title: Impact of QTL properties on the accuracy of multi-breed genomic prediction.
abstract: BACKGROUND
  Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows.
  
  
  METHODS
  Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect.
  
  
  RESULTS
  For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects.
  
  
  CONCLUSIONS
  Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH, Wageningen, The Netherlands. yvonne.wientjes@wur.nl.|Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands. yvonne.wientjes@wur.nl.
authors.forename: Yvonne C J
authors.initials: YC
authors.lastname: Wientjes
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH, Wageningen, The Netherlands. mario.calus@wur.nl.
authors.forename: Mario P L
authors.initials: MP
authors.lastname: Calus
authors.affiliation: Department of Environment and Primary Industries, Agribio, La Trobe University, Bundoora, Victoria, 3083, Australia. mike.goddard@depi.vic.gov.au.|Faculty of Land and Environment, University of Melbourne, Parkville, Victoria, 3010, Australia. mike.goddard@depi.vic.gov.au.|Dairy Futures Cooperative Research Centre, La Trobe University, Bundoora, Victoria, 3083, Australia. mike.goddard@depi.vic.gov.au.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Goddard
authors.affiliation: Department of Environment and Primary Industries, Agribio, La Trobe University, Bundoora, Victoria, 3083, Australia. ben.hayes@depi.vic.gov.au.|Dairy Futures Cooperative Research Centre, La Trobe University, Bundoora, Victoria, 3083, Australia. ben.hayes@depi.vic.gov.au.|La Trobe University, Bundoora, Victoria, 3083, Australia. ben.hayes@depi.vic.gov.au.
authors.forename: Ben J
authors.initials: BJ
authors.lastname: Hayes
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods*; D002417:Cattle / Q000235:genetics*; D005787:Gene Frequency; D023281:Genomics / Q000379:methods*; D005838:Genotype; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D040641:Quantitative Trait Loci