scholarly journals Genomic Selection: Current Status, Opportunities and Challenges

2021 ◽  
Author(s):  
Neeraj Budhlakoti ◽  
Dwijesh Chandra Mishra ◽  
Anil Rai ◽  
K.K. Chaturvedi ◽  
Anu Sharma ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Early Stage ◽  
Breeding Value ◽  
Current Status ◽  
Breeding Research ◽  
Potential Benefits ◽  
Genetic Interval

Now a days, Genomic Selection (GS) became a preferable choice for selection of appropriate candidate for animal and plant breeding research. Various studies related to GS has been done recently where it has shown potential benefits and advantages over traditional and conventional plant breeding methods. GS has been successfully implemented in various animal and plant breeding programs. It reduces the total costs by selecting the animals at early stage hence shorten the generation interval. Genomic selection is the future of livestock and plant breeding as it improves the genetic gain by decreasing genetic interval and improving reliability. Although there is a need of further investigation to improve the accuracy of genomic estimated breeding value and manage long-term genetic gain. This article provides a brief review what we have achieved through GS till yet and what is future scope and perspective in the GS research.

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 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 New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

2021 ◽  
Author(s):  
Marlee R. Labroo ◽  
Jessica E. Rutkoski
Keyword(s):  
Genetic Gain ◽  
Overlapping Generations ◽  
Recurrent Selection ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Breeding Values ◽  
Truncation Selection ◽  
Breeding Cycles ◽  
Error Bias

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

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

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.

scholarly journals The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

2020 ◽  
Vol 33 (3) ◽  
pp. 382-389 ◽  
Author(s):  
Yun-Mi Lee ◽  
Chang-Gwon Dang ◽  
Mohammad Z. Alam ◽  
You-Sam Kim ◽  
Kwang-Hyeon Cho ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Milk Production ◽  
Genetic Gain ◽  
Milk Fat ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Data Set ◽  
Selection For ◽  
Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L<sub>SB</sub>) and that for the production of cows (L<sub>SC</sub>) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L<sub>DB</sub>) and cows (L<sub>DC</sub>) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

scholarly journals Simulation of pedigree vs. fully-informative marker based relationships matrices in a loblolly pine breeding population

2021 ◽  
Author(s):  
Adam R Festa ◽  
Ross Whetten
Keyword(s):  
Inbreeding Depression ◽  
Loblolly Pine ◽  
Genetic Gain ◽  
Field Experiments ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Deleterious Alleles

Computer simulations of breeding strategies are an essential resource for tree breeders because they allow exploratory analyses into potential long-term impacts on genetic gain and inbreeding consequences without bearing the cost, time, or resource requirements of field experiments. Previous work has modeled the potential long-term implications on inbreeding and genetic gain using random mating and phenotypic selection. Reduction in sequencing costs has enabled the use of DNA marker-based relationship matrices in addition to or in place of pedigree-based allele sharing estimates; this has been shown to provide a significant increase in the accuracy of progeny breeding value prediction. A potential pitfall of genomic selection using genetic relationship matrices is increased coancestry among selections, leading to the accumulation of deleterious alleles and inbreeding depression. We used simulation to compare the relative genetic gain and risk of inbreeding depression within a breeding program similar to loblolly pine, utilizing pedigree-based or marker-based relationships over ten generations. We saw a faster rate of purging deleterious alleles when using a genomic relationship matrix based on markers that track identity-by-descent of segments of the genome. Additionally, we observed an increase in the rate of genetic gain when using a genomic relationship matrix instead of a pedigree-based relationship matrix. While the genetic variance of populations decreased more rapidly when using genomic-based relationship matrices as opposed to pedigree-based, there appeared to be no long-term consequences on the accumulation of deleterious alleles within the simulated breeding strategy.

scholarly journals The effects of training population design on genomic prediction accuracy in wheat

2018 ◽  
Author(s):  
Stefan McKinnon Edwards ◽  
Jaap B. Buntjer ◽  
Robert Jackson ◽  
Alison R. Bentley ◽  
Jacob Lage ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
Genetic Material ◽  
Breeding Value ◽  
Training Set ◽  
Or Efficiency ◽  
Genomic Breeding Value ◽  
Close Relatives ◽  
Training Sets

AbstractGenomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 – 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

scholarly journals Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs

2015 ◽  
Vol 47 (1) ◽  
pp. 19 ◽  
Author(s):  
Huiming Liu ◽  
Theo Meuwissen ◽  
Anders C Sørensen ◽  
Peer Berg
Keyword(s):  
Genomic Selection ◽  
Genetic Gain
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