title: Maximizing crossbred performance through purebred genomic selection.
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.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. Hadi.esfandyari@mbg.au.dk.|Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands. Hadi.esfandyari@mbg.au.dk.
authors.forename: Hadi
authors.initials: H
authors.lastname: Esfandyari
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. achristian.sorensen@mbg.au.dk.
authors.forename: Anders C
authors.initials: AC
authors.lastname: Sørensen
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen University, Wageningen, the Netherlands. piter.bijma@wur.nl.
authors.forename: Piter
authors.initials: P
authors.lastname: Bijma
mesh_terms: D000465:Algorithms; D000818:Animals; D001947:Breeding; D003198:Computer Simulation; D003433:Crosses, Genetic; D016678:Genome; D023281:Genomics; D005838:Genotype; D006824:Hybridization, Genetic; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D012641:Selection, Genetic