Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

AbstractThis study evaluates optimal cross selection for balancing selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganizes a conventional breeding program into population improvement component with recurrent genomic selection to increase the mean of germplasm and product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion, optimal cross-selection enables optimal management and exploitation of population improvement germplasm in two-part programs.Key messageOptimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling.

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Improving short and long term genetic gain by accounting for within family variance in optimal cross selection

10.1101/634303 ◽

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.

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

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.19.0546 ◽

2020 ◽

Vol 33 (3) ◽

pp. 382-389 ◽

Cited By ~ 1

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 (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) 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.

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Long-term genomic selection for heterosis without dominance in multiplicative traits: case study of bunch production in oil palm

BMC Genomics ◽

10.1186/s12864-015-1866-9 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 11

Author(s):

David Cros ◽

Marie Denis ◽

Jean-Marc Bouvet ◽

Leopoldo Sánchez

Keyword(s):

Genomic Selection ◽

Oil Palm ◽

Selection For

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

Genetics Selection Evolution ◽

10.1186/s12711-015-0101-0 ◽

2015 ◽

Vol 47 (1) ◽

pp. 19 ◽

Cited By ~ 17

Author(s):

Huiming Liu ◽

Theo Meuwissen ◽

Anders C Sørensen ◽

Peer Berg

Keyword(s):

Genomic Selection ◽

Genetic Gain

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Uses of genomics in livestock agriculture

Animal Production Science ◽

10.1071/an11180 ◽

2012 ◽

Vol 52 (3) ◽

pp. 73 ◽

Cited By ~ 22

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.

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Divergent Genomic Selection for Herbage Accumulation and Days-To-Heading in Perennial Ryegrass

Agronomy ◽

10.3390/agronomy10030340 ◽

2020 ◽

Vol 10 (3) ◽

pp. 340

Author(s):

Marty Faville ◽

Mingshu Cao ◽

Jana Schmidt ◽

Douglas Ryan ◽

Siva Ganesh ◽

...

Keyword(s):

Genomic Selection ◽

Perennial Ryegrass ◽

Genetic Gain ◽

Genomic Prediction ◽

Prediction Models ◽

Selection Response ◽

Training Set ◽

Days To Heading ◽

Selection For ◽

Target Environment

Increasing the rate of genetic gain for dry matter (DM) yield in perennial ryegrass (Lolium perenne L.), which is a key source of nutrition for ruminants in temperate environments, is an important goal for breeders. Genomic selection (GS) is a strategy used to improve genetic gain by using molecular marker information to predict breeding values in selection candidates. An empirical assessment of GS for herbage accumulation (HA; proxy for DM yield) and days-to-heading (DTH) was completed by using existing genomic prediction models to conduct one cycle of divergent GS in four selection populations (Pop I G1 and G3; Pop III G1 and G3), for each trait. G1 populations were the offspring of the training set and G3 populations were two generations further on from that. The HA of the High GEBV selection group (SG) progenies, averaged across all four populations, was 28% higher (p < 0.05) than Low GEBV SGs when assessed in the target environment, while it did not differ significantly in a second environment. Divergence was greater in Pop I (43%–65%) than Pop III (10%–16%) and the selection response was higher in G1 than in G3. Divergent GS for DTH also produced significant (p < 0.05) differences between High and Low GEBV SGs in G1 populations (+6.3 to 9.1 days; 31%–61%) and smaller, non-significant (p > 0.05) responses in G3. This study shows that genomic prediction models, trained from a small, composite reference set, can be used to improve traits with contrasting genetic architectures in perennial ryegrass. The results highlight the importance of target environment selection for training models, as well as the influence of relatedness between the training set and selection populations.

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Genotype-assisted optimum contribution selection to maximize selection response over a specified time period

Genetics Research ◽

10.1017/s0016672304007050 ◽

2004 ◽

Vol 84 (2) ◽

pp. 109-116 ◽

Cited By ~ 11

Author(s):

THEO H. E. MEUWISSEN ◽

ANNA K. SONESSON

Keyword(s):

Genetic Gain ◽

Selection Response ◽

Short Term ◽

Linear Restriction ◽

Genetic Gains ◽

Time Period ◽

Optimum Selection ◽

Selection For ◽

Trait Locus

Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35·2%, 2·3% and 1·1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2·8%, 3·1% and 3·2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0·4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.

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