Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data
When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.
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| Main Authors: | , , , , , , , |
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| Format: | Genotypic data biblioteca |
| Language: | English |
| Published: |
CIMMYT Research Data & Software Repository Network
2021
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| Subjects: | Agricultural Sciences, Wheat, Triticum aestivum, Agricultural research, Plant Breeding, Genotypes, |
| Online Access: | https://hdl.handle.net/11529/10548635 |
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dat-cimmyt-11529105486352021-12-11T02:00:15ZReplication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding datahttps://hdl.handle.net/11529/10548635Lopez-Cruz, MarcoDreisigacker, SusanneCrespo-Herrera, LeonardoBentley, Alison R.Singh, RaviMondal, SuchismitaPerez-Rodriguez, PaulinoCrossa, JoseCIMMYT Research Data & Software Repository NetworkWhen genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.Agricultural SciencesWheatTriticum aestivumAgricultural researchPlant BreedingGenotypesEnglish2021Dreher, KateForeign, Commonwealth and Development Office (FCDO)Foundation for Research Levy on Agricultural Products (FFL)USDA National Institute of Food and AgricultureAccelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)Agricultural Agreement Research Fund (JA)CGIARBiometrics and Statistics Unit (BSU)United States Agency for International Development (USAID)Bill and Melinda Gates Foundation (BMGF)Global Wheat Program (GWP)Genetic Resources Program (GRP)CGIAR Research Program on Wheat (WHEAT)Genotypic dataPhenotypic dataExperimental data |
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México |
| countrycode |
MX |
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| databasecode |
dat-cimmyt |
| tag |
biblioteca |
| region |
America del Norte |
| libraryname |
Centro Internacional de Mejoramiento de Maíz y Trigo |
| language |
English |
| topic |
Agricultural Sciences Wheat Triticum aestivum Agricultural research Plant Breeding Genotypes Agricultural Sciences Wheat Triticum aestivum Agricultural research Plant Breeding Genotypes |
| spellingShingle |
Agricultural Sciences Wheat Triticum aestivum Agricultural research Plant Breeding Genotypes Agricultural Sciences Wheat Triticum aestivum Agricultural research Plant Breeding Genotypes Lopez-Cruz, Marco Dreisigacker, Susanne Crespo-Herrera, Leonardo Bentley, Alison R. Singh, Ravi Mondal, Suchismita Perez-Rodriguez, Paulino Crossa, Jose Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| description |
When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article. |
| author2 |
Dreher, Kate |
| author_facet |
Dreher, Kate Lopez-Cruz, Marco Dreisigacker, Susanne Crespo-Herrera, Leonardo Bentley, Alison R. Singh, Ravi Mondal, Suchismita Perez-Rodriguez, Paulino Crossa, Jose |
| format |
Genotypic data |
| topic_facet |
Agricultural Sciences Wheat Triticum aestivum Agricultural research Plant Breeding Genotypes |
| author |
Lopez-Cruz, Marco Dreisigacker, Susanne Crespo-Herrera, Leonardo Bentley, Alison R. Singh, Ravi Mondal, Suchismita Perez-Rodriguez, Paulino Crossa, Jose |
| author_sort |
Lopez-Cruz, Marco |
| title |
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| title_short |
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| title_full |
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| title_fullStr |
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| title_full_unstemmed |
Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| title_sort |
replication data for: sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data |
| publisher |
CIMMYT Research Data & Software Repository Network |
| publishDate |
2021 |
| url |
https://hdl.handle.net/11529/10548635 |
| work_keys_str_mv |
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| _version_ |
1778656972965412864 |