Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

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Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs

10.1101/2020.05.16.099267 ◽

2020 ◽

Author(s):

Hadi Esfandyari ◽

Dario Fè ◽

Biructawit Bekele Tessema ◽

Lucas L. Janss ◽

Just Jensen

Keyword(s):

Genomic Selection ◽

Perennial Ryegrass ◽

Genetic Gain ◽

Additive Genetic Variance ◽

Phenotypic Selection ◽

Breeding Program ◽

Breeding Programs ◽

Traditional Program ◽

Breeding Schemes ◽

Efficient Selection

AbstractGenomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in a significantly larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on GEBV. Also, GS allows for reduction in cycle time, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.

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Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

Frontiers in Genetics ◽

10.3389/fgene.2021.637017 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jana Obšteter ◽

Janez Jenko ◽

Gregor Gorjanc

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Breeding Program ◽

Progeny Testing ◽

Initial Training ◽

Training Population ◽

Breeding Programs ◽

Use Of Resources ◽

Dairy Cattle Breeding ◽

Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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Genetic and Environmental Variation in a Commercial Breeding Program of Perennial Ryegrass

Crop Science ◽

10.2135/cropsci2014.06.0441 ◽

2015 ◽

Vol 55 (2) ◽

pp. 631-640 ◽

Cited By ~ 18

Author(s):

Dario Fè ◽

Morten Greve Pedersen ◽

Christian S. Jensen ◽

Just Jensen

Keyword(s):

Perennial Ryegrass ◽

Environmental Variation ◽

Breeding Program ◽

Commercial Breeding

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

10.21203/rs.3.rs-1246254/v1 ◽

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.

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Modeling Illustrates That Genomic Selection Provides New Opportunities for Intercrop Breeding

Frontiers in Plant Science ◽

10.3389/fpls.2021.605172 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jon Bančič ◽

Christian R. Werner ◽

R. Chris Gaynor ◽

Gregor Gorjanc ◽

Damaris A. Odeny ◽

...

Keyword(s):

Grain Yield ◽

Genetic Correlation ◽

Genomic Selection ◽

Genetic Gain ◽

Phenotypic Selection ◽

Breeding Program ◽

Weed Suppression ◽

Selection Strategy ◽

Agricultural Practice ◽

Breeding Programs

Intercrop breeding programs using genomic selection can produce faster genetic gain than intercrop breeding programs using phenotypic selection. Intercropping is an agricultural practice in which two or more component crops are grown together. It can lead to enhanced soil structure and fertility, improved weed suppression, and better control of pests and diseases. Especially in subsistence agriculture, intercropping has great potential to optimize farming and increase profitability. However, breeding for intercrop varieties is complex as it requires simultaneous improvement of two or more component crops that combine well in the field. We hypothesize that genomic selection can significantly simplify and accelerate the process of breeding crops for intercropping. Therefore, we used stochastic simulation to compare four different intercrop breeding programs implementing genomic selection and an intercrop breeding program entirely based on phenotypic selection. We assumed three different levels of genetic correlation between monocrop grain yield and intercrop grain yield to investigate how the different breeding strategies are impacted by this factor. We found that all four simulated breeding programs using genomic selection produced significantly more intercrop genetic gain than the phenotypic selection program regardless of the genetic correlation with monocrop yield. We suggest a genomic selection strategy which combines monocrop and intercrop trait information to predict general intercropping ability to increase selection accuracy in the early stages of a breeding program and to minimize the generation interval.

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Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping

10.1101/2020.08.16.252841 ◽

2020 ◽

Author(s):

Jana Obšteter ◽

Janez Jenko ◽

Gregor Gorjanc

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Breeding Program ◽

Progeny Testing ◽

Initial Training ◽

Training Population ◽

Breeding Programs ◽

Female Candidates ◽

Use Of Resources ◽

Close Relatives

AbstractThis paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario had 11 phenotype records per lactation. In genomic scenarios, we reduced phenotyping to between 10 and 1 phenotype records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic scenarios expectedly increased accuracy for young non-phenotyped male and female candidates, but also cows. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximise return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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Optimizing Resource Allocation in a Genomic Breeding Program for Perennial Ryegrass to Balance Genetic Gain, Cost, and Inbreeding

Crop Science ◽

10.2135/cropsci2016.07.0577 ◽

2016 ◽

Vol 57 (1) ◽

pp. 243-252 ◽

Cited By ~ 10

Author(s):

Zibei Lin ◽

Junping Wang ◽

Noel O.I. Cogan ◽

Luke W. Pembleton ◽

Pieter Badenhorst ◽

...

Keyword(s):

Resource Allocation ◽

Perennial Ryegrass ◽

Genetic Gain ◽

Breeding Program ◽

Genomic Breeding

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Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield

Scientific Reports ◽

10.1038/s41598-021-92537-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Z. Z. Jahufer ◽

Sai Krishna Arojju ◽

Marty J. Faville ◽

Kioumars Ghamkhar ◽

Dongwen Luo ◽

...

Keyword(s):

Genomic Selection ◽

Perennial Ryegrass ◽

Genetic Gain ◽

Dry Matter ◽

New Technologies ◽

Stochastic Modelling ◽

Phenotypic Selection ◽

Breeding Strategy ◽

Yield Data ◽

The Impact

AbstractIncreasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

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Simulation studies to optimize genomic selection in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-021-00654-x ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Richard Bernstein ◽

Manuel Du ◽

Andreas Hoppe ◽

Kaspar Bienefeld

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Honey Bees ◽

Reference Population ◽

Breeding Program ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Genomic Relationship ◽

Genomic Breeding ◽

Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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