Optimized breeding strategies to harness Genetic Resources with different performance levels

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

10.1101/634303 ◽

2019 ◽

Author(s):

Antoine Allier ◽

Christina Lehermeier ◽

Alain Charcosset ◽

Laurence Moreau ◽

Simon Teyssèdre

Keyword(s):

Genetic Diversity ◽

Genomic Selection ◽

Genetic Gain ◽

Family Selection ◽

Breeding Programs ◽

Short And Long Term ◽

Inbreeding Rate ◽

Genetic Value ◽

Optimal Set

AbstractThe 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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Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03932-w ◽

2021 ◽

Author(s):

David Vanavermaete ◽

Jan Fostier ◽

Steven Maenhout ◽

Bernard De Baets

Keyword(s):

Genetic Variation ◽

Genetic Gain ◽

Breeding Population ◽

Breeding Strategy ◽

Premature Convergence ◽

Financial Cost ◽

Truncation Selection ◽

Trait Locus ◽

Genetic Value

Abstract Key message The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Abstract Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.

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How to reach a higher selection plateau with optimal contribution selection and compensatory mating?

10.1101/2021.02.15.431233 ◽

2021 ◽

Author(s):

Tiret Mathieu ◽

Pégard Marie ◽

Sánchez Leopoldo

Keyword(s):

Genetic Gain ◽

Random Mating ◽

Snp Array ◽

Breeding Strategy ◽

Genetic Response ◽

Short Term ◽

Breeding Programs ◽

Trade Off ◽

Study Case

AbstractIn breeding programs, balancing short-term genetic gain and loss of diversity per generation is essential to sustain a long-term genetic response. Depending on the dynamic of the species, the acceptable trade-off will be different. One of the most common and successful tools to achieve this management is the Optimal Contribution Selection (OCS), which readily mathematically formulate the trade-off between genetic gain and coancestry. However, OCS only accounts for the next generation gain and diversity, which can lead to suboptimality given the uncertainties of random mating and segregation. In this paper, we have extended the OCS by conveniently integrating a way to promote certain parental pairs, so that this method can account for the next t+2 generation. In the study case of Populus nigra, fully phenotyped and SNP array genotyped, we have shown that (i) a non negligible part of the long-term success of a breeding strategy depends on the implemented mating strategy, and (ii) favoring a compensatory mating can accelerate the selection without compromising the future diversity.

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A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

10.1101/2020.01.08.899039 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nicholas Santantonio ◽

Kelly Robbins

Keyword(s):

Plant Breeding ◽

Genomic Selection ◽

Genetic Gain ◽

Genetic Variance ◽

Hybrid Approach ◽

Breeding Program ◽

Short Term ◽

Breeding Programs ◽

Plant Breeding Program

1AbstractPlant breeding programs must adapt genomic selection to an already complex system. Inbred or hybrid plant breeding programs must make crosses, produce inbred individuals, and phenotype inbred lines or their hybrid test-crosses to select and validate superior material for product release. These products are few, and while it is clear that population improvement is necessary for continued genetic gain, it may not be sufficient to generate superior products. Rapid-cycle recurrent truncation genomic selection has been proposed to increase genetic gain by reducing generation time. This strategy has been shown to increase short-term gains, but can quickly lead to loss of genetic variance through inbreeding as relationships drive prediction. The optimal contribution of each individual can be determined to maximize gain in the following generation while limiting inbreeding. While optimal contribution strategies can maintain genetic variance in later generations, they suffer from a lack of short-term gains in doing so. We present a hybrid approach that branches out yearly to push the genetic value of potential varietal materials while maintaining genetic variance in the recurrent population, such that a breeding program can achieve short-term success without exhausting long-term potential. Because branching increases the genetic distance between the phenotyping pipeline and the recurrent population, this method requires sacrificing some trial plots to phenotype materials directly out of the recurrent population. We envision the phenotypic pipeline not only for selection and validation, but as an information generator to build predictive models and develop new products.

