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A crossbred reference population can improve the response to genomic selection for crossbred performance.

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

Bottom Line: A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance.Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population.If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.

View Article: PubMed Central - PubMed

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

ABSTRACT

Background: Breeding goals in a crossbreeding system should be defined at the commercial crossbred level. However, selection is often performed to improve purebred performance. A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance. Optimization of the GS model raises the question of whether marker effects should be estimated from data on the pure lines or crossbreds. Therefore, the first objective of this study was to compare response to selection of crossbreds by simulating a two-way crossbreeding program with either a purebred or a crossbred training population. We assumed a trait of interest that was controlled by loci with additive and dominance effects. Animals were selected on estimated breeding values for crossbred performance. There was no genotype by environment interaction. Linkage phase and strength of linkage disequilibrium between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs) can differ between breeds, which causes apparent effects of SNPs to be line-dependent. Thus, our second objective was to compare response to GS based on crossbred phenotypes when the line origin of alleles was taken into account or not in the estimation of breeding values.

Results: Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population. Response to selection in crossbreds was larger if both phenotypes and genotypes were collected on crossbreds than if phenotypes were only recorded on crossbreds and genotypes on their parents. If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.

Conclusions: Response to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improves genomic prediction.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the simulation steps. The crossbreeding program started in step 4 and consisted of five generations of purebred selection for crossbred performance; the random sample of individuals from the last generation of step 3 (Generation 2308) composed the purebred training set, and crossbred animals (AB*) in generation 2307 composed the crossbred training set; AM and BM represent the selected males of breeds A and B, AF and BF the selected females of breeds A and B; lines with arrows denote reproduction, while lines without arrows denote selection; the size of the reference population for scenarios with purebred training was 1000 within each pure breed, and 2000 for the scenarios with crossbred training; thus all scenarios had a total reference population size of 2000
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Fig1: Schematic representation of the simulation steps. The crossbreeding program started in step 4 and consisted of five generations of purebred selection for crossbred performance; the random sample of individuals from the last generation of step 3 (Generation 2308) composed the purebred training set, and crossbred animals (AB*) in generation 2307 composed the crossbred training set; AM and BM represent the selected males of breeds A and B, AF and BF the selected females of breeds A and B; lines with arrows denote reproduction, while lines without arrows denote selection; the size of the reference population for scenarios with purebred training was 1000 within each pure breed, and 2000 for the scenarios with crossbred training; thus all scenarios had a total reference population size of 2000

Mentions: The QMSim software [16] was used to simulate a historical population of 2000 generations with a constant size of 2000 individuals for 1000 generations, followed by a gradual decrease in population size from 2000 to 1000 to create initial LD (Fig. 1). The number of individuals of each sex was equal and mating was performed by randomly drawing the parents of an animal from the animals of the previous generation (step 1). To simulate the two purebred recent populations (referred to as breeds A and B, hereafter), two random samples of 100 animals were drawn from the last generation of the historical population and, within each sample, animals were randomly mated for another 300 generations (step 2); 300 generations of random mating for breed formation may seem unrealistic but this was done to simulate two distantly related breeds. In step 3, in order to expand breeds A and B, eight generations were simulated with five offspring per dam. Random mating within each breed was also assumed and no selection was considered in this step.Fig. 1


A crossbred reference population can improve the response to genomic selection for crossbred performance.

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

Schematic representation of the simulation steps. The crossbreeding program started in step 4 and consisted of five generations of purebred selection for crossbred performance; the random sample of individuals from the last generation of step 3 (Generation 2308) composed the purebred training set, and crossbred animals (AB*) in generation 2307 composed the crossbred training set; AM and BM represent the selected males of breeds A and B, AF and BF the selected females of breeds A and B; lines with arrows denote reproduction, while lines without arrows denote selection; the size of the reference population for scenarios with purebred training was 1000 within each pure breed, and 2000 for the scenarios with crossbred training; thus all scenarios had a total reference population size of 2000
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4587753&req=5

Fig1: Schematic representation of the simulation steps. The crossbreeding program started in step 4 and consisted of five generations of purebred selection for crossbred performance; the random sample of individuals from the last generation of step 3 (Generation 2308) composed the purebred training set, and crossbred animals (AB*) in generation 2307 composed the crossbred training set; AM and BM represent the selected males of breeds A and B, AF and BF the selected females of breeds A and B; lines with arrows denote reproduction, while lines without arrows denote selection; the size of the reference population for scenarios with purebred training was 1000 within each pure breed, and 2000 for the scenarios with crossbred training; thus all scenarios had a total reference population size of 2000
Mentions: The QMSim software [16] was used to simulate a historical population of 2000 generations with a constant size of 2000 individuals for 1000 generations, followed by a gradual decrease in population size from 2000 to 1000 to create initial LD (Fig. 1). The number of individuals of each sex was equal and mating was performed by randomly drawing the parents of an animal from the animals of the previous generation (step 1). To simulate the two purebred recent populations (referred to as breeds A and B, hereafter), two random samples of 100 animals were drawn from the last generation of the historical population and, within each sample, animals were randomly mated for another 300 generations (step 2); 300 generations of random mating for breed formation may seem unrealistic but this was done to simulate two distantly related breeds. In step 3, in order to expand breeds A and B, eight generations were simulated with five offspring per dam. Random mating within each breed was also assumed and no selection was considered in this step.Fig. 1

Bottom Line: A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance.Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population.If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.

View Article: PubMed Central - PubMed

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

ABSTRACT

Background: Breeding goals in a crossbreeding system should be defined at the commercial crossbred level. However, selection is often performed to improve purebred performance. A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance. Optimization of the GS model raises the question of whether marker effects should be estimated from data on the pure lines or crossbreds. Therefore, the first objective of this study was to compare response to selection of crossbreds by simulating a two-way crossbreeding program with either a purebred or a crossbred training population. We assumed a trait of interest that was controlled by loci with additive and dominance effects. Animals were selected on estimated breeding values for crossbred performance. There was no genotype by environment interaction. Linkage phase and strength of linkage disequilibrium between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs) can differ between breeds, which causes apparent effects of SNPs to be line-dependent. Thus, our second objective was to compare response to GS based on crossbred phenotypes when the line origin of alleles was taken into account or not in the estimation of breeding values.

Results: Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population. Response to selection in crossbreds was larger if both phenotypes and genotypes were collected on crossbreds than if phenotypes were only recorded on crossbreds and genotypes on their parents. If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.

Conclusions: Response to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improves genomic prediction.

No MeSH data available.


Related in: MedlinePlus