Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat
Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2022
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| Online Access: | https://hdl.handle.net/10568/130122 |
| _version_ | 1855515072524189696 |
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| author | Shahi, Dipendra Jia Guo Pradhan, Sumit Afridi, Jahangir Khan Avci, Muhsin Khan, Naeem McBreen, Jordan Guihua Bai Reynolds, Matthew P. Foulkes, John Michael Babar, Md Ali |
| author_browse | Afridi, Jahangir Khan Avci, Muhsin Babar, Md Ali Foulkes, John Michael Guihua Bai Jia Guo Khan, Naeem McBreen, Jordan Pradhan, Sumit Reynolds, Matthew P. Shahi, Dipendra |
| author_facet | Shahi, Dipendra Jia Guo Pradhan, Sumit Afridi, Jahangir Khan Avci, Muhsin Khan, Naeem McBreen, Jordan Guihua Bai Reynolds, Matthew P. Foulkes, John Michael Babar, Md Ali |
| author_sort | Shahi, Dipendra |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops. |
| format | Journal Article |
| id | CGSpace130122 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1301222025-11-06T13:02:57Z Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat Shahi, Dipendra Jia Guo Pradhan, Sumit Afridi, Jahangir Khan Avci, Muhsin Khan, Naeem McBreen, Jordan Guihua Bai Reynolds, Matthew P. Foulkes, John Michael Babar, Md Ali breeding canopy fruiting genetic gain genotypes genotyping harvest index phenotypes single nucleotide polymorphism spikes vegetation wheat genetics biotechnology Background: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. Results: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. Conclusions: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops. 2022-04-12 2023-04-23T10:53:53Z 2023-04-23T10:53:53Z Journal Article https://hdl.handle.net/10568/130122 en Open Access application/pdf Springer Shahi, D., Guo, J., Pradhan, S., Khan, J., AVCI, M., Khan, N., McBreen, J., Bai, G., Reynolds, M., Foulkes, J., & Babar, M. A. (2022). Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat. BMC Genomics, 23(1). https://doi.org/10.1186/s12864-022-08487-8 |
| spellingShingle | breeding canopy fruiting genetic gain genotypes genotyping harvest index phenotypes single nucleotide polymorphism spikes vegetation wheat genetics biotechnology Shahi, Dipendra Jia Guo Pradhan, Sumit Afridi, Jahangir Khan Avci, Muhsin Khan, Naeem McBreen, Jordan Guihua Bai Reynolds, Matthew P. Foulkes, John Michael Babar, Md Ali Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title | Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title_full | Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title_fullStr | Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title_full_unstemmed | Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title_short | Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat |
| title_sort | multi trait genomic prediction using in season physiological parameters increases prediction accuracy of complex traits in us wheat |
| topic | breeding canopy fruiting genetic gain genotypes genotyping harvest index phenotypes single nucleotide polymorphism spikes vegetation wheat genetics biotechnology |
| url | https://hdl.handle.net/10568/130122 |
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