Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability
To secure the world’s food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least...
| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Language: | Inglés |
| Published: |
Cold Spring Harbor Laboratory
2019
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| Online Access: | https://hdl.handle.net/10568/164608 |
| _version_ | 1855515477136113664 |
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| author | Orhobor, Oghenejokpeme I. Alexandrov, Nickolai N. Chebotarov, Dmytro Kretzschmar, Tobias McNally, Kenneth L. Sanciangco, Millicent D. King, Ross D. |
| author_browse | Alexandrov, Nickolai N. Chebotarov, Dmytro King, Ross D. Kretzschmar, Tobias McNally, Kenneth L. Orhobor, Oghenejokpeme I. Sanciangco, Millicent D. |
| author_facet | Orhobor, Oghenejokpeme I. Alexandrov, Nickolai N. Chebotarov, Dmytro Kretzschmar, Tobias McNally, Kenneth L. Sanciangco, Millicent D. King, Ross D. |
| author_sort | Orhobor, Oghenejokpeme I. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | To secure the world’s food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts. |
| format | Preprint |
| id | CGSpace164608 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Cold Spring Harbor Laboratory |
| publisherStr | Cold Spring Harbor Laboratory |
| record_format | dspace |
| spelling | CGSpace1646082024-12-19T14:11:54Z Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability Orhobor, Oghenejokpeme I. Alexandrov, Nickolai N. Chebotarov, Dmytro Kretzschmar, Tobias McNally, Kenneth L. Sanciangco, Millicent D. King, Ross D. To secure the world’s food supply it is essential that we improve our knowledge of the genetic underpinnings of complex agronomic traits. In this paper, we report our findings from performing trait prediction and association mapping using marker stability in diverse rice landraces. We used the least absolute shrinkage and selection operator as our marker selection algorithm, and considered twelve real agronomic traits and a hundred simulated traits using a population with approximately a hundred thousand markers. For trait prediction, we considered several statistical/machine learning methods. We found that some of the methods considered performed best when preselected markers using marker stability were used. However, our results also show that one might need to make a trade-off between model size and performance for some learning methods. For association mapping, we compared marker stability to the genome-wide efficient mixed-model analysis (GEMMA), and for the simulated traits, we found that marker stability significantly outperforms GEMMA. For the real traits, marker stability successfully identifies multiple associated markers, which often entail those selected by GEMMA. Further analysis of the markers selected for the real traits using marker stability showed that they are located in known quantitative trait loci (QTL) using the QTL Annotation Rice Online database. Furthermore, co-functional network prediction of the selected markers using RiceNet v2 also showed association to known controlling genes. We argue that a wide adoption of the marker stability approach for the prediction of agronomic traits and association mapping could improve global rice breeding efforts. 2019-10-15 2024-12-19T12:54:06Z 2024-12-19T12:54:06Z Preprint https://hdl.handle.net/10568/164608 en Cold Spring Harbor Laboratory Orhobor, Oghenejokpeme I.; Alexandrov, Nickolai N.; Chebotarov, Dmytro; Kretzschmar, Tobias; McNally, Kenneth L.; Sanciangco, Millicent D. and King, Ross D. 2019. Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability. bioRxiv, 24 pages |
| spellingShingle | Orhobor, Oghenejokpeme I. Alexandrov, Nickolai N. Chebotarov, Dmytro Kretzschmar, Tobias McNally, Kenneth L. Sanciangco, Millicent D. King, Ross D. Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title | Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title_full | Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title_fullStr | Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title_full_unstemmed | Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title_short | Predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| title_sort | predicting agronomic traits and associated genomic regions in diverse rice landraces using marker stability |
| url | https://hdl.handle.net/10568/164608 |
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