Abstract
The prediction accuracies of genomic selection depend on several factors, including the genetic architecture of target traits, the number of traits considered at a given time, and the statistical models. Here, we assessed the potential of single-trait (ST) and multi-trait (MT) genomic prediction models for durum wheat on yield and quality traits using a breeding panel (BP) of 170 varieties and advanced breeding lines, and a doubled-haploid (DH) population of 154 lines. The two populations were genotyped with the Infinium iSelect 90K SNP assay and phenotyped for various traits. Six ST-GS models (RR-BLUP, G-BLUP, BayesA, BayesB, Bayesian LASSO, and RKHS) and three MT prediction approaches (MT-BayesA, MT-Matrix, and MT-SI approaches which use economic selection index as a trait value) were applied for predicting yield, protein content, gluten index, and alveograph measures. The ST prediction accuracies ranged from 0.5 to 0.8 for the various traits and models and revealed comparable prediction accuracies for most of the traits in both populations, except BayesA and BayesB, which better predicted gluten index, tenacity, and strength in the DH population. The MT-GS models were more accurate than the ST-GS models only for grain yield in the BP. Using BP as a training set to predict the DH population resulted in poor predictions. Overall, all the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but we recommend the simple computational models RR-BLUP or G-BLUP for predicating single trait and MT-SI for predicting yield and protein simultaneously.
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Abbreviations
- BL:
-
Bayesian Least Absolute Shrinkage, and Selection Operator
- BLUP:
-
Best linear unbiased prediction
- BP:
-
Breeding panel
- CIMMYT:
-
International Maize and Wheat Improvement Center
- CV:
-
Cross-validation
- DAPC:
-
Discriminant analysis of principal components
- DH:
-
Doubled-haploid
- G-BLUP:
-
Genomic best linear unbiased prediction
- G × E:
-
Genotype by environment interaction
- GEBV:
-
Genomic estimated breeding value
- GI:
-
Gluten index
- GPC:
-
Grain protein content
- GS:
-
Genomic selection
- GYLD:
-
Grain yield
- L:
-
Average abscissas at rupture (extensibility)
- LD:
-
Linkage disequilibrium
- MAF:
-
Minor allele frequency
- MT:
-
Multi-traits
- NIR:
-
Near infra-red
- P:
-
Dough overpressure (tenacity)
- QTL:
-
Quantitative trait loci
- RKHS:
-
Reproducing kernel Hilbert space
- RR-BLUP:
-
Ridge regression best linear unbiased prediction
- SI:
-
Selection index
- SNP:
-
Single-nucleotide polymorphism
- ST:
-
Single trait
- ST-GS:
-
Single-trait genomic selection
- TP:
-
Training population
- VP:
-
Validation population
- W:
-
Deformation energy (strength)
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Acknowledgements
We gratefully acknowledge funding of this research by the Genome Canada and Genome Prairie as part of the Canadian Triticum Applied Genomics (CTAG) project. Additional funding provided by the Saskatchewan Ministry of Agriculture, Western Grains Research Foundation, Agriculture and Agri-Food Canada, and the Natural Sciences and Engineering Research Council of Canada is also gratefully acknowledged. We also acknowledge the technical assistance of Shawn Yates at the Agriculture and Agri-Food Canada Research and Development Centre, Swift Current, for curation of the database from which the breeding panel data were drawn. The data from these trials were conducted under the auspices of the Prairie Recommending Committee for Wheat, Rye, and Triticale, and we acknowledge them for permission to use the data presented for the breeding panel, and the many people who conducted the field trials. We acknowledge the coordinators of these trials, including Dr. D. Leisle, Agriculture and Agri-Food Canada, Winnipeg (retired), from 1961 to 1994, Dr. J. M. Clarke, from 1995 to 2007, Dr. A.K. Singh from 2008 to 2012, and Dr. R.M. DePauw in 2013, all from Agriculture and Agri-Food Canada. We also acknowledge those responsible for end-use quality analysis of these trials from the Grain Research Laboratory of the Canadian Grain Commission. Lastly, we acknowledge Krysta Wiebe, Jennifer Ens, and Justin Coulson for support in generation of the molecular data used in this project.
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Fran Clarke is retired.
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Haile, J.K., N’Diaye, A., Clarke, F. et al. Genomic selection for grain yield and quality traits in durum wheat. Mol Breeding 38, 75 (2018). https://doi.org/10.1007/s11032-018-0818-x
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DOI: https://doi.org/10.1007/s11032-018-0818-x