scholarly journals Strategies to assure optimal trade-offs among competing objectives for genetic improvement of soybean

2021 ◽  
Author(s):  
Vishnu Ramasubramanian ◽  
William Beavis
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Genetic Variance ◽  
Breeding Strategies ◽  
Short Term ◽  
Genetic Gains ◽  
Mating Design ◽  
Genetic Potential ◽  
Breeding Populations

AbstractPlant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize loss of useful genetic variance. For commercial plant breeders competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS retains useful genetic diversity during the early cycles of selection. With limited resources must a soybean breeder choose between the two extreme responses provided by GS or PS? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs and whether the breeding population should be organized as family islands. For breeding populations organized into islands decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

scholarly journals Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

2021 ◽  
Vol 12 ◽  
Author(s):  
Vishnu Ramasubramanian ◽  
William D. Beavis
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Genetic Variance ◽  
Phenotypic Selection ◽  
Breeding Strategies ◽  
Short Term ◽  
Mating Design ◽  
Breeding Populations ◽  
Hub Network

Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

scholarly journals Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding

2021 ◽  
Vol 12 ◽  
Author(s):  
Éder David Borges da Silva ◽  
Alencar Xavier ◽  
Marcos Ventura Faria
Keyword(s):  
Random Forest ◽  
Bayesian Methods ◽  
Genomic Prediction ◽  
Genetic Variance ◽  
Breeding Strategies ◽  
Selection Intensity ◽  
Soybean Breeding ◽  
Genetic Gains ◽  
Selection Strategies

Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28–58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.

scholarly journals Optimal contribution selection in highly fecund species with overlapping generations

2020 ◽  
Author(s):  
Matthew G Hamilton
Keyword(s):  
Overlapping Generations ◽  
Family Relationship ◽  
Age Groups ◽  
Breeding Strategies ◽  
Breeding Programs ◽  
Genetic Gains ◽  
Breeding Populations ◽  
Outcrossing Species ◽  
Number Of Individuals

Abstract Optimal contributions approaches to parental selection in closed breeding populations aim to maximise genetic gains, while restraining long-term inbreeding. The adoption of optimal contribution selection (OCS) in highly-fecund outcrossing species presents a number of challenges not applicable to species of low fecundity (e.g. livestock) for which they were developed. This is particularly true if overlapping-generations or rolling-front breeding strategies are applied, in which case the number of individuals per family in juvenile (i.e. sexually immature) age groups is not necessarily known but is likely to be large. In these circumstances, conventional OCS procedures must be modified or a large number of dummy individuals defined, making computations onerous. Here, an approach to OCS is presented that involves the use of ‘between-family relationship matrices’ instead of ‘between-individual relationship matrices’. The method is applicable to breeding programs involving highly fecund outcrossing species with overlapping generations, including circumstances where the number of juveniles per family is unknown but large.

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

2020 ◽  
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.

scholarly journals Selection Response in Finite Populations

Genetics ◽  
1996 ◽  
Vol 144 (4) ◽  
pp. 1961-1974 ◽  
Author(s):  
Ming Wei ◽  
Armando Caballero ◽  
William G Hill
Keyword(s):  
Linkage Disequilibrium ◽  
Inbreeding Depression ◽  
Genetic Variance ◽  
Relative Rate ◽  
Selection Response ◽  
Rate Of Change ◽  
Effective Population ◽  
Short Term ◽  
Model Account

Formulae were derived to predict genetic response under various selection schemes assuming an infinitesimal model. Account was taken of genetic drift, gametic (linkage) disequilibrium (Bulmer effect), inbreeding depression, common environmental variance, and both initial segregating variance within families (σAW02) and mutational (σM2) variance. The cumulative response to selection until generation t(CRt) can be approximated asCRt≈R0[t−β(1−σAW∞2σAW02)t24Ne]−Dt2Ne,where Ne is the effective population size, σAW∞2=NeσM2 is the genetic variance within families at the steady state (or one-half the genic variance, which is unaffected by selection), and D is the inbreeding depression per unit of inbreeding. R  0 is the selection response at generation 0 assuming preselection so that the linkage disequilibrium effect has stabilized. β is the derivative of the logarithm of the asymptotic response with respect to the logarithm of the within-family genetic variance, i.e., their relative rate of change. R  0 is the major determinant of the short term selection response, but σM2, Ne and β are also important for the long term. A selection method of high accuracy using family information gives a small Ne and will lead to a larger response in the short term and a smaller response in the long term, utilizing mutation less efficiently.

