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Maximizing crossbred performance through purebred genomic selection.

Esfandyari H, Sørensen AC, Bijma P - Genet. Sel. Evol. (2015)

Bottom Line: Estimated breeding values for CP can be calculated from additive and dominance effects of alleles that are estimated using pure line data.However, for a high correlation of LD phase, marker effects that were estimated using a single combined reference population increased the gain in CP.Furthermore, if the correlation of LD phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. Hadi.esfandyari@mbg.au.dk.

ABSTRACT

Background: In livestock production, many animals are crossbred, with two distinct advantages: heterosis and breed complementarity. Genomic selection (GS) can be used to select purebred parental lines for crossbred performance (CP). Dominance being the likely genetic basis of heterosis, explicitly including dominance in the GS model may be an advantage to select purebreds for CP. Estimated breeding values for CP can be calculated from additive and dominance effects of alleles that are estimated using pure line data. The objective of this simulation study was to investigate the benefits of applying GS to select purebred animals for CP, based on purebred phenotypic and genotypic information. A second objective was to compare the use of two separate pure line reference populations to that of a single reference population that combines both pure lines. These objectives were investigated under two conditions, i.e. either a low or a high correlation of linkage disequilibrium (LD) phase between the pure lines.

Results: The results demonstrate that the gain in CP was higher when parental lines were selected for CP, rather than purebred performance, both with a low and a high correlation of LD phase. For a low correlation of LD phase between the pure lines, the use of two separate reference populations yielded a higher gain in CP than use of a single reference population that combines both pure lines. However, for a high correlation of LD phase, marker effects that were estimated using a single combined reference population increased the gain in CP.

Conclusions: Under the hypothesis that performance of crossbred animals differs from that of purebred animals due to dominance, a dominance model can be used for GS of purebred individuals for CP, without using crossbred data. Furthermore, if the correlation of LD phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects.

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Mean phenotype of crossbred individuals. (a) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart). (b) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted responses are means from 30 replicates. Sc. Ref: Selection criteria in both breed A and B was for purebred performance (P) and both breeds had Separate training sets. Sc.1: Selection criteria in breed A was for crossbred performance (C) and selection criteria in breed B was for purebred performance and both breeds had separate training sets. Sc.2: Selection criteria in both breed A and B was for crossbred performance and both breeds had separate training sets. Sc.3: Selection criteria in breed A was for crossbred performance and selection criteria in breed B was for purebred performance and both breeds had a Common training sets. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. Standard error of phenotypic means for simulated scenarios in generation 5 ranged from 0.03 to 0.04.
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Fig5: Mean phenotype of crossbred individuals. (a) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart). (b) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted responses are means from 30 replicates. Sc. Ref: Selection criteria in both breed A and B was for purebred performance (P) and both breeds had Separate training sets. Sc.1: Selection criteria in breed A was for crossbred performance (C) and selection criteria in breed B was for purebred performance and both breeds had separate training sets. Sc.2: Selection criteria in both breed A and B was for crossbred performance and both breeds had separate training sets. Sc.3: Selection criteria in breed A was for crossbred performance and selection criteria in breed B was for purebred performance and both breeds had a Common training sets. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. Standard error of phenotypic means for simulated scenarios in generation 5 ranged from 0.03 to 0.04.

Mentions: The purebred-crossbred genetic correlation, i.e. the correlation between TBVP and TBVC (rtbvp,tbvc), was equal to 0.66 and 0.70 on average for low and high correlations of LD phase, respectively. Figure 5 shows the mean values of phenotypes for crossbred animals in five generations under the five simulated scenarios with either a low (r = 0.2 in 1 cM) or a high correlation of LD phase (r = 0.7 in 1 cM) between the two breeds. When the correlation of LD phase was low between the two breeds, the ranking of scenarios in terms of mean phenotype of crossbred animals shows that breeding for CP led to higher gains in crossbred animals. By generation 5, scenario 2, in which both breeds were selected for CP, had a higher mean phenotype in the crossbred offspring than other scenarios. Scenario 1 also resulted in higher gain than the reference scenario since, in this scenario, one of the breeds was selected for CP. In the reference scenario, in which both breeds were selected for purebred performance, response to selection was lower than the other scenarios. Graph a in Figure 5 shows that, when each breed had a separate training set to estimate marker effects (scenarios 1 and 2), the performance of their crossbred offspring improved compared to that with the alternative scenarios for which a common reference was used to estimate marker effects (scenarios 3 and 4). For example, although in scenarios 1 and 3 one of the breeds (breed A) was selected for CP and because in scenario 1 each breed had its own training set, the response for scenario 1 was greater than for scenario 3.Figure 5


Maximizing crossbred performance through purebred genomic selection.

