Combining pedigree and genomic information to improve prediction quality : an example in sorghum
Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information o...
| Main Authors: | , , , , , |
|---|---|
| Format: | Artículo |
| Language: | Inglés |
| Published: |
Springer
2019
|
| Subjects: | |
| Online Access: | https://link.springer.com/article/10.1007/s00122-019-03337-w http://hdl.handle.net/20.500.12123/6325 https://doi.org/10.1007/s00122-019-03337-w |
| _version_ | 1855483789951631360 |
|---|---|
| author | Velazco, Julio Gabriel Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. Van Eeuwijk, Fred A. |
| author_browse | Hunt, Colleen H. Jordan, David R. Mace, Emma S. Malosetti, Marcos Van Eeuwijk, Fred A. Velazco, Julio Gabriel |
| author_facet | Velazco, Julio Gabriel Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. Van Eeuwijk, Fred A. |
| author_sort | Velazco, Julio Gabriel |
| collection | INTA Digital |
| description | Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers. |
| format | Artículo |
| id | INTA6325 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA63252019-11-19T13:02:52Z Combining pedigree and genomic information to improve prediction quality : an example in sorghum Velazco, Julio Gabriel Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. Van Eeuwijk, Fred A. Sorghum almum Sorgos Valor Genético Genómica Pedigrí Pedigree livestock Rendimiento Evaluación Breeding Value Genomics Yields Evaluation Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers. EEA Pergamino Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia Fil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia Fil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda 2019-11-19T12:57:00Z 2019-11-19T12:57:00Z 2019-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://link.springer.com/article/10.1007/s00122-019-03337-w http://hdl.handle.net/20.500.12123/6325 0040-5752 1432-2242 https://doi.org/10.1007/s00122-019-03337-w eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Springer Theoretical and Applied Genetics 132 (7) : 2055–2067. (July 2019) |
| spellingShingle | Sorghum almum Sorgos Valor Genético Genómica Pedigrí Pedigree livestock Rendimiento Evaluación Breeding Value Genomics Yields Evaluation Velazco, Julio Gabriel Malosetti, Marcos Hunt, Colleen H. Mace, Emma S. Jordan, David R. Van Eeuwijk, Fred A. Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title | Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title_full | Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title_fullStr | Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title_full_unstemmed | Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title_short | Combining pedigree and genomic information to improve prediction quality : an example in sorghum |
| title_sort | combining pedigree and genomic information to improve prediction quality an example in sorghum |
| topic | Sorghum almum Sorgos Valor Genético Genómica Pedigrí Pedigree livestock Rendimiento Evaluación Breeding Value Genomics Yields Evaluation |
| url | https://link.springer.com/article/10.1007/s00122-019-03337-w http://hdl.handle.net/20.500.12123/6325 https://doi.org/10.1007/s00122-019-03337-w |
| work_keys_str_mv | AT velazcojuliogabriel combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum AT malosettimarcos combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum AT huntcolleenh combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum AT maceemmas combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum AT jordandavidr combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum AT vaneeuwijkfreda combiningpedigreeandgenomicinformationtoimprovepredictionqualityanexampleinsorghum |