Improving root characterisation for genomic prediction in cassava
Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the Int...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/109544 |
| _version_ | 1855541295075819520 |
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| author | Yonis, B.O. Carpio, D.P. del Wolfe, M. Jannink, Jean-Luc Kulakow, Peter A. Rabbi, Ismail Y. |
| author_browse | Carpio, D.P. del Jannink, Jean-Luc Kulakow, Peter A. Rabbi, Ismail Y. Wolfe, M. Yonis, B.O. |
| author_facet | Yonis, B.O. Carpio, D.P. del Wolfe, M. Jannink, Jean-Luc Kulakow, Peter A. Rabbi, Ismail Y. |
| author_sort | Yonis, B.O. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava. |
| format | Journal Article |
| id | CGSpace109544 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1095442025-11-11T11:05:34Z Improving root characterisation for genomic prediction in cassava Yonis, B.O. Carpio, D.P. del Wolfe, M. Jannink, Jean-Luc Kulakow, Peter A. Rabbi, Ismail Y. cassava african cassava mosaic virus processing plant diseases nigeria genomics phenotypes postharvest technology Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava. 2020 2020-09-18T14:36:21Z 2020-09-18T14:36:21Z Journal Article https://hdl.handle.net/10568/109544 en Open Access application/pdf Springer Yonis, B.O., del Carpio, D.P., Wolfe, M., Jannink, J.L., Kulakow, P. & Rabbi, I. (2020). Improving root characterisation for genomic prediction in cassava. Scientific Reports, 10(1), 1-12. |
| spellingShingle | cassava african cassava mosaic virus processing plant diseases nigeria genomics phenotypes postharvest technology Yonis, B.O. Carpio, D.P. del Wolfe, M. Jannink, Jean-Luc Kulakow, Peter A. Rabbi, Ismail Y. Improving root characterisation for genomic prediction in cassava |
| title | Improving root characterisation for genomic prediction in cassava |
| title_full | Improving root characterisation for genomic prediction in cassava |
| title_fullStr | Improving root characterisation for genomic prediction in cassava |
| title_full_unstemmed | Improving root characterisation for genomic prediction in cassava |
| title_short | Improving root characterisation for genomic prediction in cassava |
| title_sort | improving root characterisation for genomic prediction in cassava |
| topic | cassava african cassava mosaic virus processing plant diseases nigeria genomics phenotypes postharvest technology |
| url | https://hdl.handle.net/10568/109544 |
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