title: Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection.
abstract: BACKGROUND
  The success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built. Here, we explore the benefit of combining two unrelated populations into a single reference population.
  
  
  METHODS
  The datasets consisted of 1829 Brahman and 1973 Tropical Composite cattle with measurements on five phenotypes relevant to tropical adaptation and genotypes for 71,726 genome-wide single nucleotide polymorphisms (SNPs). The underlying genomic correlation for the same phenotype across the two breeds was explored on the basis of consistent linkage disequilibrium (LD) phase and marker effects in both breeds.
  
  
  RESULTS
  The proportion of genetic variance explained by the entire set of SNPs ranged from 37.5 to 57.6 %. Estimated genomic correlations were drastically affected by the process used to select SNPs and went from near 0 to more than 0.80 for most traits when using the set of SNPs with significant effects and the same LD phase in the two breeds. We found that, by carefully selecting the subset of SNPs, the missing heritability can be largely recovered and accuracies in genomic predictions can be improved six-fold. However, the increases in accuracy might come at the expense of large biases.
  
  
  CONCLUSIONS
  Our results offer hope for the effective implementation of genomic selection schemes in situations where the number of breeds is large, the sample size within any single breed is small and the breeding objective includes many phenotypes.
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. Laercio.Portoneto@csiro.au.
authors.forename: Laercio R
authors.initials: LR
authors.lastname: Porto-Neto
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. Bill.Barendse@csiro.au.
authors.forename: William
authors.initials: W
authors.lastname: Barendse
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. John.Henshall@cobb-vantress.com.
authors.forename: John M
authors.initials: JM
authors.lastname: Henshall
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. Sean.McWilliam@csiro.au.
authors.forename: Sean M
authors.initials: SM
authors.lastname: McWilliam
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. Sigrid.Lehnert@csiro.au.
authors.forename: Sigrid A
authors.initials: SA
authors.lastname: Lehnert
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. Toni.Reverter-Gomez@csiro.au.
authors.forename: Antonio
authors.initials: A
authors.lastname: Reverter
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D003433:Crosses, Genetic; D066264:Datasets as Topic; D019143:Evolution, Molecular; D005828:Genetics, Population; D016678:Genome; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D005838:Genotype; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D012641:Selection, Genetic