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

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.

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

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.

scholarly journals Optimized breeding strategies to harness Genetic Resources with different performance levels

2019 ◽  
Author(s):  
Antoine Allier ◽  
Simon Teyssèdre ◽  
Christina Lehermeier ◽  
Laurence Moreau ◽  
Alain Charcosset
Keyword(s):  
Genetic Resources ◽  
Genetic Gain ◽  
Breeding Population ◽  
Short Term ◽  
Breeding Programs ◽  
Performance Levels ◽  
Genetic Base ◽  
Base Broadening ◽  
Genetic Value

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.

scholarly journals Dw2, a New Dwarfing Locus in Apple Rootstocks and Its Relationship to Induction of Early Bearing in Apple Scions

2014 ◽  
Vol 139 (2) ◽  
pp. 87-98 ◽  
Author(s):  
Gennaro Fazio ◽  
Yizhen Wan ◽  
Dariusz Kviklys ◽  
Leticia Romero ◽  
Richard Adams ◽  
...  
Keyword(s):  
Genetic Map ◽  
Production Systems ◽  
Breeding Population ◽  
Breeding Strategy ◽  
Nucleotide Polymorphisms ◽  
Apple Rootstock ◽  
Apple Rootstocks ◽  
Yield Data ◽  
Trait Locus ◽  
Rootstock Breeding

The ability of certain apple rootstocks to dwarf their scions has been known for centuries and their use revolutionized apple (Malus ×domestica) production systems. In this investigation, several apple rootstock breeding populations, planted in multiple replicated field and pot experiments, were used to ascertain the degree of dwarfing when grafted with multiple scions. A previous genetic map of a breeding population derived from parents ‘Ottawa 3’ (O.3) and ‘Robusta 5’ (R5) was used for quantitative trait locus (QTL) analysis of traits related to scion vigor suppression, induction of early bearing, and other tree size measurements on own-rooted and grafted trees. The analysis confirmed a previously reported QTL that imparts vigor control [Dw1, log of odds (LOD) = 7.2] on linkage group (LG) 5 and a new QTL named Dw2 (LOD = 6.4) on LG11 that has a similar effect on vigor. The data from this population were used to study the interaction of these two loci. To validate these findings, a new genetic map comprised of 1841 single-nucleotide polymorphisms was constructed from a cross of the dwarfing, precocious rootstocks ‘Geneva 935’ (G.935) and ‘Budagovsky 9’ (B.9), resulting in the confirmation and modeling of the effect of Dw1 and Dw2 on vigor control of apple scions. Flower density and fruit yield data allowed the identification of genetic factors Eb1 (LOD = 7.1) and Eb2 (LOD = 7.6) that cause early bearing of scions, roughly colocated with the dwarfing factors. The major QTL for mean number of fruit produced per tree colocated with Dw2 (LOD = 7.0) and a minor QTL was located on LG16 (LOD = 3.5). These findings will aid the development of a marker-assisted breeding strategy, and the discovery of additional sources for dwarfing and predictive modeling of new apple rootstocks in the Geneva® apple rootstock breeding program.

scholarly journals Application of marker assisted selection for livestock improvement in Bangladesh

2015 ◽  
Vol 31 (1) ◽  
pp. 1-11 ◽  
Author(s):  
M Moniruzzaman ◽  
R Khatun ◽  
AA Mintoo
Keyword(s):  
Dna Sequences ◽  
Genetic Gain ◽  
Marker Assisted Selection ◽  
Breeding Strategy ◽  
Meat Production ◽  
Economic Traits ◽  
Generation Interval ◽  
Breeding Programmes ◽  
Selection Of

Molecular markers usually do not have any biological effect. They are identifiable DNA sequences, found at specific locations of the genome, and transmitted from one generation to the next. Marker assisted selection (MAS) is a novel technique that can complement traditional breeding methods for rapid genetic gains. Genetic gain through selective breeding is the objective of a breeder to achieve long term improvement in animal and plant genomes; however the pace of improvement is inversely proportional to the Generation Interval. Genetic improvement in livestock, particularly those with long generation intervals, requires decades for tangible results. Successful MAS breeding programmes require gene mapping, marker genotyping, quantitative trait loci (QTL) detection, genetic evaluation and finally MAS. Genomic selection is a form of markerassisted selection. Using markers covering the whole genome could mean potentially that all the genetic variance is explained; and the markers are assumed to be in linkage disequilibrium with the QTL so that the number of effects per QTL to be estimated is small. MAS drastically reduces generation interval and increases selection accuracy. Therefore, a breeding strategy based upon markers making the best use of the two approaches can facilitate rapid genetic gain though selection of markers related to economic traits such as milk and meat production. This review is designed to elaborate the technique of MAS and its application in developing countries. DOI: http://dx.doi.org/10.3329/bvet.v31i1.22837 Bangl. vet. 2014. Vol. 31, No. 1, 1-11

scholarly journals New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

2021 ◽  
Author(s):  
Marlee R. Labroo ◽  
Jessica E. Rutkoski
Keyword(s):  
Genetic Gain ◽  
Overlapping Generations ◽  
Recurrent Selection ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Breeding Values ◽  
Truncation Selection ◽  
Breeding Cycles ◽  
Error Bias

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

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

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.

scholarly journals A multi-part strategy for introgression of exotic germplasm into elite plant breeding programs using genomic selection

2022 ◽  
Author(s):  
Irene S. Breider ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Steve Thorn ◽  
Manish K. Pandey ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Breeding Strategy ◽  
Breeding Program ◽  
Breeding Programs ◽  
Polygenic Inheritance ◽  
Polygenic Traits

Abstract Some of the most economically important traits in plant breeding show highly polygenic inheritance. Genetic variation is a key determinant of the rates of genetic improvement in selective breeding programs. Rapid progress in genetic improvement comes at the cost of a rapid loss of genetic variation. Germplasm available through expired Plant Variety Protection (exPVP) lines is a potential resource of variation previously lost in elite breeding programs. Introgression for polygenic traits is challenging, as many genes have a small effect on the trait of interest. Here we propose a way to overcome these challenges with a multi-part pre-breeding program that has feedback pathways to optimise recurrent genomic selection. The multi-part breeding program consists of three components, namely a bridging component, population improvement, and product development. Parameters influencing the multi-part program were optimised with the use of a grid search. Haploblock effect and origin were investigated. Results showed that the introgression of exPVP germplasm using an optimised multi-part breeding strategy resulted in 1.53 times higher genetic gain compared to a two-part breeding program. Higher gain was achieved through reducing the performance gap between exPVP and elite germplasm and breaking down linkage drag. Both first and subsequent introgression events showed to be successful. In conclusion, the multi-part breeding strategy has a potential to improve long-term genetic gain for polygenic traits and therefore, potential to contribute to global food security.

Stratified sublining: a new option for structuring breeding populations

2000 ◽  
Vol 30 (4) ◽  
pp. 596-604 ◽  
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.

scholarly journals Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding

2004 ◽  
Vol 83 (1) ◽  
pp. 55-64 ◽  
Author(s):  
S. AVENDAÑO ◽  
J. A. WOOLLIAMS ◽  
B. VILLANUEVA
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Selective Advantage ◽  
Breeding Values ◽  
Truncation Selection ◽  
Joint Management ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Available Information ◽  
Mendelian Sampling

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.

scholarly journals Genotype-assisted optimum contribution selection to maximize selection response over a specified time period

2004 ◽  
Vol 84 (2) ◽  
pp. 109-116 ◽  
Author(s):  
THEO H. E. MEUWISSEN ◽  
ANNA K. SONESSON
Keyword(s):  
Genetic Gain ◽  
Selection Response ◽  
Short Term ◽  
Linear Restriction ◽  
Genetic Gains ◽  
Time Period ◽  
Optimum Selection ◽  
Selection For ◽  
Trait Locus

Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35·2%, 2·3% and 1·1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2·8%, 3·1% and 3·2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0·4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.

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