Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program

Allier, Antoine; Teyssèdre, Simon; Lehermeier, Christina; Claustres, Bruno; Maltese, S.; Melkior, Stephane; Moreau, Laurence; Charcosset, Alain

doi:10.1007/s00122-019-03280-w

Cited by 23 publications

(38 citation statements)

References 57 publications

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“…This explains the higher long term genetic gain at commercial and whole progeny levels 426 observed for UCPC-He*. This result supports the idea, suggested in Allier et al (2019a), that accounting 427 for complementarity between parents when defining crossing plans is an efficient way to favor 428 recombination events to reveal part of the additive genic variance hidden by repulsion between QTLs. 429…”

Section: Accounting For Within Family Variance In Optimal Cross Selecsupporting

confidence: 66%

“…to account for all available information on candidates, some crosses do not yield selected progeny and 5 do not contribute to genetic gain (Heslot et al 2015). As breeding programs from different companies 6 compete for short term gain, breeders tend to use intensively the most performant individuals sometimes 7 at the expense of genetic diversity (Rauf et al 2010;Gerke et al 2015;Allier et al 2019a). The 8 identification of the crossing plan that maximizes the performance in progeny and limits diversity 9 reduction for long term genetic gain is essential.…”

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

Preprint

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The implementation of genomic selection in recurrent breeding programs raised several concerns, especially 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 on current and yet unknown future targets is essential. The optimal cross selection approach aims at identifying the optimal set of crosses maximizing the expected genetic value in the progeny under a constraint on diversity in the progeny. Usually, optimal cross selection does not account for within family selection, i.e. the fact that only a selected fraction of each family serves as candidate 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 method called usefulness criterion parental contribution (UCPC). We compared UCPC based optimal cross selection and optimal cross selection 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 limited reduction of the short term commercial genetic gain.

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Section: Accounting For Within Family Variance In Optimal Cross Selecsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

Allier

Lehermeier²,

Charcosset

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…518burn-in matching that of an experimental program reported byAllier et al (2019a). Breeding 519 programs ongoing for different species and breeders may present a diversity superior or inferior 520 to the one that was simulated, which would make the importance of introductions lower or 521 stronger than in the simulated scenarios, respectively.…”

mentioning

confidence: 86%

Optimized breeding strategies to harness Genetic Resources with different performance levels

Allier

Teyssèdre²,

Lehermeier³

et al. 2019

Preprint

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ABSTRACTThe narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.

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“…The proposed framework that builds on the work of Sorensen et al (2001), Lehermeier et al (2017 and Allier et al (2019) enables straightforward temporal analysis both in terms of years and stages of a breeding programme. The framework uses all the available data spanning multiple years (generations) to estimate model parameters, which are in turn used to infer genetic values and their variances.…”

Section: Temporal Analysismentioning

confidence: 99%

“…The aim of this work is to i) build and validate a flexible framework based on the work of Sorensen et al (2001), Lehermeier et al (2017) and Allier et al (2019), ii) show how to evaluate temporal and genomic analysis of additive genetic variance in different stages of a breeding programme and iii) indicate genetic processes that change genome. We also show how different statistical approaches affect the results.…”

Section: Introductionmentioning

confidence: 99%

Temporal and genomic analysis of additive genetic variance in breeding programmes

Lara

Pocrnić

Gaynor

2020

Preprint

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This study demonstrates a framework for temporal and genomic analysis of additive genetic variance in a breeding programme. Traditionally we used specific experimental designs to estimate genetic variance for a specific group of individuals and a general pedigree-based model to estimate genetic variance for pedigree founders. However, with the pedigree-based model we can also analyse temporal changes in genetic variance by summarising sampled realisations of genetic values from a fitted model. Here we extend this analysis to a marker-based model and build a framework for temporal and genomic analyses of genetic variance. The framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values, and (iii) calculating variance of the sampled genetic values by time and genome partitions. Genome partitions enable estimation of contributions from chromosomes and chromosome pairs and genic and linkage-disequilibrium variances. We demonstrate the framework by analysing data from a simulated breeding programme involving a complex trait with additive gene action. We use the full Bayesian and empirical Bayesian approaches to account for the uncertainty due to model fitting. We also evaluate the use of principal component approximation. Results show good concordance between the simulated and estimated variances for temporal and genomic analyses and give insight into genetic processes. For example, we observe reduction of genic variance due to selection and drift and buildup of negative linkage-disequilibrium (the Bulmer effect) due to directional selection. In this study the popular empirical Bayesian approach estimated the variances well but it underestimated uncertainty of the estimates. The principal components approximation biases estimates, in particular for the genic variance. This study gives breeders a framework to analyse genetic variance and its components in different stages of a programme and over time.

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Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program

Cited by 23 publications

References 57 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

Optimized breeding strategies to harness Genetic Resources with different performance levels

Temporal and genomic analysis of additive genetic variance in breeding programmes

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