Selectiongain: an R package for optimizing multi-stage selection

Mi, Xuefei; Utz, H. Friedrich; Melchinger, Albrecht E.

doi:10.1007/s00180-015-0583-9

Cited by 5 publications

(6 citation statements)

References 14 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This vector corresponds to the optimum allocation only if it maximizes selection gain in the respective breeding strategy for a given scenario regarding variance components, budgets, and costs. The optimum allocation was determined by a grid search across all possible allocations for the scenario under consideration (for details see Mi et al 2015 ). Different breeding strategies were only compared at their respective optimum allocation for the scenario under consideration.…”

Section: Methodsmentioning

confidence: 99%

“…The computation of the covariances between traits to obtain the required correlations and is presented in detail in the appendix. An extension of this formula given by Tallis ( 1961 ) to estimate selection gain for any number of correlated traits was implemented in the package R selectiongain (Mi et al 2015 ). Finally, we computed the maximum selection gain for the ICL net merit as where is the economic weight of trait k .…”

Section: Methodsmentioning

confidence: 99%

“…For all our calculations, we used the open-source R (R Core Team 2016) package "selectiongain", which was extended for the incorporation of index selection including phenotypic and genomic information for two traits. For details about the package, the reader is referred to Mi et al (2015) and the user manual of version 2.0.65 (http:// cran.rproje ct. org/ web/ packa ges/ selec tiong ain/ selec tiong ain. pdf).…”

Section: Optimum Allocation Of Resources and Calculation Of Selection Gainmentioning

confidence: 99%

“…The computation of the covariances between traits to obtain the required correlations p 1 ,y k and p 2 ,y k p 1 ,p 2 is presented in detail in the appendix. An extension of this formula given by Tallis (1961) to estimate selection gain for any number of correlated traits was implemented in the package R selectiongain (Mi et al 2015). Finally, we computed the maximum selection gain for the ICL net merit as ∑ n k=1 a k ΔG k where a k is the economic weight of trait k. Following the approach of Hazel and Lush (1942), ΔG a of ICL was determined under the optimum allocation of resources for the SH index regarding j , N j , L j , T j at the stage j.…”

Section: Implementation Of Index Selectionmentioning

confidence: 99%

See 3 more Smart Citations

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

Marulanda

Utz

et al. 2021

Theor Appl Genet

Self Cite

View full text Add to dashboard Cite

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. Abstract 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Optimum Allocation Of Resources and Calculation Of Selection Gainmentioning

confidence: 99%

Section: Implementation Of Index Selectionmentioning

confidence: 99%

See 2 more Smart Citations

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

Marulanda

Utz

et al. 2021

Theor Appl Genet

Self Cite

View full text Add to dashboard Cite

show abstract

“…The R Package Breeding Scheme Language (Yabe et al 2017) simulates breeding programs. Multi-stage breeding schemes for hybrids using economic constraints are implemented in the R package Selectiongain (Mi et al 2014(Mi et al , 2016.…”

Section: Application To Pre-breedingmentioning

confidence: 99%

Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs

Civáň

Rincent

Danguy-Des-Deserts

et al. 2021

Population Genomics

View full text Add to dashboard Cite

The breeding efforts of the twentieth century contributed to large increases in yield but selection may have increased vulnerability to environmental perturbations. In that context, there is a growing demand for methodology to re-introduce useful variation into cultivated germplasm. Such efforts can focus on the introduction of specific traits monitored through diagnostic molecular markers identified by QTL/association mapping or selection signature screening. A combined approach is to increase the global diversity of a crop without targeting any particular trait.A considerable portion of the genetic diversity is conserved in genebanks. However, benefits of genetic resources (GRs) in terms of favorable alleles have to be weighed against unfavorable traits being introduced along. In order to facilitate utilization of GR, core collections are being identified and progressively characterized at the phenotypic and genomic levels. High-throughput genotyping and sequencing technologies allow to build prediction models that can estimate the genetic value of an entire genotyped collection. In a pre-breeding program, predictions can accelerate recurrent selection using rapid cycles in greenhouses by skipping some phenotyping steps. In a breeding program, reduced phenotyping characterization allows to increase the number of tested parents and crosses (and global genetic variance) for a fixed budget. Finally, the whole cross design can be optimized using progeny variance predictions to maximize short-term genetic gain or long-term genetic gain by constraining a minimum level of diversity in the germplasm. There is also a potential to further increase the accuracy of genomic predictions by taking into account genotype by environment interactions, integrating additional layers of omics and environmental information.Here, we aim to review some relevant concepts in population genomics together with recent advances in quantitative genetics in order to discuss how the combination of both disciplines can facilitate the use of genetic diversity in plant (pre) breeding programs.

show abstract

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

Marulanda

Melchinger

et al. 2016

Theor Appl Genet

View full text Add to dashboard Cite

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.

show abstract

Selectiongain: an R package for optimizing multi-stage selection

Cited by 5 publications

References 14 publications

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

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

Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs

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

Contact Info

Product

Resources

About