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Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits

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Abstract
China’s and Europe’s dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.
Keywords
GWAS, alfalfa, genetic diversity, genomic prediction, phenology, LINKAGE DISEQUILIBRIUM, POPULATION-STRUCTURE, R-PACKAGE, AUTOTETRAPLOID ALFALFA, REGULARIZATION PATHS, MOLECULAR MARKERS, PENALIZED SPLINES, WINTER HARDINESS, FALL DORMANCY, MIXED-MODEL

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MLA
Pégard, Marie, et al. “Genome-Wide Genotyping Data Renew Knowledge on Genetic Diversity of a Worldwide Alfalfa Collection and Give Insights on Genetic Control of Phenology Traits.” FRONTIERS IN PLANT SCIENCE, vol. 14, 2023, doi:10.3389/fpls.2023.1196134.
APA
Pégard, M., Barre, P., Delaunay, S., Surault, F., Karagić, D., Milić, D., … Julier, B. (2023). Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits. FRONTIERS IN PLANT SCIENCE, 14. https://doi.org/10.3389/fpls.2023.1196134
Chicago author-date
Pégard, Marie, Philippe Barre, Sabrina Delaunay, Fabien Surault, Djura Karagić, Dragan Milić, Miroslav Zorić, Tom Ruttink, and Bernadette Julier. 2023. “Genome-Wide Genotyping Data Renew Knowledge on Genetic Diversity of a Worldwide Alfalfa Collection and Give Insights on Genetic Control of Phenology Traits.” FRONTIERS IN PLANT SCIENCE 14. https://doi.org/10.3389/fpls.2023.1196134.
Chicago author-date (all authors)
Pégard, Marie, Philippe Barre, Sabrina Delaunay, Fabien Surault, Djura Karagić, Dragan Milić, Miroslav Zorić, Tom Ruttink, and Bernadette Julier. 2023. “Genome-Wide Genotyping Data Renew Knowledge on Genetic Diversity of a Worldwide Alfalfa Collection and Give Insights on Genetic Control of Phenology Traits.” FRONTIERS IN PLANT SCIENCE 14. doi:10.3389/fpls.2023.1196134.
Vancouver
1.
Pégard M, Barre P, Delaunay S, Surault F, Karagić D, Milić D, et al. Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits. FRONTIERS IN PLANT SCIENCE. 2023;14.
IEEE
[1]
M. Pégard et al., “Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits,” FRONTIERS IN PLANT SCIENCE, vol. 14, 2023.
@article{01HN02K2N5NKQAKXT5AR6KNSZC,
  abstract     = {{China’s and Europe’s dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.}},
  articleno    = {{1196134}},
  author       = {{Pégard, Marie and Barre, Philippe and Delaunay, Sabrina and Surault, Fabien and Karagić, Djura and Milić, Dragan and Zorić, Miroslav and Ruttink, Tom and Julier, Bernadette}},
  issn         = {{1664-462X}},
  journal      = {{FRONTIERS IN PLANT SCIENCE}},
  keywords     = {{GWAS,alfalfa,genetic diversity,genomic prediction,phenology,LINKAGE DISEQUILIBRIUM,POPULATION-STRUCTURE,R-PACKAGE,AUTOTETRAPLOID ALFALFA,REGULARIZATION PATHS,MOLECULAR MARKERS,PENALIZED SPLINES,WINTER HARDINESS,FALL DORMANCY,MIXED-MODEL}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits}},
  url          = {{http://doi.org/10.3389/fpls.2023.1196134}},
  volume       = {{14}},
  year         = {{2023}},
}

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