Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding

Abstract

The implementation of genomic selection in breeding programs can be recommended for hybrid and line breeding in wheat. High prediction accuracies from genomic selection (GS) were reported for grain yield in wheat asking for the elaboration of efficient breeding strategies applying GS. Our objectives were therefore, (1) to optimize the number of lines, locations and testers in different multi-stage breeding strategies with and without GS, (2) to elaborate the most efficient breeding strategy based on the selection gain and its standard deviation, and (3) to investigate the potential of GS to improve the relative efficiency of hybrid versus line breeding in wheat. We used the open source software package "selectiongain" to optimize the allocation of resources in different breeding strategies by predicting the expected selection gain for a fixed budget. Classical two-stage phenotypic selection was compared with three GS breeding strategies for line and hybrid breeding in wheat. The ranking of the alternative breeding strategies varied largely in dependence of the GS prediction accuracy. Fast-track breeding strategies based solely on GS were only advantageous for high GS prediction accuracies that is >0.50 and >0.65 for hybrid and line breeding, respectively. However, a GS prediction accuracy across breeding cycles of 0.3 or even less must be assumed as realistic for grain yield in wheat. For this low GS prediction accuracy, the use of GS is advantageous for line but especially for hybrid breeding in wheat. Furthermore, the use of GS in hybrid wheat breeding increased the relative efficiency of hybrid versus line breeding and, thus, might be an important pillar for the establishment of hybrid wheat.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

“…Depending on the breeding scheme it has been suggested to conduct positive genomic selection for the best lines (Bassi et al 2016) or discarding the worst lines by negative selection (Longin et al 2015), while we observed no difference of any genomic selection approach to correctly identify lines from either tail of the distribution. Nevertheless, these considerations are valid for single traits only and it is generally not recommended to sequentially select for one trait after another as a lower gain in selection is expected by such tandem selection (Hazel and Lush 1942).…”

“…The broad range of possible applications has led to different strategies concerning the implementation of genomic selection into line breeding schemes (Heffner et al 2010; Longin et al 2015; Spindel et al 2015; Marulanda et al 2016), though it is generally suggested that a genomic selection step is integrated before multi-environment trials are being conducted. Breeders might thus consider the replacement of traditional preliminary yield trials by genomic selection to spare phenotyping costs or even integrating them into the genomic selection framework as they deliver a first insight into the future performance of the putative varietal candidates (Endelman et al 2014).…”

Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data

“…Previous model studies in wheat and maize suggested a threshold for the prediction accuracy of 0.5 [80, 81]. We chose two important traits, oil content and protein content, to illustrate the size of the training population, the number of environments, and the marker density required to reach a prediction accuracy of 0.5 for the bi-parental population.…”

QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

“…Epistatic QTLs and epiQTLs will also be discovered in future to be used for molecular breeding in wheat. There is also a need to explore genomic selection (GS) for drought tolerance, which has already been used for the improvement of other traits, particularly disease resistance in wheat [120][121][122].…”