title: Within- and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels.
abstract: BACKGROUND
  Currently, genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs). However with the decreasing costs of and current advances in next-generation sequencing technologies, whole-genome sequence (WGS) data on large numbers of individuals is within reach. Availability of such data provides new opportunities for genomic selection, which need to be explored.
  
  
  METHODS
  This simulation study investigated how much predictive ability is gained by using WGS data under scenarios with QTL (quantitative trait loci) densities ranging from 45 to 132 QTL/Morgan and heritabilities ranging from 0.07 to 0.30, compared to different SNP densities, with emphasis on divergent dairy cattle breeds with small populations. The relative performances of best linear unbiased prediction (SNP-BLUP) and of a variable selection method with a mixture of two normal distributions (MixP) were also evaluated. Genomic predictions were based on within-population, across-population, and multi-breed reference populations.
  
  
  RESULTS
  The use of WGS data for within-population predictions resulted in small to large increases in accuracy for low to moderately heritable traits. Depending on heritability of the trait, and on SNP and QTL densities, accuracy increased by up to 31 %. The advantage of WGS data was more pronounced (7 to 92 % increase in accuracy depending on trait heritability, SNP and QTL densities, and time of divergence between populations) with a combined reference population and when using MixP. While MixP outperformed SNP-BLUP at 45 QTL/Morgan, SNP-BLUP was as good as MixP when QTL density increased to 132 QTL/Morgan.
  
  
  CONCLUSIONS
  Our results show that, genomic predictions in numerically small cattle populations would benefit from a combination of WGS data, a multi-breed reference population, and a variable selection method.
authors.affiliation: Department of Animal and Aquaculture Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway. oscar.iheshiulor@nmbu.no.
authors.forename: Oscar O M
authors.initials: OO
authors.lastname: Iheshiulor
authors.affiliation: Department of Animal and Aquaculture Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway. john.woolliams@roslin.ed.ac.uk.|The Roslin Institute (Edinburgh), Royal (DICK) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Scotland, UK. john.woolliams@roslin.ed.ac.uk.
authors.forename: John A
authors.initials: JA
authors.lastname: Woolliams
authors.affiliation: Department of Animal and Aquaculture Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway. xijiang.yu@nmbu.no.
authors.forename: Xijiang
authors.initials: X
authors.lastname: Yu
authors.affiliation: Institute of Animal Husbandry and Animal Breeding, University of Hohenheim, 70593, Stuttgart, Germany. r.wellmann@uni-hohenheim.de.
authors.forename: Robin
authors.initials: R
authors.lastname: Wellmann
authors.affiliation: Department of Animal and Aquaculture Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway. theo.meuwissen@nmbu.no.
authors.forename: Theo H E
authors.initials: TH
authors.lastname: Meuwissen
mesh_terms: D000483:Alleles; D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D003198:Computer Simulation; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci