title: Strategies for implementing genomic selection in family-based aquaculture breeding schemes: double haploid sib test populations.
abstract: BACKGROUND
  Simulation studies have shown that accuracy and genetic gain are increased in genomic selection schemes compared to traditional aquaculture sib-based schemes. In genomic selection, accuracy of selection can be maximized by increasing the precision of the estimation of SNP effects and by maximizing the relationships between test sibs and candidate sibs. Another means of increasing the accuracy of the estimation of SNP effects is to create individuals in the test population with extreme genotypes. The latter approach was studied here with creation of double haploids and use of non-random mating designs.
  
  
  METHODS
  Six alternative breeding schemes were simulated in which the design of the test population was varied: test sibs inherited maternal (Mat), paternal (Pat) or a mixture of maternal and paternal (MatPat) double haploid genomes or test sibs were obtained by maximum coancestry mating (MaxC), minimum coancestry mating (MinC), or random (RAND) mating. Three thousand test sibs and 3000 candidate sibs were genotyped. The test sibs were recorded for a trait that could not be measured on the candidates and were used to estimate SNP effects. Selection was done by truncation on genome-wide estimated breeding values and 100 individuals were selected as parents each generation, equally divided between both sexes.
  
  
  RESULTS
  Results showed a 7 to 19% increase in selection accuracy and a 6 to 22% increase in genetic gain in the MatPat scheme compared to the RAND scheme. These increases were greater with lower heritabilities. Among all other scenarios, i.e. Mat, Pat, MaxC, and MinC, no substantial differences in selection accuracy and genetic gain were observed.
  
  
  CONCLUSIONS
  In conclusion, a test population designed with a mixture of paternal and maternal double haploids, i.e. the MatPat scheme, increases substantially the accuracy of selection and genetic gain. This will be particularly interesting for traits that cannot be recorded on the selection candidates and require the use of sib tests, such as disease resistance and meat quality.
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Science, PO Box 5003, Ås 1432, Norway. kahsay.nirea@umb.no
authors.forename: Kahsay G
authors.initials: KG
authors.lastname: Nirea
authors.forename: Anna K
authors.initials: AK
authors.lastname: Sonesson
authors.forename: John A
authors.initials: JA
authors.lastname: Woolliams
authors.forename: Theo H E
authors.initials: TH
authors.lastname: Meuwissen
mesh_terms: D000818:Animals; D017756:Aquaculture; D001947:Breeding; D005260:Female; D005399:Fishes / Q000235:genetics*; D016678:Genome; D006238:Haploidy; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D010375:Pedigree; D011153:Population / Q000235:genetics; D012641:Selection, Genetic / Q000235:genetics*; D035781:Siblings