Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits.

KEY MESSAGE: A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package "selectiongain" from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith-Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package "selectiongain" with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Candidate gene based association mapping in Fusarium culmorum for field quantitative pathogenicity and mycotoxin production in wheat.

BACKGROUND: Quantitative traits are common in nature, but quantitative pathogenicity has received only little attention in phytopathology. In this study, we used 100 Fusarium culmorum isolates collected from natural field environments to assess their variation for two quantitative traits, aggressiveness and deoxynivalenol (DON) production on wheat plants grown in four different field environments (location-year combinations). Seventeen Fusarium graminearum pathogenicity candidate genes were assessed for their effect on the aggressiveness and DON production of F. culmorum under field conditions. RESULTS: For both traits, genotypic variance among isolates was high and significant while the isolate-by-environment interaction was also significant, amounting to approximately half of the genotypic variance. Among the studied candidate genes, the mitogen-activated protein kinase (MAPK) HOG1 was found to be significantly associated with aggressiveness and deoxynivalenol (DON) production, explaining 10.29 and 6.05% of the genotypic variance, respectively. CONCLUSIONS: To the best of our knowledge, this is the first report of a protein kinase regulator explaining differences in field aggressiveness and mycotoxin production among individuals from natural populations of a plant pathogen.

Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale.

KEY MESSAGE: A breeding strategy with moderate nursery selection followed by genomic selection and one-stage phenotypic selection maximizes annual selection gain for grain yield across a wide range of hybrid breeding scenarios. Genomic selection (GS) is a promising method for the selection of quantitatively inherited traits but its most effective implementation in routine hybrid breeding schemes requires further research. We compared five breeding strategies and varied their available budget, the costs for doubled haploid (DH) line and hybrid seed production as well as variance components for grain yield in a wide range. In contrast to previous studies, we included a nursery selection for disease resistance just before GS on grain yield. The breeding strategy GSrapid with moderate nursery selection followed by one stage GS and one final stage with phenotypic selection on grain yield had the highest annual selection gain across all strategies, budgets, costs and variance components considered and we, therefore, highly recommend its use in hybrid breeding of cereals. Although selecting on traits not correlated with grain yield in the observation nursery, this selection reduced the selection gain of grain yield, especially in the breeding schemes with GS and for selected fractions smaller than 0.3. Owing to the very high number of test candidates entering breeding strategies with GS, the costs for DH line production had a larger impact on the annual selection gain than the hybrid seed production costs. The optimum allocation of test resources maximizing annual selection gain in classical two-stage phenotypic selection on grain yield and for the recommended breeding strategy GSrapid is finally explored for maize, wheat, rye, barley, rice and triticale.