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Development, progress and future prospects in cryobiotechnology of Lilium spp.

Plant Methods ◽

10.1186/s13007-019-0506-9 ◽

2019 ◽

Vol 15 (1) ◽

Cited By ~ 4

Author(s):

Jing-Wei Li ◽

Xiao-Chen Zhang ◽

Min-Rui Wang ◽

Wen-Lu Bi ◽

M. Faisal ◽

...

Keyword(s):

Cold Stress ◽

Genetic Resources ◽

Easy Access ◽

Future Prospects ◽

Breeding Programs ◽

Future Studies ◽

Comprehensive Information ◽

Long Term Preservation ◽

Made In

Abstract Lilium is one of the most popular flower crops worldwide, and some species are also used as vegetables and medicines. The availability of and easy access to diverse Lilium genetic resources are essential for plant genetic improvements. Cryopreservation is currently considered as an ideal means for the long-term preservation of plant germplasm. Over the last two decades, great efforts have been exerted in studies of Lilium cryopreservation and progress has been made in the successful cryopreservation of pollen, seeds and shoot tips in Lilium. Genes that exist in Lilium, including those that regulate flower shape, color and size, and that are resistant to cold stress and diseases caused by fungi and viruses, provide a rich source of valuable genetic resources for breeding programs to create novel cultivars required by the global floriculture and ornamental markets. Successful cryopreservation of Lilium spp. is a way to preserve these valuable genes. The present study provides updated and comprehensive information about the development of techniques that have advanced Lilium cryopreservation. Further ideas are proposed to better direct future studies on Lilium cryobiotechnology.

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

10.1007/13836_2021_97 ◽

2021 ◽

Author(s):

Peter Civan ◽

Renaud Rincent ◽

Alice Danguy-Des-Deserts ◽

Jean-Michel Elsen ◽

Sophie Bouchet

Keyword(s):

Genetic Diversity ◽

Quantitative Genetics ◽

Genetic Gain ◽

Recurrent Selection ◽

Prediction Models ◽

Population Genomics ◽

Breeding Program ◽

Breeding Programs ◽

Sequencing Technologies ◽

Genetic Value

AbstractThe 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.

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Optimization of a specialized beef breeding program with a crossbreeding component

Archives Animal Breeding ◽

10.5194/aab-45-433-2002 ◽

2002 ◽

Vol 45 (5) ◽

pp. 433-441

Author(s):

B. Fuerst-Waltl ◽

A. Willam ◽

J. Sölkner

Keyword(s):

Genetic Gain ◽

Performance Testing ◽

Beef Cows ◽

Breeding Population ◽

Cattle Breeding ◽

Breeding Programs ◽

Dual Purpose ◽

Beef Breed ◽

Calving Ease ◽

Natural Service

Abstract. A complex deterministic approach (ZPLAN) was used to optimize the breeding programs for beef breeds. For the model population 1,000 beef cows and 60,000 dual purpose Simmental cows for crossbreeding were assumed. The percentage of AI was 25% within the beef breed and 93% within the Simmental cows. Domestic AI beef bulls were used for crossbreeding only. The total merit index included beef traits (birth weight, 200-day-weight direct and maternal, 365-day-weight, daily gain, dressing percentage, EUROP grading score) and functional traits (calving ease, stillbirth, fertility and functional longevity). The proportion of foreign proven and domestic AI bulls was varied as well as the number of bulls tested on stations and on contract farms. Annual monetary genetic gain and discounted profit were used to evaluate alternative breeding strategies. Extending the number of bulls tested on stations and establishing performance testing of natural service bulls on contract farms increased the annual monetary genetic gain and the discounted profit, especially when domestic AI bulls were also used in the beef cattle breeding population.

