Abstract
Key message
Genomic predictions across environments and within populations resulted in moderate to high accuracies but across-population genomic prediction should not be considered in wheat for small population size.
Abstract
Genomic selection (GS) is a marker-based selection suggested to improve the genetic gain of quantitative traits in plant breeding programs. We evaluated the effects of training population (TP) composition, cross-validation design, and genetic relationship between the training and breeding populations on the accuracy of GS in spring wheat (Triticum aestivum L.). Two populations of 231 and 304 spring hexaploid wheat lines that were phenotyped for six agronomic traits and genotyped with the wheat 90 K array were used to assess the accuracy of seven GS models (RR-BLUP, G-BLUP, BayesB, BL, RKHS, GS + de novo GWAS, and reaction norm) using different cross-validation designs. BayesB outperformed the other models for within-population genomic predictions in the presence of few quantitative trait loci (QTL) with large effects. However, including fixed-effect marker covariates gave better performance for an across-population prediction when the same QTL underlie traits in both populations. The accuracy of prediction was highly variable based on the cross-validation design, which suggests the importance to use a design that resembles the variation within a breeding program. Moderate to high accuracies were obtained when predictions were made within populations. In contrast, across-population genomic prediction accuracies were very low, suggesting that the evaluated models are not suitable for prediction across independent populations. On the other hand, across-environment prediction and forward prediction designs using the reaction norm model resulted in moderate to high accuracies, suggesting that GS can be applied in wheat to predict the performance of newly developed lines and lines in incomplete field trials.
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Acknowledgements
This study was conducted as part of the Canadian Triticum Applied Genomics (CTAG2) project and funded by Genome Canada (Grant number: 8310), Saskatchewan Ministry of Agriculture, Western Grains Research Foundation, Saskatchewan Wheat Development Commission, and Government of Saskatchewan. We would like to acknowledge the technical assistance from the Durum Wheat Breeding and Genetics field and molecular laboratory staff at the University of Saskatchewan.
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TAH designed the experiment, generated phenotypic and marker data, performed all analyses, and wrote the manuscript. SW, AN, and JMC edited the manuscript. PJH designed the experiment, developed and maintained early generation of the breeding population, and edited the manuscript. RDC and REK collected phenotypic data, and edited the manuscript. CJP acquired funding, designed the experiment, supervised the project, collected phenotypic data and edited the manuscript.
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Communicated by Hiroyoshi Iwata.
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John M. Clarke: Deceased 01 February 2020.
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122_2020_3703_MOESM2_ESM.csv
Online Resource 2: Markers fitted as fixed effects for genomic predictions within and across populations using GS + de novo GWAS model (CSV 114 kb)
Online Resource 5: Frequency distributions of agronomic traits in the training population (PDF 3 kb)
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Online Resource 6: Frequency distribution of agronomic traits in the breeding population. The values for the check cultivars (AC Barrie, CDC Utmost, CDC Plentiful, and Pasteur) are indicated with arrows (PDF 29 kb)
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Online Resource 7: Quantile–quantile (Q–Q) plots of the association analysis using Mixed Linear Model with Kinship matrix and five marker-derived principal components (PDF 416 kb)
122_2020_3703_MOESM9_ESM.docx
Online Resource 9: Summary of environment specific QTL identified for six agronomic traits in the breeding population (DOCX 26 kb)
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Haile, T.A., Walkowiak, S., N’Diaye, A. et al. Genomic prediction of agronomic traits in wheat using different models and cross-validation designs. Theor Appl Genet 134, 381–398 (2021). https://doi.org/10.1007/s00122-020-03703-z
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DOI: https://doi.org/10.1007/s00122-020-03703-z