title: Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.
abstract: BACKGROUND
  In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.
  
  
  METHODS
  Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.
  
  
  RESULTS
  Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.
  
  
  CONCLUSIONS
  Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 338, 6700 AH, Wageningen, The Netherlands. rianne.vanbinsbergen@wur.nl.|Biometris, Wageningen University and Research Centre, PO Box 16, 6700 AA, Wageningen, The Netherlands. rianne.vanbinsbergen@wur.nl.
authors.forename: Rianne
authors.initials: R
authors.lastname: van Binsbergen
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 338, 6700 AH, Wageningen, The Netherlands. mario.calus@wur.nl.
authors.forename: Mario P L
authors.initials: MP
authors.lastname: Calus
authors.affiliation: Biometris, Wageningen University and Research Centre, PO Box 16, 6700 AA, Wageningen, The Netherlands. marco.bink@wur.nl.
authors.forename: Marco C A M
authors.initials: MC
authors.lastname: Bink
authors.affiliation: Biometris, Wageningen University and Research Centre, PO Box 16, 6700 AA, Wageningen, The Netherlands. fred.vaneeuwijk@wur.nl.
authors.forename: Fred A
authors.initials: FA
authors.lastname: van Eeuwijk
authors.affiliation: CRV, Arnhem, The Netherlands. chris.schrooten@crv4all.com.
authors.forename: Chris
authors.initials: C
authors.lastname: Schrooten
authors.affiliation: Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 338, 6700 AH, Wageningen, The Netherlands. roel.veerkamp@wur.nl.
authors.forename: Roel F
authors.initials: RF
authors.lastname: Veerkamp
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle; D033481:Chromosomes, Mammalian; D005787:Gene Frequency; D016678:Genome; D023281:Genomics / Q000379:methods*; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA