Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Description
2.2. Plant Genetic Material and Experimental Design
2.3. Traits Measurement
2.4. Phenotypic Data Analysis
2.5. Genotypic Data Analysis
2.6. Prediction Models
2.6.1. Multi-Environment Genomic Best Linear Unbiased Predictor (MGBLUP) Model
2.6.2. Bayesian Multi-Trait Multi-Environment (BMTME) Model
2.6.3. Bayesian Multi-Output Regressor Stacking (BMORS) Model
2.6.4. Deep Learning (DL) Models
2.7. Model Evaluation
2.8. Software Implementation
2.9. Data Availability
3. Results
3.1. Descriptive Statistics
3.2. Prediction Accuracy
3.3. Response to Selection
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site | Year | Coordinates | Soil Type 1 |
---|---|---|---|
Citra | 2016–2017 | 29°24′18″ N 82°10′22″ W | Well-drained sandy soil with loamy subsoil at 20–80 inches |
2017–2018 | 29°24′32″ N 82°10′46′’ W | ||
Quincy | 2016–2017 | 30°33′04″ N 84°35′51″ W | Well-drained loamy soils |
2017–2018 | 30°32′45″ N 84°35′46″ W |
Trait | BLUE | SE | H2 | CV | Min | Max | |
---|---|---|---|---|---|---|---|
Citra 2017 | GY | 2.0 | 0.1 | 0.71 | 28.3 | 0.3 | 4.5 |
HI | 30.5 | 0.8 | 0.78 | 17.8 | 16 | 52 | |
SF | 63.9 | 1.7 | 0.38 | 27.2 | 12 | 142 | |
TGW | 34.7 | 0.4 | 0.48 | 10.8 | 24 | 48 | |
Citra 2018 | GY | 3.8 | 0.1 | 0.80 | 11.5 | 1.0 | 7.0 |
HI | 37.4 | 0.4 | 0.74 | 6.7 | 20 | 48 | |
SF | 98.3 | 1.2 | 0.68 | 9.5 | 62 | 161 | |
TGW | 34.1 | 0.4 | 0.87 | 5.1 | 19 | 46 | |
Quincy 2017 | GY | 3.3 | 0.1 | 0.36 | 16.6 | 1.5 | 5.6 |
HI | 34.3 | 0.4 | 0.43 | 12.6 | 20 | 47 | |
SF | 83.2 | 1.7 | 0.22 | 25.1 | 34 | 148 | |
TGW | 39.4 | 0.3 | 0.58 | 7.4 | 26 | 50 | |
Quincy 2018 | GY | 5.3 | 0.1 | 0.24 | 18.4 | 2.1 | 8.8 |
HI | 42.7 | 0.3 | 0.26 | 9.8 | 28 | 54 | |
SF | 94.6 | 1.4 | 0.32 | 15.6 | 52 | 158 | |
TGW | 40.9 | 0.4 | 0.44 | 9.7 | 30 | 54 |
GY | HI | SF | TGW | |
---|---|---|---|---|
GY | 0.67 | 0.17 | 0.18 | |
HI | 0.76 | 0.17 | 0.10 | |
SF | 0.36 | 0.30 | −0.32 | |
TGW | 0.33 | 0.24 | −0.23 |
Quincy 2017 | Citra 2017 | Quincy 2018 | Citra 2018 | |
---|---|---|---|---|
Quincy 2017 | 0.24 | 0.26 | 0.19 | |
Citra 2017 | 0.19 | 0.17 | ||
Quincy 2018 | 0.16 |
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Guo, J.; Khan, J.; Pradhan, S.; Shahi, D.; Khan, N.; Avci, M.; Mcbreen, J.; Harrison, S.; Brown-Guedira, G.; Murphy, J.P.; et al. Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes. Genes 2020, 11, 1270. https://doi.org/10.3390/genes11111270
Guo J, Khan J, Pradhan S, Shahi D, Khan N, Avci M, Mcbreen J, Harrison S, Brown-Guedira G, Murphy JP, et al. Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes. Genes. 2020; 11(11):1270. https://doi.org/10.3390/genes11111270
Chicago/Turabian StyleGuo, Jia, Jahangir Khan, Sumit Pradhan, Dipendra Shahi, Naeem Khan, Muhsin Avci, Jordan Mcbreen, Stephen Harrison, Gina Brown-Guedira, Joseph Paul Murphy, and et al. 2020. "Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes" Genes 11, no. 11: 1270. https://doi.org/10.3390/genes11111270