Genomic Prediction of Agronomic Traits in Common Bean (
common bean (Phaseolus vulgaris L.)
drought
genome-wide association studies (GWAS)
genomic selection
genotype × environment interactions
low phosphorus stress
plant breeding
Journal
Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200
Informations de publication
Date de publication:
2020
2020
Historique:
received:
16
03
2020
accepted:
18
06
2020
entrez:
11
8
2020
pubmed:
11
8
2020
medline:
11
8
2020
Statut:
epublish
Résumé
In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50-80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.
Identifiants
pubmed: 32774338
doi: 10.3389/fpls.2020.01001
pmc: PMC7381332
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1001Informations de copyright
Copyright © 2020 Keller, Ariza-Suarez, de la Hoz, Aparicio, Portilla-Benavides, Buendia, Mayor, Studer and Raatz.
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