Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits
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.
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Frontiers Media S.A.
2023
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Alfalfa, Genomic Prediction, MEDICAGO SATIVA, GENETIC DIVERSITY (AS RESOURCE), PHENOLOGY, GENOME-WIDE ASSOCIATION STUDIES, BREEDING PROGRAMMES, Institutional, |
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dig-cimmyt-10883-227082023-10-19T16:06:36Z Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits Pégard, M. Barre, P. Delaunay, S. Surault, F. Karagic, D. Milic, D. Zoric, M. Ruttink, T. Julier, B. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional 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. 2023-09-27T00:00:16Z 2023-09-27T00:00:16Z 2023 Article Published Version https://hdl.handle.net/10883/22708 10.3389/fpls.2023.1196134 English https://www.frontiersin.org/articles/10.3389/fpls.2023.1196134/full#supplementary-material CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Switzerland Frontiers Media S.A. 14 1664-462X Frontiers in Plant Science 1196134 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional Pégard, M. Barre, P. Delaunay, S. Surault, F. Karagic, D. Milic, D. Zoric, M. Ruttink, T. Julier, B. Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
description |
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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Alfalfa Genomic Prediction MEDICAGO SATIVA GENETIC DIVERSITY (AS RESOURCE) PHENOLOGY GENOME-WIDE ASSOCIATION STUDIES BREEDING PROGRAMMES Institutional |
author |
Pégard, M. Barre, P. Delaunay, S. Surault, F. Karagic, D. Milic, D. Zoric, M. Ruttink, T. Julier, B. |
author_facet |
Pégard, M. Barre, P. Delaunay, S. Surault, F. Karagic, D. Milic, D. Zoric, M. Ruttink, T. Julier, B. |
author_sort |
Pégard, M. |
title |
Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
title_short |
Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
title_full |
Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
title_fullStr |
Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
title_full_unstemmed |
Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
title_sort |
genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://hdl.handle.net/10883/22708 |
work_keys_str_mv |
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