Genetic architecture and genomic predictive ability of apple quantitative traits across environments
Jung, Michaela (Institute of Agricultural Sciences (Zurich))
Keller, Beat (Institute of Agricultural Sciences (Zurich))
Roth, Morgane (Agroscope (Wädenswil))
Aranzana, Maria José (Centre de Recerca en Agrigenòmica)
Auwerkerken, Annemarie (Better3fruit N.V (Belgium))
Guerra, Walter (Research Centre Laimburg)
Al-Rifaï, Mehdi (Université Angers)
Lewandowski, Mariusz (The National Institute of Horticultural Research (Poland))
Sanin, Nadia (Research Centre Laimburg)
Rymenants, Marijn (Laboratory for Plant Genetics and Crop Improvement (Belgium))
Didelot, Frédérique (INRAE. Unité expérimentale Horticole)
Dujak, Christian (Centre de Recerca en Agrigenòmica)
Font i Forcada, Carolina (Institut de Recerca i Tecnologia Agroalimentàries)
Knauf, Andrea (Institute of Agricultural Sciences (Zurich))
Laurens, François (Université Angers)
Studer, Bruno (Institute of Agricultural Sciences (Zurich))
Muranty, Hélène (Université Angers)
Patocchi, Andrea (Agroscope (Wädenswil))

Fecha:	2022
Resumen:	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.
Ayudas:	European Commission 817970 Ministerio de Economía y Competitividad SEV-2015-0533 Ministerio de Economía y Competitividad CEX2019-000902-S
Nota:	Altres ajuts: CERCA Programme/Generalitat de Catalunya, Project RIS3CAT (COTPA-FRUIT3CAT) financed by the European Regional Development Fund through the FEDER frame of Catalonia 2014-2020
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Horticulture research, Vol. 9 (February 2022) , art. uhac028, ISSN 2052-7276

DOI: 10.1093/hr/uhac028
PMID: 35184165

22 p, 1.9 MB

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