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.

Influence of milking frequency on the productivity of dairy cows

2006 ◽  
Vol 46 (7) ◽  
pp. 965 ◽  
Author(s):  
C. R. Stockdale
Keyword(s):  
Genetic Selection ◽  
Stocking Rate ◽  
Yield Losses ◽  
Short Term ◽  
Once Daily ◽  
Genetic Potential ◽  
Average Production ◽  
Attractive Option ◽  
Milking Frequency

Benefits and issues of changing milking frequency from the traditional twice a day are reviewed. Increased efficiency through dairy automation and mechanisation, and the desire to utilise advances in genetic selection, have made milking more frequently than twice a day an attractive option for some farmers. The size of the response to increased milking frequency appeared not to be related to existing milk yield, with the average response to increasing the frequency from 2 to 3 times a day being 3.5–3.8 kg/day. Labour is the single most important cost associated with the decision to increase milking frequency. For this reason, automated milking systems may hold the key to the long-term profitability of challenging cows to produce to their genetic potential. In contrast, reducing milking frequency to once a day has been used to reduce stress on underfed cows or for lifestyle and/or labour considerations. Short-term experiments indicate an average production loss of 21% for once daily relative to twice daily milking. Full lactation experiments suggest greater losses of 35–50%, but there is evidence that cows can adapt to longer milking intervals and this, coupled with increased stocking rate and care to maximise milk removal, may restrict yield losses to less than 10% on a whole-farm basis.

scholarly journals Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153945 ◽  
Author(s):  
Shiori Yabe ◽  
Masanori Yamasaki ◽  
Kaworu Ebana ◽  
Takeshi Hayashi ◽  
Hiroyoshi Iwata
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
Island Model ◽  
Selection For

scholarly journals A Two-Part Strategy using Genomic Selection in Hybrid Crop Breeding Programs

2020 ◽  
Author(s):  
Owen Powell ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
Christian R. Werner ◽  
John M. Hickey
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Variance ◽  
New Technologies ◽  
Stochastic Simulations ◽  
Practical Implementation ◽  
Crop Breeding ◽  
Breeding Programs ◽  
Multiple Generations

AbstractHybrid crop breeding programs using a two-part strategy produced the most genetic gain, but a maximum avoidance of inbreeding crossing scheme was required to increase long-term genetic gain. The two-part strategy uses outbred parents to complete multiple generations per year to reduce the generation interval of hybrid crop breeding programs. The maximum avoidance of inbreeding crossing scheme manages genetic variance by maintaining uniform contributions and inbreeding coefficients across all crosses. This study performed stochastic simulations to quantify the potential of a two-part strategy in combination with two crossing schemes to increase the rate of genetic gain in hybrid crop breeding programs. The two crossing schemes were: (i) a circular crossing scheme, and (ii) a maximum avoidance of inbreeding crossing scheme. The results from this study show that the implementation of genomic selection increased the rate of genetic gain, and that the two-part hybrid crop breeding program generated the highest genetic gain. This study also shows that the maximum avoidance of inbreeding crossing scheme increased long-term genetic gain in two-part hybrid crop breeding programs completing multiple selection cycles per year, as a result of maintaining higher levels of genetic variance over time. The flexibility of the two-part strategy offers further opportunities to integrate new technologies to further increase genetic gain in hybrid crop breeding programs, such as the use of outbred training populations. However, the practical implementation of the two-part strategy will require the development of bespoke transition strategies to fundamentally change the data, logistics, and infrastructure that underpin hybrid crop breeding programs.Key messageHybrid crop breeding programs using a two-part strategy produced the most genetic gain by using outbred parents to complete multiple generations per year. However, a maximum avoidance of inbreeding crossing scheme was required to manage genetic variance and increase long-term genetic gain.

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