scholarly journals Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yongjun Li ◽  
Jaroslav Klápště ◽  
Emily Telfer ◽  
Phillip Wilcox ◽  
Natalie Graham ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Predictive Ability ◽  
Radiata Pine ◽  
Exome Capture ◽  
Forward Selection ◽  
Generation Interval ◽  
Stem Straightness ◽  
Cluster Frequency

Abstract Background Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. Results Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. Conclusion It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

Genetic response to within-family selection using molecular markers in some radiata pine breeding schemes

2001 ◽  
Vol 31 (5) ◽  
pp. 779-785 ◽  
Author(s):  
Satish Kumar ◽  
D J Garrick
Keyword(s):  
Genetic Gain ◽  
Radiata Pine ◽  
Forest Tree ◽  
Clonal Forestry ◽  
Genetic Response ◽  
Forest Tree Breeding ◽  
Forward Selection ◽  
Family Selection ◽  
Marker Haplotype ◽  
Selection For

Marker-assisted selection (MAS) provides an opportunity to increase the efficiency of within-family selection in forest tree breeding. Within-family MAS involves selection decisions first made on conventional breeding values and quantitative trait loci (QTL) information used for within-family selection. In this study genetic response obtained by using MAS was compared with conventional methods for three options: "full-sib family forestry," "clonal forestry," and "forward selection for deployment." This comparison was undertaken using stochastic simulation for a locus that explained 10 or 20% of the genetic variance. In the full-sib family forestry scenario, markers were used to select genotypes (among juvenile individuals in a family) for vegetative propagation. Markers were used to preselect genotypes for clonal testing in clonal forestry option. In case of forward selection for deployment option, offspring that have favourable marker haplotype and a superior phenotype were selected from each family. The comparison between the MAS and the conventional strategy was evaluated in genetic terms based on comparison of the average genetic merit of the genotypes used for deployment in production plantations. The relative genetic gain (%) using MAS were found to be 4–8% and 2–3% higher compared with conventional strategy for full-sib family forestry and clonal forestry options, respectively. In case of forward selection for deployment option, MAS was generally found to be providing higher genetic gain only when the heritability is low.

Correlation between clonal means and open-pollinated seedling progeny means and its implications for radiata pine breeding strategy

2006 ◽  
Vol 36 (8) ◽  
pp. 1968-1975 ◽  
Author(s):  
Satish Kumar
Keyword(s):  
Recurrent Selection ◽  
Radiata Pine ◽  
Breeding Strategy ◽  
Empirical Correlations ◽  
Stem Straightness ◽  
The Mean ◽  
And Performance ◽  
Clonal Test ◽  
Cluster Frequency ◽  
Vegetative Propagules

Open-pollinated (OP) offspring of individual genotypes are commonly used for estimating their breeding values in recurrent selection programmes. Alternatively, vegetative propagules of individual genotypes could also be used to evaluate their relative performance. However, the mean for the vegetative propagules of a genotype represents an estimate of its total genotypic value, and it may or may not be closely related to the general combining ability. The main objective of this study was to correlate the performance of a number of clones, propagated by fascicle cuttings in a clonal test, with the performance of their OP offspring, planted in a progeny test. The empirical correlations between performance of a genotype, given as the mean of its ramets, and performance of the genotypes's offspring, given as the mean of its OP progeny, were 0.56, 0.63, 0.81, and 0.09 for stem diameter, stem straightness scores, branch cluster frequency scores, and malformation scores, respectively. The theoretical expectations of these correlations were 0.55, 0.69, 0.81, and 0.24, respectively. The size of the correlation largely depended on the genetic control of the trait concerned. Implications of these results on the radiata pine (Pinus radiata D. Don) breeding strategy were discussed.

scholarly journals Uses of genomics in livestock agriculture

2012 ◽  
Vol 52 (3) ◽  
pp. 73 ◽  
Author(s):  
M. E. Goddard
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Reference Population ◽  
Biological Information ◽  
Breeding Value ◽  
Biological Efficiency ◽  
Minimum Age ◽  
Livestock Products

World demand for livestock products is likely to increase in coming decades but the cost of production could escalate faster than the price due to competition for land, water, grain and fertiliser and the effects of climate change and its mitigation. To remain competitive for these resources, livestock agriculture has to dramatically increase in efficiency of production. Genetic gain is one mechanism to achieve increased efficiency and there is the opportunity to utilise the scientific advances in genomics. Three ways in which genomics can be used are in additive genetic improvement, exploitation of non-additive genetic variance and management which exploits genotype by environment interactions to optimise management. Genomic selection is already being widely implemented in dairy cattle and beef cattle and sheep will follow in the future once the accuracy of genomic selection is high enough. The accuracy of equations that predict breeding value from DNA genotypes can be increased by increasing the size of the reference population from which the equations are estimated, increasing the density of markers, using genome sequences instead of markers, using more appropriate statistical procedures and incorporating biological information into the prediction. In the long term, genomic selection combined with reproductive technology that reduces the minimum age at breeding will greatly increase the rate of genetic gain. This will allow long-term increases in biological efficiency and short-term tailoring of livestock to meet the demands of particular markets and opportunities.

