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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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Accuracy of selection in breeds A and B in five scenarios. (a) and (b) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart) (c) and (d) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted accuracies 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 set. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. It should be noted that accuracies in this Figure are correlations between the selection criterion and the EBV of interest. Thus, when selection is for purebred performance, accuracy is the correlation between GEBVP and TBVP, while when selection is for crossbred performance, accuracy is the correlation between GEBVC and TBVC.
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Fig7: Accuracy of selection in breeds A and B in five scenarios. (a) and (b) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart) (c) and (d) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted accuracies 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 set. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. It should be noted that accuracies in this Figure are correlations between the selection criterion and the EBV of interest. Thus, when selection is for purebred performance, accuracy is the correlation between GEBVP and TBVP, while when selection is for crossbred performance, accuracy is the correlation between GEBVC and TBVC.

Mentions: Prediction accuracy, i.e. correlation between the breeding values predicted by GS and the TBV obtained from simulation, ranged from 0.69 to 0.86 in the validation population (generation 1) across the different scenarios analysed (Figure 7). It should be noted that accuracies in Figure 7 always refer to the selection criterion. In other words, when selection is for purebred performance, accuracy is the correlation between TBVP and GEBVP, i.e. (rtbvp,gebvp). Conversely, when selection is for CP, accuracy is the correlation between TBVC and GEBVC, i.e. (rtbvc,gebvc). Hence, this comparison shows whether selection for CP is, or is not, more difficult than selection for purebred performance.Figure 7


Maximizing crossbred performance through purebred genomic selection.

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

Accuracy of selection in breeds A and B in five scenarios. (a) and (b) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart) (c) and (d) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted accuracies 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 set. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. It should be noted that accuracies in this Figure are correlations between the selection criterion and the EBV of interest. Thus, when selection is for purebred performance, accuracy is the correlation between GEBVP and TBVP, while when selection is for crossbred performance, accuracy is the correlation between GEBVC and TBVC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4358869&req=5

Fig7: Accuracy of selection in breeds A and B in five scenarios. (a) and (b) Results for a low correlation of LD phase between breeds A and B (r = 0.2 for markers 1 cM apart) (c) and (d) Results for a high correlation of LD phase between breeds A and B (r = 0.7 for markers 1 cM apart). The plotted accuracies 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 set. Sc.4: Selection criteria in both breed A and B was for crossbred performance and both breeds had a common training set. It should be noted that accuracies in this Figure are correlations between the selection criterion and the EBV of interest. Thus, when selection is for purebred performance, accuracy is the correlation between GEBVP and TBVP, while when selection is for crossbred performance, accuracy is the correlation between GEBVC and TBVC.
Mentions: Prediction accuracy, i.e. correlation between the breeding values predicted by GS and the TBV obtained from simulation, ranged from 0.69 to 0.86 in the validation population (generation 1) across the different scenarios analysed (Figure 7). It should be noted that accuracies in Figure 7 always refer to the selection criterion. In other words, when selection is for purebred performance, accuracy is the correlation between TBVP and GEBVP, i.e. (rtbvp,gebvp). Conversely, when selection is for CP, accuracy is the correlation between TBVC and GEBVC, i.e. (rtbvc,gebvc). Hence, this comparison shows whether selection for CP is, or is not, more difficult than selection for purebred performance.Figure 7

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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