title: Genomic selection using different marker types and densities.
abstract: With the availability of high-density marker maps and cost-effective genotyping, genomic selection methods may provide faster genetic gain than can be achieved by current selection methods based on phenotypes and the pedigree. Here we investigate some of the factors driving the accuracy of genomic selection, namely marker density and marker type (i.e., microsatellite and SNP markers), and the use of marker haplotypes versus marker genotypes alone. Different densities were tested with marker densities equivalent to 2, 1, 0.5, and 0.25N(e) markers/morgan using microsatellites and 8, 4, 2, and 1N(e) markers/morgan using SNP, where 1N(e) markers/morgan means 100 markers per morgan, if effective size (N(e)) is 100. Marker characteristics and linkage disequilibria were obtained by simulating a population over 1,000 generations to achieve a mutation drift balance. The marker designs were evaluated for their accuracy of predicting breeding values from either estimating marker effects or estimating effects of haplotypes based upon combining 2 markers. Using microsatellites as direct marker effects, the accuracy of selection increased from 0.63 to 0.83 as the density increased from 0.25N(e)/morgan to 2N(e)/morgan. Using SNP markers as direct marker effects, the accuracy of selection increased from 0.69 to 0.86 as the density increased from 1N(e)/morgan to 8N(e)/morgan. The SNP markers required a 2 to 3 times greater density compared with using microsatellites to achieve a similar accuracy. The biases that genomic selection EBV often show are due to the prediction of marker effects instead of QTL effects, and hence, genomic selection EBV may need rescaling for practical use. Using haplotypes resulted in similar or reduced accuracies compared with using direct marker effects. In practical situations, this means that it is advantageous to use direct marker effects, because this avoids the estimation of marker phases with the associated errors. In general, the results showed that the accuracy remained responsive with small bias to increasing marker density at least up to 8N(e) SNP/morgan, where the effective population size was 100 and with the genomic model assumed. For a 30-morgan genome and N(e) = 100, this implies that about approximately 24,000 SNP are needed.
authors.affiliation: Department of Animal and Aquacultural Sciences, University of Life Sciences, N-1432 As, Norway. trygve.roger.solberg@umb.no
authors.forename: T R
authors.initials: TR
authors.lastname: Solberg
authors.forename: A K
authors.initials: AK
authors.lastname: Sonesson
authors.forename: J A
authors.initials: JA
authors.lastname: Woolliams
authors.forename: T H E
authors.initials: TH
authors.lastname: Meuwissen
mesh_terms: D000818:Animals; D000829:Animals, Domestic; D003198:Computer Simulation; D005260:Female; D008040:Genetic Linkage; D005819:Genetic Markers; D014644:Genetic Variation; D023281:Genomics; D008297:Male; D008390:Markov Chains; D018895:Microsatellite Repeats / Q000235:genetics; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci; D012641:Selection, Genetic
chemical_list: D005819:Genetic Markers