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

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

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401382 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3783-3795

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 ◽

Estimated Breeding Values

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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Genetic gain in the breeding program of common beans at IAC from 1989 to 2007

Cropp Breeding and Applied Biotechnology ◽

10.1590/s1984-70332010000400007 ◽

2010 ◽

Vol 10 (4) ◽

pp. 329-336 ◽

Cited By ~ 16

Author(s):

Alisson Fernando Chiorato ◽

Sérgio Augusto Morais Carbonell ◽

Roland Vencovsky ◽

Nelson da Silva Fonseca Júnior ◽

José Baldin Pinheiro

Keyword(s):

Grain Yield ◽

Common Bean ◽

Genetic Gain ◽

Grain Quality ◽

The State ◽

Breeding Program ◽

Common Beans ◽

The Common ◽

The Mean ◽

Second Period

The goal of the present work was to evaluate the genetic gain obtained in grain yield for the common bean genotypes from 1989 until 2007, at the Instituto Agronômico de Campinas, in the state of São Paulo. Genetic gain has been separated into two research periods; the first, from 1989 to 1996, and the second, from 1997 to 2007. In the first period, a genetic gain of 1.07 % per year was obtained, whereas for the second period, the gain was zero. However, the mean yield of the evaluated lines was approximately 1000 kg ha-1 superior to the figures obtained in the first period. The main cause for the absence of genetic gain in the second period is that the focus of the breeding program was changed to grain quality. The individualized analysis of the genotypes with carioca grains in the second period indicated the lack of genetic gain during the investigated period.

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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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Estimation of Realized Rates of Genetic Gain and Indicators for Breeding Program Assessment

Crop Science ◽

10.2135/cropsci2018.09.0537 ◽

2019 ◽

Vol 59 (3) ◽

pp. 981-993 ◽

Cited By ~ 2

Author(s):

J. E. Rutkoski

Keyword(s):

Genetic Gain ◽

Program Assessment ◽

Breeding Program

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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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Designing dairy cattle breeding schemes under genomic selection: a review of international research

Animal Production Science ◽

10.1071/an11098 ◽

2012 ◽

Vol 52 (3) ◽

pp. 107 ◽

Cited By ~ 67

Author(s):

J. E. Pryce ◽

H. D. Daetwyler

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Genetic Gain ◽

Reproductive Technologies ◽

Progeny Testing ◽

Genomic Breeding ◽

Breeding Values ◽

Scheme Design ◽

Dairy Cattle Breeding ◽

Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

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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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