotecnologia, Agroscope, Institute of Agricultural Sciences, Génétique et Amélioration des Fruits et Légumes (GAFL), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Centre for Research in Agricultural Genomics (CRAG), Better3Fruit, Research Centre Laimburg, Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, 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), The National Institute of Horticultural Research, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Unité Horticole (HORTI), Spanish GovernmentSEV-20150533CEX2019-000902-S, European Project: 817970,H2020 INVITE, European Commission, Generalitat de Catalunya, and Ministerio de Economía y Competitividad (España)
- Subjects
fungi ,Apple scab ,Malus Sierversii ,Plant Science ,biochemical phenomena, metabolism, and nutrition ,Horticulture ,equipment and supplies ,complex mixtures ,Biochemistry ,[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics ,[SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding ,Genetics ,bacteria ,Cultivar ,Biotechnology - Abstract
Implementation of genomic tools is desirable to increase the efficiency of apple breeding. Recently, the multi-environment apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic predictive ability, and studying genotype by environment interactions (G × E). So far, only two phenological traits were investigated using the apple REFPOP, although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding. Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic predictive abilities of 0.18-0.88 were estimated using main-effect univariate, main-effect multivariate, multi-environment univariate, and multi-environment multivariate models. The G × E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency., This work was partially supported by the project RIS3CAT (COTPA-FRUIT3CAT) financed by the European Regional Development Fund through the FEDER frame of Catalonia 2014-2020 and by the CERCA Program from Generalitat de Catalunya. We acknowledge financial support from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa Programme for Centres of Excellence in R&D” 2016-2019 (SEV-20150533) and 2020-2023 (CEX2019-000902-S)., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000902-S)
- Published
- 2022
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