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Grapevine Cultivars, Trellis Systems, and Mechanization of the California Raisin Industry

HortTechnology ◽

10.21273/horttech.24.3.285 ◽

2014 ◽

Vol 24 (3) ◽

pp. 285-289 ◽

Cited By ~ 5

Author(s):

Matthew W. Fidelibus

Keyword(s):

Production Systems ◽

The United States ◽

Medium Term ◽

Thompson Seedless ◽

Short Term ◽

Breeding Programs ◽

Early Ripening ◽

Ct System ◽

Made In

Growers in California’s San Joaquin Valley produced >25% of the world’s raisins in 2012, with a farm-gate value of >$590 million, making the United States the leading global producer of raisins. California’s traditional raisin-making method is a laborious process in which clusters of grapes (Vitis vinifera) are harvested by hand onto paper trays, which are left in the vineyard to dry. The drying fruit may need to be turned or rolled, tasks requiring manual labor, and the trays of dried raisins are also picked up by hand. Most California raisins continue to be made in this way, but in recent years, the declining availability and increasing cost of labor has prompted many growers to implement one of two mechanized production systems, “continuous tray” (CT) or “dry-on-vine” (DOV). In CT systems, machines are used to pick the berries, lay them onto a tray, and pick up the dried raisins. The CT system could be considered a short-term strategy: it is compatible with existing conventional ‘Thompson Seedless’ raisin vineyards and has been widely adopted. The DOV system could be considered a medium-term strategy: it is best suited for vineyards specifically designed for DOV, with early ripening grapevine cultivars on expansive trellis systems, which ensures timely drying, and capitalizes on the fact that sunlit row middles are not needed for fruit drying. Grapevine breeding programs are currently working toward the development of raisin grape cultivars with fruitful basal nodes, with fruit that dry naturally upon ripening. This is a long-term strategy to further reduce labor needs by enabling mechanical pruning in winter and eliminating the need for cane severance in the summer.

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Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401354 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2753-2762

Author(s):

David Vanavermaete ◽

Jan Fostier ◽

Steven Maenhout ◽

Bernard De Baets

Keyword(s):

Genetic Variation ◽

Genomic Selection ◽

Breeding Population ◽

Local Optimum ◽

Parental Selection ◽

The Mean ◽

Estimated Breeding Values ◽

Genetic Value ◽

Parental Mating

Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and delivers offspring with high genetic values. However, several favorable quantitative trait loci (QTL) alleles risk being eliminated from the breeding population during breeding. We show that this could reduce the mean genetic value that the breeding population could reach in the long term with up to 40%. In this paper, by means of a simulation study, we propose a new method for parental mating that is able to preserve the genetic variation in the breeding population, preventing premature convergence of the genetic values to a local optimum, thus maximizing the genetic values in the long term. We do not only prevent the fixation of several unfavorable QTL alleles, but also demonstrate that the genetic values can be increased by up to 15 percentage points compared with truncation selection.

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Stratified sublining: a new option for structuring breeding populations

Canadian Journal of Forest Research ◽

10.1139/x99-245 ◽

2000 ◽

Vol 30 (4) ◽

pp. 596-604 ◽

Cited By ~ 3

Author(s):

Seppo Ruotsalainen ◽

Dag Lindgren

Keyword(s):

Genetic Gain ◽

Breeding Population ◽

Random Allocation ◽

Genetic Quality ◽

Seed Orchards ◽

Breeding Values ◽

Breeding Populations ◽

Genetic Value ◽

Interesting Alternative

When structuring a breeding population into sublines, the conventional approach is to assign parents to sublines randomly, so that each subline has approximately the same genetic value. By using deterministic infinitesimal model we study an alternative, stratified sublining system, where sublines are initially formed by positive assortative grouping of parents according to their breeding values. Stratified and random allocation to sublines are compared by evaluating the genetic quality of the seed orchards that each approach can provide. The seed orchards were established by selecting first the best individual from each subline and then a given best proportion from them. The greater among-subline variance in stratified sublining led to higher genetic gain in resulting seed orchards than did random sublining. For the case studied, stratified sublining gave considerably more genetic gain than random sublining, over 15% more, making it an interesting alternative that deserves further consideration and study.

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