This work focuses on the evaluation of a bagging EB method in terms of its ability to select a subset of QTL-related markers for accurate EBV prediction. Experiments were performed on several simulated and real datasets consisting of SNP genotypes and phenotypes. The simulated datasets modeled different dominance levels and different levels of background noises.
Our results show that the bagging EB method is able to detect most of the simulated QTL, even with large background noises. The average recall of QTL detection was $0.71$. When using the markers detected by the bagging EB method to predict EBVs, the prediction accuracy improved dramatically on the simulation datasets compared to using the entire set of markers. However, the prediction accuracy did not improve much when doing the same experiments on the two real datasets. The best accuracy of EBV prediction we achieved for the dairy dataset is 0.57 and the best accuracy for the beef dataset is 0.73.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/626 |
| Date | 11 1900 |
| Creators | Xu, Jiaofen |
| Contributors | Lin, Guohui (Computing Science), Stothard, Paul (Agricultural, Food and Nutritional Science), Moore, Stephen (Agricultural, Food and Nutritional Science), Goebel, Randy (Computing Science) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 2371929 bytes, application/pdf |
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