title: Genomic selection using low density marker panels with application to a sire line in pigs.
abstract: BACKGROUND
  Genomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.
  
  
  RESULTS
  Genotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.
  
  
  CONCLUSIONS
  Genomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.
authors.affiliation: Institute of Animal Husbandry and Animal Breeding, University of Hohenheim, D-70599 Stuttgart, Germany. r.wellmann@uni-hohenheim.de
authors.forename: Robin
authors.initials: R
authors.lastname: Wellmann
authors.forename: Siegfried
authors.initials: S
authors.lastname: Preuß
authors.forename: Ernst
authors.initials: E
authors.lastname: Tholen
authors.forename: Jörg
authors.initials: J
authors.lastname: Heinkel
authors.forename: Klaus
authors.initials: K
authors.lastname: Wimmers
authors.forename: Jörn
authors.initials: J
authors.lastname: Bennewitz
mesh_terms: D000483:Alleles; D000818:Animals; D001947:Breeding; D002417:Cattle; D002874:Chromosome Mapping; D005260:Female; D005787:Gene Frequency; D005819:Genetic Markers; D016678:Genome; D023281:Genomics; D005838:Genotype; D015810:Linkage Disequilibrium; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results
chemical_list: D005819:Genetic Markers
references.citation: Genet Sel Evol. 2012 Jul 31;44:25
references.pmid: 22849718
references.citation: J Anim Sci. 2011 Dec;89(12):3908-16
references.pmid: 21841086
references.citation: Genetics. 2010 Aug;185(4):1441-9
references.pmid: 20479147
references.citation: Am J Hum Genet. 2006 Apr;78(4):629-44
references.pmid: 16532393
references.citation: Theor Appl Genet. 1968 Jun;38(6):226-31
references.pmid: 24442307
references.citation: Am J Hum Genet. 2009 Feb;84(2):210-23
references.pmid: 19200528
references.citation: Nat Rev Genet. 2010 Jul;11(7):499-511
references.pmid: 20517342
references.citation: Genetics. 2009 May;182(1):343-53
references.pmid: 19299339
references.citation: J Dairy Sci. 2010 Jul;93(7):3320-30
references.pmid: 20630248
references.citation: BMC Genomics. 2012 Nov 15;13:586
references.pmid: 23152986
references.citation: Genetica. 2009 Jun;136(2):245-57
references.pmid: 18704696
references.citation: J Dairy Sci. 2010 May;93(5):2229-38
references.pmid: 20412938
references.citation: PLoS One. 2009 Aug 05;4(8):e6524
references.pmid: 19654876
references.citation: PLoS One. 2008;3(10):e3395
references.pmid: 18852893
references.citation: Am J Hum Genet. 2007 Nov;81(5):1084-97
references.pmid: 17924348
references.citation: Poult Sci. 2013 Jul;92(7):1712-23
references.pmid: 23776257
references.citation: Am J Hum Genet. 2006 Jun;78(6):903-13
references.pmid: 16685642
references.citation: PLoS Genet. 2009 Jun;5(6):e1000529
references.pmid: 19543373
references.citation: Anim Genet. 2012 Feb;43(1):72-80
references.pmid: 22221027
references.citation: Genet Res (Camb). 2009 Feb;91(1):47-60
references.pmid: 19220931
references.citation: Genetics. 2011 Sep;189(1):317-27
references.pmid: 21705746
references.citation: Genetics. 2001 Apr;157(4):1819-29
references.pmid: 11290733
references.citation: Nat Rev Genet. 2009 Jun;10(6):381-91
references.pmid: 19448663
references.citation: Genet Sel Evol. 2009 Dec 31;41:55
references.pmid: 20043827
references.citation: Genet Sel Evol. 2011 Mar 10;43:12
references.pmid: 21388557
references.citation: J Anim Breed Genet. 2006 Aug;123(4):218-23
references.pmid: 16882088