Influence of the mating design on the additive genetic variance in plant breeding populations
Abstract: Key message Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. Abstract The additive genetic variance $${V}_{A}$$ V A inherent to a breeding population is a major determinant of short- and long-term g… Show more
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Cited by 5 publications
References 34 publications
Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families
Key message Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Abstract Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ($$\Sigma \Delta G$$ Σ Δ G ), the genetic, GCA and SCA variances ($$\sigma_{G}^{2}$$ σ G 2 ,$$\sigma_{gca}^{2}$$ σ gca 2 , $$\sigma_{sca}^{2}$$ σ sca 2 ) of the hybrid population, and prediction accuracy ($$r_{gca}$$ r gca ) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of $$N_{e} = 20$$ N e = 20 out of 950 candidates in each parent population. Scenarios differed for heritability $$\left( {h^{2} } \right)$$ h 2 and the proportion $$\tau = 100 \times \sigma_{sca}^{2} :\sigma_{G}^{2}$$ τ = 100 × σ sca 2 : σ G 2 of traits, training set (TS) size ($$N_{TS}$$ N TS ), and maize vs. wheat. Curves of $$\Sigma \Delta G$$ Σ Δ G over selection cycles showed no crossing of both methods. If $$\tau$$ τ was high, $$\Sigma \Delta G$$ Σ Δ G was generally higher for FS-RRGS than HS-RRGS due to higher $$r_{gca}$$ r gca . In contrast, HS-RRGS was superior or on par with FS-RRGS, if $$\tau$$ τ or $$h^{2}$$ h 2 and $$N_{TS}$$ N TS were low. $$\Sigma \Delta G$$ Σ Δ G showed a steeper increase and higher selection limit for scenarios with low $$\tau$$ τ , high $$h^{2}$$ h 2 and large $$N_{TS}$$ N TS . $$\sigma_{gca}^{2}$$ σ gca 2 and even more so $$\sigma_{sca}^{2}$$ σ sca 2 decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing $$\sigma_{gca}^{2}$$ σ gca 2 . Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.
Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families
Key message Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Abstract Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ($$\Sigma \Delta G$$ Σ Δ G ), the genetic, GCA and SCA variances ($$\sigma_{G}^{2}$$ σ G 2 ,$$\sigma_{gca}^{2}$$ σ gca 2 , $$\sigma_{sca}^{2}$$ σ sca 2 ) of the hybrid population, and prediction accuracy ($$r_{gca}$$ r gca ) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of $$N_{e} = 20$$ N e = 20 out of 950 candidates in each parent population. Scenarios differed for heritability $$\left( {h^{2} } \right)$$ h 2 and the proportion $$\tau = 100 \times \sigma_{sca}^{2} :\sigma_{G}^{2}$$ τ = 100 × σ sca 2 : σ G 2 of traits, training set (TS) size ($$N_{TS}$$ N TS ), and maize vs. wheat. Curves of $$\Sigma \Delta G$$ Σ Δ G over selection cycles showed no crossing of both methods. If $$\tau$$ τ was high, $$\Sigma \Delta G$$ Σ Δ G was generally higher for FS-RRGS than HS-RRGS due to higher $$r_{gca}$$ r gca . In contrast, HS-RRGS was superior or on par with FS-RRGS, if $$\tau$$ τ or $$h^{2}$$ h 2 and $$N_{TS}$$ N TS were low. $$\Sigma \Delta G$$ Σ Δ G showed a steeper increase and higher selection limit for scenarios with low $$\tau$$ τ , high $$h^{2}$$ h 2 and large $$N_{TS}$$ N TS . $$\sigma_{gca}^{2}$$ σ gca 2 and even more so $$\sigma_{sca}^{2}$$ σ sca 2 decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing $$\sigma_{gca}^{2}$$ σ gca 2 . Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.
Genetic constitution and variability in synthetic populations of intermediate wheatgrass, an outcrossing perennial grain crop
Intermediate wheatgrass (IWG) is a perennial grass that produces nutritious grain while offering substantial ecosystem services. Commercial varieties of this crop are mostly synthetic panmictic populations that are developed by intermating a few selected individuals. As development and generation advancement of these synthetic populations is a multi-year process, earlier synthetic generations are tested by the breeders and subsequent generations are released to the growers. A comparison of generations within IWG synthetic cultivars is currently lacking. In this study, we used simulation models and genomic prediction to analyze population differences and trends of genetic variance in four synthetic generations of MN-Clearwater, a commercial cultivar released by the University of Minnesota. Little to no differences were observed among the four generations for population genetic, genetic kinship, and genome-wide marker relationships measured via linkage disequilibrium. A reduction in genetic variance was observed when 7 parents were used to generate synthetic populations while using 20 led to the best possible outcome in determining population variance. Genomic prediction of plant height, free threshing ability, seed mass, and grain yield among the four synthetic generations showed a few significant differences among the generations yet the difference in values were negligible. Based on these observations, we make two major conclusions: 1) The earlier and latter synthetic generations of IWG are mostly similar to each other with minimal differences; and 2) Using 20 genotypes to create synthetic populations is recommended to sustain ample genetic variance and trait expression among all synthetic generations.
Plant breeding simulations with AlphaSimR
Plant breeding plays a crucial role in the development of high-performing crop varieties that meet the demands of society. Emerging breeding techniques offer the potential to improve the precision and efficiency of plant breeding programs; however, their optimal implementation requires refinement of existing breeding programs or the design of new ones. Stochastic simulations are a cost-effective solution for testing and optimizing new breeding strategies. The aim of this paper is to provide an introduction to stochastic simulation with software AlphaSimR for plant breeding students, researchers, and experienced breeders. We present an overview of how to use the software and provide an introductory AlphaSimR vignette as well as complete AlphaSimR scripts of breeding programs for self-pollinated, clonal, and cross-pollinated plants, including relevant breeding techniques, such as backcrossing, speed breeding, genomic selection, index selection, and others. Our objective is to provide a foundation for understanding and utilizing simulation software, enabling readers to adapt the provided scripts for their own use or even develop completely new plant breeding programs. By incorporating simulation software into plant breeding education and practice, the next generation of plant breeders will have a valuable tool in their quest to provide sustainable and nutritious food sources for a growing population.
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