scholarly journals Efficient use of genomic information for sustainable genetic improvement in small cattle populations

2019 ◽  
Author(s):  
J. Obšteter ◽  
J. Jenko ◽  
J. M. Hickey ◽  
G. Gorjanc
Keyword(s):  
Genetic Variation ◽  
Genomic Selection ◽  
Conversion Efficiency ◽  
Genetic Gain ◽  
Progeny Testing ◽  
Effective Population ◽  
Generation Interval ◽  
Truncation Selection ◽  
Selection Accuracy ◽  
Sire Selection

ABSTRACTThis paper compares genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies showed that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ~10,500 cows. We compared different truncation selection scenarios by varying i) the method of sire selection and their use on cows or bull-dams, and ii) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared the scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, the rate of coancestry, effective population size, and the conversion efficiency. The results show that early use of genomically tested sires increased genetic gain compared to progeny testing as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. While maximizing intensity gave the lowest conversion efficiency, a faster turn-over of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny tested sires that were used over several years. Compared to truncation selection optimizing sire selection and their usage increased the conversion efficiency by either achieving comparable genetic gain for a smaller loss of genetic variation or achieving higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations to implement sustainable genomic selection.

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 SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Plants ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 331 ◽  
Author(s):  
Paulina Ballesta ◽  
Carlos Maldonado ◽  
Paulino Pérez-Rodríguez ◽  
Freddy Mora
Keyword(s):  
Genomic Selection ◽  
Eucalyptus Globulus ◽  
Quantitative Traits ◽  
Prediction Models ◽  
Wood Quality ◽  
Predictive Ability ◽  
Breeding Population ◽  
Snp Markers ◽  
Nucleotide Polymorphisms ◽  
Stem Straightness

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

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 The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatemeh Amini ◽  
Felipe Restrepo Franco ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
In Planta ◽  
Data Set ◽  
Look Ahead ◽  
Trace Back ◽  
New Variant ◽  
The Look ◽  
Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

scholarly journals Genomic Approaches Reveal Pleiotropic Effects in Crossbred Beef Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Fernanda M. Rezende ◽  
Eduardo Rodriguez ◽  
Joel D. Leal-Gutiérrez ◽  
Mauricio A. Elzo ◽  
Dwain D. Johnson ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Meat Quality ◽  
Additive Genetic Variance ◽  
Cytoskeletal Proteins ◽  
Pleiotropic Effects ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Genomic Regions

Carcass and meat quality are two important attributes for the beef industry because they drive profitability and consumer demand. These traits are of even greater importance in crossbred cattle used in subtropical and tropical regions for their superior adaptability because they tend to underperform compared to their purebred counterparts. Many of these traits are challenging and expensive to measure and unavailable until late in life or after the animal is harvested, hence unrealistic to improve through traditional phenotypic selection, but perfect candidates for genomic selection. Before genomic selection can be implemented in crossbred populations, it is important to explore if pleiotropic effects exist between carcass and meat quality traits. Therefore, the objective of this study was to identify genomic regions with pleiotropic effects on carcass and meat quality traits in a multibreed Angus–Brahman population that included purebred and crossbred animals. Data included phenotypes for 10 carcass and meat quality traits from 2,384 steers, of which 1,038 were genotyped with the GGP Bovine F-250. Single-trait genome-wide association studies were first used to investigate the relevance of direct additive genetic effects on each carcass, sensory and visual meat quality traits. A second analysis for each trait included all other phenotypes as covariates to correct for direct causal effects from identified genomic regions with pure direct effects on the trait under analysis. Five genomic windows on chromosomes BTA5, BTA7, BTA18, and BTA29 explained more than 1% of additive genetic variance of two or more traits. Moreover, three suggestive pleiotropic regions were identified on BTA10 and BTA19. The 317 genes uncovered in pleiotropic regions included anchoring and cytoskeletal proteins, key players in cell growth, muscle development, lipid metabolism and fat deposition, and important factors in muscle proteolysis. A functional analysis of these genes revealed GO terms directly related to carcass quality, meat quality, and tenderness in beef cattle, including calcium-related processes, cell signaling, and modulation of cell–cell adhesion. These results contribute with novel information about the complex genetic architecture and pleiotropic effects of carcass and meat quality traits in crossbred beef cattle.

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