Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems.
Journal
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
ISSN: 1432-2242
Titre abrégé: Theor Appl Genet
Pays: Germany
ID NLM: 0145600
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
11
08
2021
accepted:
19
10
2021
pubmed:
2
11
2021
medline:
5
4
2022
entrez:
1
11
2021
Statut:
ppublish
Résumé
Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0
Identifiants
pubmed: 34724078
doi: 10.1007/s00122-021-03982-0
pii: 10.1007/s00122-021-03982-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
537-552Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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