Multi-objective optimized breeding strategies

Akdemir, Deniz; Sánchez, Julio Isidro

doi:10.1101/209080

Cited by 5 publications

(6 citation statements)

References 73 publications

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“…Accounting for within cross variance to measure the expected gain of a cross in optimal cross-selection was already suggested in Shepherd and Kinghorn (1998). More recently, Akdemir and Isidro-Sánchez (2016) and Akdemir et al (2018) accounted for within cross variance considering linkage equilibrium between QTLs. Akdemir and Isidro-Sánchez (2016) also observed that accounting for within cross variance during cross-selection yielded higher long-term mean performance with a penalty at short-term mean progeny performance.…”

Section: Discussionmentioning

confidence: 99%

“…Optimal contribution selection aims at identifying the optimal contributions ( c ) of candidate parents to the next generation obtained by random mating, in order to maximize the expected genetic value in the progeny ( V ) under a certain constraint on inbreeding ( D ). Optimal cross-selection, further referred as OCS, is an extension of the optimal contribution selection to deliver a crossing plan that maximizes V by considering additional constraints on the allocation of mates in crosses to limit D (Kinghorn et al, 2009; Kinghorn, 2011; Akdemir and Isidro-Sánchez, 2016; Gorjanc et al, 2018; Akdemir et al, 2018). In GS, the expected genetic value in progeny ( V ) to be maximized is the mean of parental GEBV ( a ) weighted by parental contributions c , i.e c’a , and the constraint on inbreeding ( D ) to be minimized is c ’ Kc with K a genomic coancestry matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Differential evolutionary algorithms have been proposed to obtain optimal solutions for c and the crossing plan (Storn and Price, 1997; Kinghorn et al, 2009; Kinghorn, 2011). Optimal contribution selection is commonly used in animal breeding (Woolliams et al 2015) and is increasingly adopted in plant breeding (Akdemir and Isidro-Sánchez, 2016; De Beukelaer et al, 2017; Lin et al, 2017; Gorjanc et al, 2018; Akdemir et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In previous works, the genetic gain ( V ) and constraint ( D ) have been defined at the level of the progeny before within-family selection. Exceptions are the work of Shepherd and Kinghorn (1998) and Akdemir and Isidro-Sánchez (2016); Akdemir et al (2018), who added a term to V accounting for within cross variance assuming linkage equilibrium between QTLs. To our knowledge, no previous study considered linkage disequilibrium (LD) between QTLs.…”

Section: Introductionmentioning

confidence: 99%

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Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection

Allier

Lehermeier²,

Charcosset

et al. 2019

Front. Genet.

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The implementation of genomic selection in recurrent breeding programs raises the concern that a higher inbreeding rate could compromise the long-term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long-term genetic gain is therefore essential. The optimal cross-selection approach aims at identifying the optimal set of crosses that maximizes the expected genetic value in the progeny under a constraint on genetic diversity in the progeny. Optimal cross-selection usually does not account for within-family selection, i.e., the fact that only a selected fraction of each family is used as parents of the next generation. In this study, we consider within-family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the usefulness criterion parental contribution (UCPC) method. We compared UCPC-based optimal cross-selection and the optimal cross-selection approach in a long-term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC-based optimal cross-selection proved to be more efficient to convert the genetic diversity into short- and long-term genetic gains than optimal cross-selection. We also showed that, using the UCPC-based optimal cross-selection, the long-term genetic gain can be increased with only a limited reduction of the short-term commercial genetic gain.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection

Allier

Lehermeier²,

Charcosset

et al. 2019

Front. Genet.

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show abstract

“…A more systematic alternative might be to perform a search of the multivariate space of scenarios defined by desired gains, correlated responses, economic values and selection intensities using different constraints [e.g. overall cost of the breeding cycle (Gaynor et al , 2017)] or approach the search as an optimization problem (Akdemir and Isidro Sanchez, 2017; Li et al , 2017).…”

Section: Genomic Index Selection Can Be Used To Evaluate Breeding Tarmentioning

confidence: 99%

Genomic index selection provides a pragmatic framework for setting and refining multi-objective breeding targets in Miscanthus

et al. 2018

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Background Miscanthus has potential as a biomass crop but the development of varieties that are consistently superior to the natural hybrid M. × giganteus has been challenging, presumably because of strong G × E interactions and poor knowledge of the complex genetic architectures of traits underlying biomass productivity and climatic adaptation. While linkage and association mapping studies are starting to generate long lists of candidate regions and even individual genes, it seems unlikely that this information can be translated into effective marker-assisted selection for the needs of breeding programmes. Genomic selection has emerged as a viable alternative, and prediction accuracies are moderate across a range of phenological and morphometric traits in Miscanthus, though relatively low for biomass yield per se. Methods We have previously proposed a combination of index selection and genomic prediction as a way of overcoming the limitations imposed by the inherent complexity of biomass yield. Here we extend this approach and illustrate its potential to achieve multiple breeding targets simultaneously, in the absence of a priori knowledge about their relative economic importance, while also monitoring correlated selection responses for non-target traits. We evaluate two hypothetical scenarios of increasing biomass yield by 20 % within a single round of selection. In the first scenario, this is achieved in combination with delaying flowering by 44 d (roughly 20 %), whereas, in the second, increased yield is targeted jointly with reduced lignin (–5 %) and increased cellulose (+5 %) content, relative to current average levels in the breeding population.Key ResultsIn both scenarios, the objectives were achieved efficiently (selection intensities corresponding to keeping the best 20 and 4 % of genotypes, respectively). However, the outcomes were strikingly different in terms of correlated responses, and the relative economic values (i.e. value per unit of change in each trait compared with that for biomass yield) of secondary traits included in selection indices varied considerably.ConclusionsAlthough these calculations rely on multiple assumptions, they highlight the need to evaluate breeding objectives and explicitly consider correlated responses in silico, prior to committing extensive resources. The proposed approach is broadly applicable for this purpose and can readily incorporate high-throughput phenotyping data as part of integrated breeding platforms.

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Design of training populations for selective phenotyping in genomic prediction

Akdemir

Isidro-Sánchez

2019

Sci Rep

Self Cite

110

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Phenotyping is the current bottleneck in plant breeding, especially because next-generation sequencing has decreased genotyping cost more than 100.000 fold in the last 20 years. Therefore, the cost of phenotyping needs to be optimized within a breeding program. When designing the implementation of genomic selection scheme into the breeding cycle, breeders need to select the optimal method for (1) selecting training populations that maximize genomic prediction accuracy and (2) to reduce the cost of phenotyping while improving precision. In this article, we compared methods for selecting training populations under two scenarios: Firstly, when the objective is to select a training population set (TRS) to predict the remaining individuals from the same population (Untargeted), and secondly, when a test set (TS) is first defined and genotyped, and then the TRS is optimized specifically around the TS (Targeted). Our results show that optimization methods that include information from the test set (targeted) showed the highest accuracies, indicating that apriori information from the TS improves genomic predictions. In addition, predictive ability enhanced especially when population size was small which is a target to decrease phenotypic cost within breeding programs.

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Multi-objective optimized breeding strategies

Cited by 5 publications

References 73 publications

Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection

Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection

Genomic index selection provides a pragmatic framework for setting and refining multi-objective breeding targets in Miscanthus

Design of training populations for selective phenotyping in genomic prediction

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