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Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits
Sunday, 2021/12/12 | 05:05:47
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Jose J. Marulanda, Xuefei Mi, H. Friedrich Utz, Albrecht E. Melchinger, Tobias Würschum & C. Friedrich H. Longin Theoretical and Applied Genetics December 2021; vol.134: 4025–4042 Key messageA 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. AbstractSelection 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.
See https://link.springer.com/article/10.1007/s00122-021-03945-5
Fig. 1: Practical section on hybrid wheat: Maximum annual selection gain (ΔGa) for net merit and corresponding selection gain in individual traits for three selection indices in two hybrid breeding schemes. Trait one was grain yield and trait two was the quality trait protein content or sedimentation volume. Variance components, the correlation between traits, and economic weights were based on empirical data as described in Goals 1 and 2 of Suppl. Table 1. Prediction accuracies for both traits were assumed to be equal in all strategies except for “unequal accuracies”, where the accuracy of trait 1 is shown in the x axis and the accuracy of trait 2 is 0.2 higher than for trait 1 in absolute terms. For illustration, the maximum annual selection was also shown when directly selecting only one trait (“No Index”). ICL independent culling levels; SH, Smith–Hazel; Restric, Restricted. |
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