Abstract
End-use value of wheat flour depends strongly on the concentration and composition of storage proteins, namely the gliadins and glutenins. As protein concentration in wheat grain is negatively correlated with grain yield, monitoring the gliadin to glutenin ratio is a mean to maintain end-use quality in modern varieties. However, the measurement of this ratio is expensive and time consuming. As genomic selection (GS) has proved very successful for traits controlled by many Quantitative Trait Loci and is already used for breeding, we decided to apply it to the gliadin to glutenin ratio. Therefore, we phenotyped for this trait and genotyped with a 420,000 SNP (Single Nucleotide Polymorphism) array a set of 88 modern varieties and 325 core-collection varieties. A GS model taking into account the genotypic, environmental and genotype x environment interaction effects was tested. Its predictive ability depends on the composition of the training population (TP). Adding significant SNPs as fixed effects did not improve the predictive ability. However, we observed improvements by optimizing the TP with five methods based on relatedness between genotypes and obtained a maximum predictive ability of 0.62 and a minimum Root Mean Square Error of 0.056 for the gliadin to glutenin ratio. To conclude, our results are promising and strongly suggested that GS can be efficiently applied to the gliadin to glutenin ratio. In addition, genotypes phenotyped and genotyped in previous breeding generations could be useful to train the model.
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Data availability
Genotypic data are presented in additional supporting files. Phenotypic data are available upon request.
Code avaibility
The code developed for data analysis is available on request.
Abbreviations
- CaP:
-
Cappelle en Pévèle
- CC:
-
Core collection
- CF:
-
Clermont-Ferrand
- FDR:
-
False discovery rate
- G-BLUP:
-
Genomic best linear unbiased prediction
- Gli/Glu:
-
Gliadin to glutenin ratio
- GS:
-
Genomic selection
- GWAS:
-
Genome wide association study
- LM:
-
Le Moulon
- MAS:
-
Marker-assisted selection
- Or:
-
Orsonville
- PCoA:
-
Principal coordinates analysis
- QTL:
-
Quantitative trait locus
- REML:
-
Restricted maximum likelihood
- RMSE:
-
Root mean square error
- TP:
-
Training population
- VP:
-
Validation population
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Acknowledgements
The authors thank the wheat breeding team of Florimond Desprez and Agri-Obtentions, and F. X. Oury from the GDEC unit (INRAE) for cultivation of the BDul population. The work in experimental units of INRAE (Clermont-Ferrand, Le Moulon) is also gratefully acknowledged. The authors thank the Small Grain Cereals Biological Resources Centre at INRAE, Clermont-Ferrand (https://www6.clermont.inrae.fr/umr1095_eng/Organisation/Experimental-Infrastructure/Biological-Resources-Centre) for providing some seed samples. They thank the genotyping platform GENTYANE at INRAE Clermont-Ferrand (gentyane.clermont.inrae.fr) which has conducted the genotyping with the help of Mireille Dardevet. The authors also thank D. Alvarez and S. Perrochon for the analysis of seed storage protein composition. They are grateful to the Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul) for computing calculations for the G-BLUP + GWAS model and the optimizations of training populations. This work was supported by the French Funds to support Plant Breeding (FSOV) in the context of the project “B-DuL. In addition, this work used results obtained thanks to funding from the French Government managed by the Research National Agency (ANR) under the Investment for the Future programme (BreedWheat project ANR-10-BTBR-03), from FranceAgriMer and FSOV.
Funding
This work was supported by the French Funds to support Plant Breeding (FSOV) in the context of the project “B-DuL”. In addition, this work used results obtained thanks to funding from the French Government managed by the Research National Agency (ANR) under the Investment for the Future program (BreedWheat project ANR-10-BTBR-03), from FranceAgriMer and FSOV.
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PL, EP, JA, EGD, RR and CR wrote the manuscript. JA, EGD, and CR conceived and coordinated the project. PL and SB conducted statistical analysis. RR supervised the work on modelisation and method optimization. EP developed the markers.
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Lemeunier, P., Paux, E., Babi, S. et al. Training population optimization for genomic selection improves the predictive ability of a costly measure in bread wheat, the gliadin to glutenin ratio. Euphytica 218, 111 (2022). https://doi.org/10.1007/s10681-022-03062-4
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DOI: https://doi.org/10.1007/s10681-022-03062-4