| Download | - View final version: Evaluation of genomic prediction for Fusarium head blight resistance with a multi-parental population (PDF, 982 KiB)
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| DOI | Resolve DOI: https://doi.org/10.3390/biology10080756 |
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| Author | Search for: Zhang, Wentao1; Search for: Boyle, Kerry1; Search for: Brule-Babel, Anita; Search for: Fedak, George; Search for: Gao, Peng1; Search for: Djama, Zeinab Robleh; Search for: Polley, Brittany1; Search for: Cuthbert, Richard; Search for: Randhawa, Harpinder; Search for: Graf, Robert; Search for: Jiang, Fengying1; Search for: Eudes, Francois; Search for: Fobert, Pierre R.1 |
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| Affiliation | - National Research Council of Canada. Aquatic and Crop Resource Development
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| Format | Text, Article |
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| Subject | fusarium head blight; prediction accuracy; genomic selection; model |
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| Abstract | Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application. |
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| Publication date | 2021-08-06 |
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| Publisher | MDPI |
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| Licence | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRC-ACRD-PDB-089 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 61645807-5288-48b2-815f-74add08923cd |
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| Record created | 2021-09-28 |
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| Record modified | 2021-09-28 |
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