scholarly journals Strategies of Preserving Genetic Diversity While Maximizing Genetic Response From Implementing Genomic Selection in Pulse Breeding Programs

2021 ◽  
Author(s):  
Yongjun Li ◽  
Sukhjiwan Kaur ◽  
Luke W. Pembleton ◽  
Hossein Valipour-Kahrood ◽  
Garry M. Rosewarne ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Grain Yield ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Complex Traits ◽  
Phenotypic Selection ◽  
Breeding Cycle ◽  
Environment Interaction ◽  
Breeding Programs ◽  
Diversity Loss

Abstract Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity. Whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of GEBVs and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.

scholarly journals Modeling Illustrates That Genomic Selection Provides New Opportunities for Intercrop Breeding

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.

scholarly journals Modelling illustrates that genomic selection provides new opportunities for intercrop breeding

2020 ◽  
Author(s):  
Jon Bančič ◽  
Christian Werner ◽  
Chris Gaynor ◽  
Gregor Gorjanc ◽  
Damaris Odeny ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Phenotypic Selection ◽  
Breeding Program ◽  
Weed Suppression ◽  
Selection Strategy ◽  
Agricultural Practice ◽  
Breeding Programs

AbstractIntercrop 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 optimise 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 early stages of a breeding program and to minimize the generation interval.

scholarly journals Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience

2021 ◽  
Vol 12 ◽  
Author(s):  
◽  
Aline Fugeray-Scarbel ◽  
Catherine Bastien ◽  
Mathilde Dupont-Nivet ◽  
Stéphane Lemarié
Keyword(s):  
Genetic Diversity ◽  
Genomic Selection ◽  
Plant Species ◽  
Animal Species ◽  
Animal Breeding ◽  
Breeding Cycle ◽  
Breeding Programs ◽  
The Third ◽  
Wide Range ◽  
Selection For

The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.

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

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.

scholarly journals Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat

2020 ◽  
Vol 11 ◽  
Author(s):  
Biructawit Bekele Tessema ◽  
Huiming Liu ◽  
Anders Christian Sørensen ◽  
Jeppe Reitan Andersen ◽  
Just Jensen
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Selection Criterion ◽  
Phenotypic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
Estimated Breeding Values ◽  
The One

Conventional wheat-breeding programs involve crossing parental lines and subsequent selfing of the offspring for several generations to obtain inbred lines. Such a breeding program takes more than 8 years to develop a variety. Although wheat-breeding programs have been running for many years, genetic gain has been limited. However, the use of genomic information as selection criterion can increase selection accuracy and that would contribute to increased genetic gain. The main objective of this study was to quantify the increase in genetic gain by implementing genomic selection in traditional wheat-breeding programs. In addition, we investigated the effect of genetic correlation between different traits on genetic gain. A stochastic simulation was used to evaluate wheat-breeding programs that run simultaneously for 25 years with phenotypic or genomic selection. Genetic gain and genetic variance of wheat-breeding program based on phenotypes was compared to the one with genomic selection. Genetic gain from the wheat-breeding program based on genomic estimated breeding values (GEBVs) has tripled compared to phenotypic selection. Genomic selection is a promising strategy for improving genetic gain in wheat-breeding programs.

scholarly journals Using high-throughput phenotypes to enable genomic selection by inferring genotypes

2020 ◽  
Author(s):  
Andrew Whalen ◽  
Chris Gaynor ◽  
John M Hickey
Keyword(s):  
Genomic Selection ◽  
High Throughput ◽  
Genetic Gain ◽  
Phenotypic Selection ◽  
Breeding Programs ◽  
Large Numbers ◽  
Plant Breeding Program ◽  
Phenotype Data ◽  
The Cost

AbstractIn this paper we develop and test a method which uses high-throughput phenotypes to infer the genotypes of an individual. The inferred genotypes can then be used to perform genomic selection. Previous methods which used high-throughput phenotype data to increase the accuracy of selection assumed that the high-throughput phenotypes correlate with selection targets. When this is not the case, we show that the high-throughput phenotypes can be used to determine which haplotypes an individual inherited from their parents, and thereby infer the individual’s genotypes. We tested this method in two simulations. In the first simulation, we explored, how the accuracy of the inferred genotypes depended on the high-throughput phenotypes used and the genome of the species analysed. In the second simulation we explored whether using this method could increase genetic gain a plant breeding program by enabling genomic selection on non-genotyped individuals. In the first simulation, we found that genotype accuracy was higher if more high-throughput phenotypes were used and if those phenotypes had higher heritability. We also found that genotype accuracy decreased with an increasing size of the species genome. In the second simulation, we found that the inferred genotypes could be used to enable genomic selection on non-genotyped individuals and increase genetic gain compared to random selection, or in some scenarios phenotypic selection. This method presents a novel way for using high-throughput phenotype data in breeding programs. As the quality of high-throughput phenotypes increases and the cost decreases, this method may enable the use of genomic selection on large numbers of non-genotyped individuals.

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

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.

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

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.

Environmental and genetic variation for grain yield and barley malting quality attributes

1999 ◽  
Vol 50 (8) ◽  
pp. 1425 ◽  
Author(s):  
M. Q. Lu ◽  
L. O'Brien ◽  
I. M. Stuart
Keyword(s):  
Grain Yield ◽  
Protein Content ◽  
Genetic Gain ◽  
Quality Attributes ◽  
Malting Quality ◽  
Malt Extract ◽  
Progeny Testing ◽  
Environment Interaction ◽  
Wide Range ◽  
Selection For

Genotype, environment, and genotype × environment interaction effects for malting quality attributes and grain yield were investigated using breeding lines from the F2, F3, and F4 generations and the parental varieties of 4 barley crosses. There were significant differences between the parental varieties for all attributes studied. Both malting quality and grain yield exhibited a wide range among progenies in all generations. While performance of the parental varieties and progeny for malting quality and grain yield were greatly influenced by environment, performance in one environment was predictive of that in other environments. Only for grain protein content was there evidence of crossover G × E interaction. Heritability was generally higher for F3 to F4 than for F2 to F3 for all malting quality attributes. F3 on F2 regression per cent heritability estimates for protein content, potential malt extract and grain weight were all highly significant with values generally medium in magnitude. Genetic gain was obtained from selection in both the F2 and F3 generations. Heritability and genetic gain varied from cross to cross for diastatic power. Progress from selection for the other quality attributes attests to the potential value of NIT (near infrared transmittance) spectroscopy for predicting potential malting quality. Heritability for F2 to F3 for grain yield was not significant in any cross, indicating selection for yield on the basis of individual F2 plant yield was ineffective. Heritability for grain yield from F3 to F4 was highly significant and medium in magnitude for 3 of the 4 crosses. The results of this study indicate that good genetic gain could be expected from early generation selection for potential malting quality using NIT spectroscopy and for grain yield using F3 progeny testing.

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