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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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Schematic representation of the simulation steps. The crossbreeding program started in step 5 and consisted of five generations of purebred selection for crossbred performance; individuals from the last generation of step 3 (Generation 2108) constitutes the training population; AM and BM represent the males selected from breeds A and B, respectively; AF and BF represent the females selected breeds A and B, respectively; lines with arrows denote reproduction, while lines without arrows denote selection.
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Fig1: Schematic representation of the simulation steps. The crossbreeding program started in step 5 and consisted of five generations of purebred selection for crossbred performance; individuals from the last generation of step 3 (Generation 2108) constitutes the training population; AM and BM represent the males selected from breeds A and B, respectively; AF and BF represent the females selected breeds A and B, respectively; lines with arrows denote reproduction, while lines without arrows denote selection.

Mentions: Using the QMSim software [9], a historical population was simulated forward in time. Subsequent generations, GS, and evaluation were simulated using a script developed in R version 2.15.2 [10] (Table 1 and Figure 1). In the first simulation step, 1000 discrete generations with a constant population size of 2000 were simulated, followed by 1000 generations with a gradual decrease in population size from 2000 to 100 in order to create initial LD. The number of individuals of each sex remained the same in this step and the mating system was based on random union of gametes that were randomly sampled from the male and female gamete pools. Therefore, only two evolutionary forces were considered in this step: mutation and drift. To simulate the two recent purebred populations (referred to as breeds A and B, hereafter), two random samples of 50 animals were drawn from the last generation of the historical population and each animal was randomly mated for another 100 generations (step 2).Table 1


Maximizing crossbred performance through purebred genomic selection.

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

Schematic representation of the simulation steps. The crossbreeding program started in step 5 and consisted of five generations of purebred selection for crossbred performance; individuals from the last generation of step 3 (Generation 2108) constitutes the training population; AM and BM represent the males selected from breeds A and B, respectively; AF and BF represent the females selected breeds A and B, respectively; lines with arrows denote reproduction, while lines without arrows denote selection.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: Schematic representation of the simulation steps. The crossbreeding program started in step 5 and consisted of five generations of purebred selection for crossbred performance; individuals from the last generation of step 3 (Generation 2108) constitutes the training population; AM and BM represent the males selected from breeds A and B, respectively; AF and BF represent the females selected breeds A and B, respectively; lines with arrows denote reproduction, while lines without arrows denote selection.
Mentions: Using the QMSim software [9], a historical population was simulated forward in time. Subsequent generations, GS, and evaluation were simulated using a script developed in R version 2.15.2 [10] (Table 1 and Figure 1). In the first simulation step, 1000 discrete generations with a constant population size of 2000 were simulated, followed by 1000 generations with a gradual decrease in population size from 2000 to 100 in order to create initial LD. The number of individuals of each sex remained the same in this step and the mating system was based on random union of gametes that were randomly sampled from the male and female gamete pools. Therefore, only two evolutionary forces were considered in this step: mutation and drift. To simulate the two recent purebred populations (referred to as breeds A and B, hereafter), two random samples of 50 animals were drawn from the last generation of the historical population and each animal was randomly mated for another 100 generations (step 2).Table 1

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.

Show MeSH