Impacts of genotyping strategies on long-term genetic response in genomic selection

AbstractHerein we report the impacts of applying five selection methods across 40 cycles of recurrent selection and identify interactions among factors that affect genetic responses in sets of simulated families of recombinant inbred lines derived from 21 homozygous soybean lines. Our use of recurrence equation to model response from recurrent selection allowed us to estimate the half-lives, asymptotic limits to recurrent selection for purposes of assessing the rates of response and future genetic potential of populations under selection. The simulated factors include selection methods, training sets, and selection intensity that are under the control of the plant breeder as well as genetic architecture and heritability. A factorial design to examine and analyze the main and interaction effects of these factors showed that both the rates of genetic improvement in the early cycles and limits to genetic improvement in the later cycles are significantly affected by interactions among all factors. Some consistent trends are that genomic selection methods provide greater initial rates of genetic improvement (per cycle) than phenotypic selection, but phenotypic selection provides the greatest long term responses in these closed genotypic systems. Model updating with training sets consisting of data from prior cycles of selection significantly improved prediction accuracy and genetic response with three parametric genomic prediction models. Ridge Regression, if updated with training sets consisting of data from prior cycles, achieved better rates of response than BayesB and Bayes LASSO models. A Support Vector Machine method, with a radial basis kernel, had the worst estimated prediction accuracies and the least long term genetic response. Application of genomic selection in a closed breeding population of a self-pollinated crop such as soybean will need to consider the impact of these factors on trade-offs between short term gains and conserving useful genetic diversity in the context of the goals for the breeding program.

Download Full-text

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

10.1101/2021.02.19.431938 ◽

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.

Download Full-text

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

Frontiers in Genetics ◽

10.3389/fgene.2021.675500 ◽

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.

Download Full-text

Combining clonal response and genetic response in dairy cattle improvement

Animal Science ◽

10.1017/s0003356100032281 ◽

1989 ◽

Vol 49 (2) ◽

pp. 163-169 ◽

Cited By ~ 9

Author(s):

G. Teepker ◽

C. Smith

Keyword(s):

Embryo Transfer ◽

Genetic Response ◽

Transfer Scheme ◽

Multiple Ovulation ◽

Selection Systems ◽

Fixed Set ◽

Commercial Population ◽

Generation Cycle ◽

Genetic Responses

ABSTRACTRepeated cloning of bovine embryos by nuclear transfer, producing large clones of monozygous animals, may be possible in the future. Initially, clones could be tested and the best one selected and spread over the commercial population by embryo transfer. Further genetic improvement could be obtained by rebreeding a number of the best clones to produce a new set of clones. However, the testing and selection systems to pick the best clone (for short-term clonal response) and to pick clones with the best breeding values (for long-term genetic response) are different. The objective of this study was to derive a system which achieves both high clonal and high genetic responses. An adult MOET (multiple ovulation and embryo transfer) scheme with 40 breeding males and 40 breeding females per generation (cycle) was used to maintain adequate genetic variation for continued genetic response. For a fixed set of testing facilities and a given family structure initial clonal response is maximized by testing several members per clone. Long-term genetic response is usually greatest when testing one member per clone. Compromises to obtain both high clonal response and high genetic responses were from 95 to 100% efficient.

Download Full-text

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.

Download Full-text

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.

Download Full-text