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 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 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 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 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 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 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 A New Breeding Strategy towards Introgression and Characterization of Stay-Green QTL for Drought Tolerance in Sorghum

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 598
Author(s):  
Nasrein Mohamed Kamal ◽  
Yasir Serag Alnor Gorafi ◽  
Hanan Abdeltwab ◽  
Ishtiag Abdalla ◽  
Hisashi Tsujimoto ◽  
...  
Keyword(s):  
Phenotypic Expression ◽  
Phenotypic Selection ◽  
Breeding Strategy ◽  
Stay Green ◽  
Breeding Programs ◽  
The Public ◽  
Drought Tolerant ◽  
Selection Step ◽  
The Cost

Several marker-assisted selection (MAS) or backcrossing (MAB) approaches exist for polygenic trait improvement. However, the implementation of MAB remains a challenge in many breeding programs, especially in the public sector. In MAB introgression programs, which usually do not include phenotypic selection, undesired donor traits may unexpectedly turn up regardless of how expensive and theoretically powerful a backcross scheme may be. Therefore, combining genotyping and phenotyping during selection will improve understanding of QTL interactions with the environment, especially for minor alleles that maximize the phenotypic expression of the traits. Here, we describe the introgression of stay-green QTL (Stg1–Stg4) from B35 into two sorghum backgrounds through an MAB that combines genotypic and phenotypic (C-MAB) selection during early backcross cycles. The background selection step is excluded. Since it is necessary to decrease further the cost associated with molecular marker assays, the costs of C-MAB were estimated. Lines with stay-green trait and good performance were identified at an early backcross generation, backcross two (BC2). Developed BC2F4 lines were evaluated under irrigated and drought as well as three rainfed environments varied in drought timing and severity. Under drought conditions, the mean grain yield of the most C-MAB-introgression lines was consistently higher than that of the recurrent parents. This study is one of the real applications of the successful use of C-MAB for the development of drought-tolerant sorghum lines for drought-prone areas.

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.

High Throughput SNP Genotyping with Two Mini-sequencing Assays

2004 ◽  
Vol 36 (6) ◽  
pp. 379-384 ◽  
Author(s):  
Chunqing Luo ◽  
Libin Deng ◽  
Changqing Zeng ◽  
You-Xin Jin
Keyword(s):  
High Throughput ◽  
Snp Genotyping ◽  
Success Rates ◽  
Ionization Time ◽  
Data Process ◽  
Pcr Product ◽  
Human Chromosome 3 ◽  
Pcr Technology ◽  
The Cost

Abstract Two mini-sequencing methods, FP-TDI (template-directed dye-terminator incorporation with fluorescence-polarization) and MassArray (matrix assisted laser desorption ionization time of flight detection mass spectrometry), were optimized. A numeric standard was introduced to evaluate the SNP scoring quality of FP-TDI assay, thus made the optimization work easier. At the same time, using multi-PCR technology, 8-plex genotyping of MassArray assay was successfully carried out, some softwares were developed and the data process of MassArray was highly automated. Then these two methods were applied to high throughput SNP genotyping, the accuracy, efficiency and robustness were compared. The result shows FP-TDI is more sensitive to the concentration of SNPprimer and PCR product, as well as extension cycles, the SNPprimer length of FP-TDI should be 24–30 bp long, whereas MassArray assay prefers to be as short as only 16 bp. Altogether 6440 SNP sites of human chromosome 3 were genotyped in a sample of 90 individuals, 4792 sites by FP-TDI assay and 1648 sites by MassArray assay, the success rates of FP-TDI and MassArray were 67.7% and 93.6% respectively. The throughput of MassArray was higher than FP-TDI, and the cost of MassArray was lower, MassArray was more suitable for high throughput SNP genotyping.

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