Abstract
BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64–70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between − 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was “superior” to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.
Highlights
Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals
Effect of marker number and training population size Average number of markers used for genomic selection (GS) for each subset (SS) were 820 (SS0.15), 540 (SS0.10), and 270 (SS0.05) single nucleotide polymorphism (SNP)
Prediction accuracies for grain yield increased from 0.33 to 0.56 when SS0.10 was used for predictions (Fig. 1; Additional file 1: Table S1)
Summary
Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. GS is a molecular breeding tool that predicts genomic estimated breeding values of individuals with only genotypic information available through prediction models constructed based on a training population with genome-wide marker and phenotypic data available [2]. In soft red winter wheat, GS studies have been conducted for Fusarium head blight (FHB) resistance [14], grain yield and stability traits [15], yield, softness equivalence, flour yield [16], grain yield, plant height, heading date, and flour quality traits [17], and normalized difference vegetative index (NDVI) [18]. Factors affecting GS accuracy include gene effects, genetic composition of the training population (TP), level of linkage disequilibrium, marker density, statistical models, number of quantitative trait loci (QTL), relationship between TP and the validation population (VP) or selection candidates, TP size, and trait heritability [19,20,21]
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