2026-04-18T02:58:06Zhttps://recercat.cat/oai/request
oai:recercat.cat:20.500.12327/17592025-10-22T11:02:50Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT author Jung, Michaela author Keller, Beat author Roth, Morgane author Aranzana, Maria José author Auwerkerken, Annemarie author Guerra, Walter author Al-Rifaï, Mehdi author Lewandowski, Mariusz author Sanin, Nadia author Rymenants, Marijn author Didelot, Frédérique author Dujak, Christian author Font i Forcada, Carolina author Knauf, Andrea author Laurens, François author Studer, Bruno author Muranty, Hélène author Patocchi, Andrea other Producció Vegetal group Genòmica i Biotecnologia 2025-10-22T11:02:50Z 2025-10-22T11:02:50Z 2022-02-19 Jung, Michaela, Beat Keller, Morgane Roth, Maria José Aranzana, Annemarie Auwerkerken, Walter Guerra, and Mehdi Al-Rifaï et al. 2022. "Genetic Architecture And Genomic Predictive Ability Of Apple Quantitative Traits Across Environments". Horticulture Research 9. doi:10.1093/hr/uhac028. 2052-7276 http://hdl.handle.net/20.500.12327/1759 https://doi.org/10.1093/hr/uhac028 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 traitenvironment 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. eng Attribution 4.0 International Genetic architecture and genomic predictive ability of apple quantitative traits across environments info:eu-repo/semantics/article