title: Identity-by-descent genomic selection using selective and sparse genotyping for binary traits.
abstract: BACKGROUND
  Genomic selection (GS) allows estimation of the breeding value of individuals, even for non-phenotyped animals. The aim of the study was to examine the potential of identity-by-descent genomic selection (IBD-GS) in genomic selection for a binary, sib-evaluated trait, using different strategies of selective genotyping. This low-cost GS approach is based on linkage analysis of sparse genome-wide marker loci.
  
  
  RESULTS
  Lowly to highly heritable (h(2) = 0.15, 0.30 or 0.60) binary traits with varying incidences (10 to 90%) were simulated for an aquaculture-like population. Genotyping was restricted to the 30% best families according to phenotype, using three genotyping strategies for training sibs. IBD-GS increased genetic gain compared to classical pedigree-based selection; the differences were largest at incidences of 10 to 50% of the desired category (i.e. a relative increase in genetic gain greater than 20%). Furthermore, the relative advantage of IBD-GS increased as the heritability of the trait increased. Differences were small between genotyping strategies, and most of the improvement was achieved by restricting genotyping to sibs with the least common binary phenotype. Genetic gains of IBD-GS relative to pedigree-based models were highest at low to moderate (10 to 50%) incidences of the category selected for, but decreased substantially at higher incidences (80 to 90%).
  
  
  CONCLUSIONS
  The IBD-GS approach, combined with sparse and selective genotyping, is well suited for genetic evaluation of binary traits. Genetic gain increased considerably compared with classical pedigree-based selection. Most of the improvement was achieved by selective genotyping of the sibs with the least common (minor) binary category phenotype. Furthermore, IBD-GS had greater advantage over classical pedigree-based models at low to moderate incidences of the category selected for.
authors.affiliation: AquaGen AS, P.O. Box 1240, Sluppen, NO-7462, Trondheim, Norway. jorgen.odegard@nmbu.no.
authors.forename: Jørgen
authors.initials: J
authors.lastname: Ødegård
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway. theo.meuwissen@nmbu.no.
authors.forename: Theo H E
authors.initials: TH
authors.lastname: Meuwissen
mesh_terms: D000465:Algorithms; D000818:Animals; D017756:Aquaculture / Q000379:methods; D001947:Breeding / Q000379:methods; D003198:Computer Simulation; D008040:Genetic Linkage; D016678:Genome; D023281:Genomics; D005838:Genotype; D060005:Genotyping Techniques / Q000379:methods*; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D040641:Quantitative Trait Loci; D012044:Regression Analysis; D012641:Selection, Genetic / Q000235:genetics*