Esfandyari H, Sørensen AC, Bijma P - Genet. Sel. Evol. (2015)

Mean phenotype of crossbred individuals. (a) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart). (b) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted responses are means from 30 replicates. Sc. Ref: Selection criteria in both breed A and B was for purebred performance (P) and both breeds had Separate training sets. Sc.1: Selection criteria in breed A was for crossbred performance (C) and selection criteria in breed B was for purebred performance and both breeds had separate training sets. Sc.2: Selection criteria in both breed A and B was for crossbred performance and both breeds had separate training sets. Sc.3: Selection criteria in breed A was for crossbred performance and selection criteria in breed B was for purebred performance and both breeds had a Common training sets. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. Standard error of phenotypic means for simulated scenarios in generation 5 ranged from 0.03 to 0.04.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
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Fig5: Mean phenotype of crossbred individuals. (a) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart). (b) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted responses are means from 30 replicates. Sc. Ref: Selection criteria in both breed A and B was for purebred performance (P) and both breeds had Separate training sets. Sc.1: Selection criteria in breed A was for crossbred performance (C) and selection criteria in breed B was for purebred performance and both breeds had separate training sets. Sc.2: Selection criteria in both breed A and B was for crossbred performance and both breeds had separate training sets. Sc.3: Selection criteria in breed A was for crossbred performance and selection criteria in breed B was for purebred performance and both breeds had a Common training sets. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. Standard error of phenotypic means for simulated scenarios in generation 5 ranged from 0.03 to 0.04.
Mentions: The purebred-crossbred genetic correlation, i.e. the correlation between TBVP and TBVC (rtbvp,tbvc), was equal to 0.66 and 0.70 on average for low and high correlations of LD phase, respectively. Figure 5 shows the mean values of phenotypes for crossbred animals in five generations under the five simulated scenarios with either a low (r = 0.2 in 1 cM) or a high correlation of LD phase (r = 0.7 in 1 cM) between the two breeds. When the correlation of LD phase was low between the two breeds, the ranking of scenarios in terms of mean phenotype of crossbred animals shows that breeding for CP led to higher gains in crossbred animals. By generation 5, scenario 2, in which both breeds were selected for CP, had a higher mean phenotype in the crossbred offspring than other scenarios. Scenario 1 also resulted in higher gain than the reference scenario since, in this scenario, one of the breeds was selected for CP. In the reference scenario, in which both breeds were selected for purebred performance, response to selection was lower than the other scenarios. Graph a in Figure 5 shows that, when each breed had a separate training set to estimate marker effects (scenarios 1 and 2), the performance of their crossbred offspring improved compared to that with the alternative scenarios for which a common reference was used to estimate marker effects (scenarios 3 and 4). For example, although in scenarios 1 and 3 one of the breeds (breed A) was selected for CP and because in scenario 1 each breed had its own training set, the response for scenario 1 was greater than for scenario 3.Figure 5

Bottom Line: Estimated breeding values for CP can be calculated from additive and dominance effects of alleles that are estimated using pure line data.However, for a high correlation of LD phase, marker effects that were estimated using a single combined reference population increased the gain in CP.Furthermore, if the correlation of LD phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. Hadi.esfandyari@mbg.au.dk.

ABSTRACT

Background: In livestock production, many animals are crossbred, with two distinct advantages: heterosis and breed complementarity. Genomic selection (GS) can be used to select purebred parental lines for crossbred performance (CP). Dominance being the likely genetic basis of heterosis, explicitly including dominance in the GS model may be an advantage to select purebreds for CP. Estimated breeding values for CP can be calculated from additive and dominance effects of alleles that are estimated using pure line data. The objective of this simulation study was to investigate the benefits of applying GS to select purebred animals for CP, based on purebred phenotypic and genotypic information. A second objective was to compare the use of two separate pure line reference populations to that of a single reference population that combines both pure lines. These objectives were investigated under two conditions, i.e. either a low or a high correlation of linkage disequilibrium (LD) phase between the pure lines.

Results: The results demonstrate that the gain in CP was higher when parental lines were selected for CP, rather than purebred performance, both with a low and a high correlation of LD phase. For a low correlation of LD phase between the pure lines, the use of two separate reference populations yielded a higher gain in CP than use of a single reference population that combines both pure lines. However, for a high correlation of LD phase, marker effects that were estimated using a single combined reference population increased the gain in CP.

Conclusions: Under the hypothesis that performance of crossbred animals differs from that of purebred animals due to dominance, a dominance model can be used for GS of purebred individuals for CP, without using crossbred data. Furthermore, if the correlation of LD phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects.

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